ENERGY CONSUMPTION ANALYSIS IN DISCRETE MANUFACTURING …
Transcript of ENERGY CONSUMPTION ANALYSIS IN DISCRETE MANUFACTURING …
The Pennsylvania State University
The Graduate School
The Harold and Inge Marcus
Department of Industrial and Manufacturing Engineering
ENERGY CONSUMPTION ANALYSIS IN DISCRETE MANUFACTURING BASED ON
SIMULATION APPROACH
A Thesis in
Industrial Engineering
by
Hyun Woo Jeon
2013 Hyun Woo Jeon
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
May 2013
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The thesis of Hyun Woo Jeon was reviewed and approved* by the following:
Vittal Prabhu
Professor of Industrial and Manufacturing Engineering
Thesis Advisor
Chia-Jung Chang
Assistant Professor of Industrial and Manufacturing Engineering
Paul Griffin
Professor of Industrial and Manufacturing Engineering
Peter and Angela Dal Pezzo Department Head Chair
*Signatures are on file in the Graduate School
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ABSTRACT
The amount of consumed energy to manufacture products is important managerial
information for decision making as well as environmental considerations. It is, however, difficult
to predict the amount or filter out energy-critical factors from which the amount can be
approximated. The reason is twofold: many factors play their roles for machine cutting, and they
are interwoven; the current approximation method to calculate energy consumption in machining
considers only a brief span of time. Hence, to address two difficulties together, this thesis
proposes a new methodology. In detail, the simulation software HySPEED (Hybrid Simulator for
Production, Energy, and Emission Dynamics) has been developed and is used to measure a spent
energy amount with each set of various parameters as well as to take yearlong continuous
production into consideration. The collected data from designed simulation experiments is then
analyzed with ANOVA to find more energy-influential factors, and identified factors are
compared with other factors of a regression model built on industrial energy surveys. The
comparison suggests the result of simulation experiments agrees with that of general
investigations, and this conformity gives a clue about how this research can be expanded to
further mathematical models.
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TABLE OF CONTENTS
LIST OF FIGURES ................................................................................................................. vi
LIST OF TABLES ................................................................................................................... viii
ACKNOWLEDGEMENTS ..................................................................................................... ix
Chapter 1 Fundamental Foundation for Energy Consumption Analysis in Discrete
Manufacturing System ..................................................................................................... 1
1.1 Introduction ................................................................................................................ 1
1.2 Definition of Machine States and Time Threshold .................................................... 2
1.3 Energy Control Policy ................................................................................................ 4
1.4 Power Consumption Levels ....................................................................................... 4
Chapter 2 Simulation Model for Energy Consumption Analysis in Discrete
Manufacturing System ..................................................................................................... 7
2.1 HySPEED................................................................................................................... 7
2.2 HySPEED User Interface ........................................................................................... 8 2.3 HySPEED Worksheets ............................................................................................... 12
2.3.1 HySPEED Setup Worksheet ........................................................................... 12
2.3.2 Workstations Worksheet ................................................................................. 12 2.3.3 HySPEED KPI Result ..................................................................................... 12 2.3.4 Parts ................................................................................................................. 13 2.3.5 HySPEED PartPower Result ........................................................................... 13 2.3.6 WS Power ........................................................................................................ 13
2.3.7 Departure ......................................................................................................... 14 2.4 HySPEED Discrete Event Simulation Algorithm ...................................................... 14
2.4.1 Outer Most Loop ............................................................................................. 14
2.4.2 Middle Loop .................................................................................................... 14 2.4.3 Inner Most Loop .............................................................................................. 14
2.5 Simulation Result and Analysis ................................................................................. 15 2.6 Validation and Verification of HySPEED ................................................................. 17
Chapter 3 Energy Consumption Analysis in Discrete Manufacturing System Based on
Design of Experiment ...................................................................................................... 24
3.1 Introduction ................................................................................................................ 24
3.2 Methodology .............................................................................................................. 24 3.3 Work Piece ................................................................................................................. 26
3.3.1 Turn Decorative Groove .................................................................................. 27
3.3.2 RH Rough Turn OD ........................................................................................ 27 3.3.3 Mill Sloped Sides of Obelisk .......................................................................... 27 3.3.4 Mill Sloped Sides of Obelisk .......................................................................... 27
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3.4 Modified Process Plan for a Rook Piece .................................................................... 29
3.5 Factors of Interest for Experimental Design .............................................................. 29
3.5.1 Machine Type .................................................................................................. 29
3.5.2 Demand ........................................................................................................... 29 3.5.3 Material Type .................................................................................................. 30
3.5.4 Production Level ............................................................................................. 30
3.5.5 Volume to Be Removed .................................................................................. 30 3.6 Experimental Design .................................................................................................. 31
3.7 Model Assumptions ................................................................................................... 33 3.8 HySPEED................................................................................................................... 33 3.9 Analysis ...................................................................................................................... 34
3.9.1 Response = Energy Saving (EC OFF – EC ON) ............................................. 34
3.9.2 Response = (Energy Spent with EC ON / Number of Products) ..................... 36
Chapter 4 Regression Analysis on Data of Industrial Assessments Centers Data ................. 40
4.1 Introduction ................................................................................................................ 40
4.2 IAC Dataset ................................................................................................................ 40 4.3 Response Variable ...................................................................................................... 41
4.4 Regression Analysis ................................................................................................... 42
4.4.1 Basic Analysis ................................................................................................. 42
4.4.2 Multiple Regression Model ............................................................................. 42
4.5 Discussion .................................................................................................................. 43
Chapter 5 Conclusions and Future Work ............................................................................... 45
5.1 Conclusions ................................................................................................................ 45
5.2 Future Research .......................................................................................................... 46
References ................................................................................................................................ 47
Appendix A: Minitab Result for 3.9.1 ..................................................................................... 49
Appendix B: Minitab Result for 3.9.2 ..................................................................................... 50
Appendix C: Minitab Result for 4.4.2 ..................................................................................... 51
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LIST OF FIGURES
Figure 1-1: Mapping of Discrete Production States to Energy States. .................................... 2
Figure 1-2: Power Signature of a Machine on in Ramp-Up State. .......................................... 5
Figure 1-3: Power Signature of Milling in a Processing State. ................................................ 6
Figure 2-1: Architecture of HySPEED. ................................................................................... 7
Figure 2-2: HySPEED Setup Worksheet. ................................................................................ 8
Figure 2-3: Structure of HySPEED Discrete Event Algorithm. .............................................. 15
Figure 2-4: Random IAT (Inter-arrival Time) and Processing Time. ...................................... 18
Figure 2-5: Exponential Random IAT Histogram (Mean = 15). ............................................. 19
Figure 2-6: Exponential Random Processing Time Histogram (Mean = 15). ......................... 19
Figure 2-7: Exponential Random IAT (Mean = 15). ............................................................... 20
Figure 2-8: Exponential Random IAT Histogram (Mean = 40). ............................................. 20
Figure 2-9: Squared Frequency Deviation ............................................................................... 21
Figure 2-10: HySPEED Discrete Event Algorithm Engine. .................................................... 22
Figure 3-1: Pennsylvania State University Chess Set Pieces ................................................... 25
Figure 3-2: Penn State Chess Set CAD Data (Rook) ............................................................... 26
Figure 3-3: Process Plan for a Rook Piece (1 of 2) .................................................................. 28
Figure 3-4: Process Plan for a Rook Piece (2 of 2) .................................................................. 28
Figure 3-5: Modified Process Plan for a Rook Piece ............................................................... 29
Figure 3-6: Parameters for Factorial Design ....................................................................... 30
Figure 3-7(a): Residuals VS. Fitted Values before Transformation. ....................................... 34
Figure 3-7(b): Residuals VS. Fitted Values after Square Root Transformation ...................... 35
Figure 3-7(c): Normal Probability Plot for Residuals after Square Root Transformation ....... 35
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Figure 3-7(d): Normal Probability Plot of Effects ................................................................... 36
Figure 3-8(a): Residuals VS. Fitted Values after Log Transformation ................................... 37
Figure 3-8(b): Normal Probability Plot for Residuals after Log Transformation .................... 38
Figure 3-8(c): Normal Probability Plot of Effects ................................................................... 38
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LIST OF TABLES
Table 2-1: HySPEED Simulation Parameters. ......................................................................... 15
Table 2-2: HySPEED Simulation Result. ................................................................................ 17
Table 2-3: K-S Test Result. ..................................................................................................... 23
Table 4-1: Simple Linear Regression Result ........................................................................... 42
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ACKNOWLEDGEMENTS
First and foremost, I like to mention my gratitude to my thesis advisor, Dr. Vittal Prabhu.
This thesis would not be finalized in time without his academic expertise and infinite patience.
Advices of Dr. Chia-Jung Chang have been also very helpful in addressing problems of this thesis,
and her thoughtful comments have to be appreciated. Finally I thank my family and friends for
their love and support. I wish all of them would know they are always more important than any
academic achievement of me.
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Chapter 1
Fundamental Foundation for Energy Consumption Analysis in Discrete
Manufacturing System
1.1 Introduction
The U.S. industrial sector deserves much of attention from energy consumption analysis
as the sector accounted for 30.8% of the total U.S. energy demand in 2010, a figure suggesting
that the sector is most energy demanding [1]. Among sub-sectors of the industrial sector,
manufacturing took the largest proportion, and the fraction is greater than total sum of what other
sub-sectors were taking up in 2010 [2]. Thus it seems to be a logical next step to look into energy
consumption of manufacturing to identify driving force of energy spending in the industrial sector.
In the light of the energy consumption of manufacturing, it is noteworthy that only 20 -
70% of the total energy consumption by machining processes is spent on actually cutting
materials from raw stocks in various manufacturing machines [3]. In other words 30 - 80% of the
total energy spending of machining processes is wasted for being idle or other non-cutting related
processes. Since this 30 - 80% fraction of the total energy consumption could have been saved,
some opportunities to save energy wasted are likely to be in manufacturing, and appropriate
methodologies will be able to actualize the idea into a well-defined theory.
Among methodologies in energy consumption analysis, one of the most well-known is
LCA (Life Cycle Assessment). While LCA is able to provide detailed energy requirements for
each manufacturing process given a product, some disadvantages have been pointed out that the
method requires the large amount of data and time to be implemented. Another drawback of LCA
is that results from different LCA analyses sometimes show significantly different energy
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estimates for the same product [4]. Thus there is a need to introduce a better tool to scrutinize the
energy consumption of a manufacturing sector, and this research considers how to meet the need
in the view of the energy consumption analysis of manufacturing sectors.
An approach of this thesis is as follows; after the introduction of machine states, energy
control policies, and energy consumption levels in Chapter 1, a simulation model for energy
analysis is built and its validity is checked in comparison with the existing simulation software
throughout Chapter 2. In the next chapter, experiments are designed and performed to see which
factors have more influence on energy consumption in an exemplary manufacturing process.
Results of experiments are analyzed with ANOVA (analysis of variance), and in the light of DOE
(design of experiment), those factors are discussed in terms of their statistical significance and
effect. Finally the comparison is made between experiments and regression results to see whether
two analyses show the consistent result in the last chapter.
1.2 Definition of Machine States and Time Threshold
Figure 1-1: Mapping of Discrete Production States to Energy States. Source: [8].
Idle Busy
Off Standby
Ramp Up
Ramp Down
Wait Process
EnergyStates
ProductionStates
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In general a manufacturing machine is composed of a large number of parts, and
therefore it is not simple to define the state of machines. However, in the perspective of power
consumption, machine states can be roughly divided into two states: busy and idle. The busy state
is defined to be a generic state for any working or cutting state of machines, and an idle state is
for any non-working or non-cutting related state of machines. In fact, this definition could be
unclear as we can see from Figure 1-1. While the figure illustrates how real states of machines
could be mapped into power consumption states, it also shows vagueness in defining the mapping.
For example, a decision could be different on whether the standby state is supposed to be mapped
into an idle or busy state. To keep the consistency of definitions in energy consumption states
throughout this thesis, machine states are assumed to be mapped to energy states as follows:
Busy state: This state represents all working or cutting state of machines. It may be
regarded as a generic working state of a machine.
Nominal power idling state: In this thesis under the energy control policy , an idle
state is divided into two states, and the nominal power idling state is one of them.
More specifically it is assumed that an idle machine enters a nominal power idling
state if the current idle duration is less than a time threshold . When the machine
enters a busy state again, it immediately quits the nominal power idling state.
Low power idling state: This state is the other state of two idle states under the
energy control policy . Contrary to the nominal power idling state, an idle
machine enters a low power idling state if the idle duration is greater than a time
threshold . When any machine enters a busy state again, it immediately quits the
nominal power idling state.
A time threshold is defined as time duration which is used for determining whether a machine
enters a nominal power idling or a low power idling state. More specifically if the length of an
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idle time window is less than , a machine is assumed to enter a nominal power idling state, and
if it is greater than or equal to , a machine is assumed to enter a low power idling state. However
this definition of machine states and other related parameters is different for each energy control
policy to be adopted. Thus the discussion about energy control policies is given in the next
section.
1.3 Energy Control Policy
It is assumed that a manufacturing system for which any energy control policy is not
considered is under the energy control policy . With this energy control policy, a
manufacturing system operates without any energy saving scheme. On the other hand implies
the energy control policy which governs time threshold in each machine of the manufacturing
system. Thus can be defined as a vector ( ) where n is the total number of
machines in the system as different values of time threshold can be applied to machines. Another
distinctive characteristic of compared with is that machines can enter a low power idling
state when under . Since does not consider any power saving plans, machines under
enter only a nominal power idling state when idle regardless of the length of each idleness. Thus
machines under can save energy in a low power idling state, spending less energy than in a
nominal power idling state.
1.4 Power Consumption Levels
As machines are supposed to use less or more energy in different states, it is necessary to
discuss how to define power consumption levels of machines in each state. One way of doing so
is to take average of power consumption over time of each state. Thus this power consumption
level or signature can be used to represent the average power consumption of a given state. In
detail, power consumption of a working machine varies over time, and it is assumed that power
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consumption of a machine can be captured as in Figure 1-2. This figure illustrates power
signature of a machine in a ramp-up state, and its power changes over time. As is
averaged power consumption over time, the total sum of consumed energy is supposed to be
calculated before the sum is divided by time duration. More specifically, the total sum of energy
consumed is the sum of duration of peak time multiplied by peak power consumption level ( )
and duration of non-peak time multiplied by non-peak time power consumption level
( ) . Dividing the sum by the total time in the state (40 units) in order to have time
average provides the following equation:
* ( )+
Figure 1-2: Power Signature of a Machine on in Ramp-Up State. Source: [8].
Whereas the above approach can be a good alternative in computing average power
consumption of each state, other methods can be also used. Thus this thesis assumes that average
power consumption of a machine in each state can be computed somehow and that the power
consumption levels for three states are assumed as follows:
: This power signature is used to represent the average power consumption of a
busy state. Thus is the amount of power a machine spends in a generic working
state for a unit time. As discussed previously, there could be multiple ways of
0
0.2
0.4
0.6
0.8
1
1.2
Tim
e 1 3 5 7 9
20 40Pmax
Plow
Ts
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calculating , and each method will be described in detail whenever it is used
throughout this thesis. For example, Figure 1-3 shows how a busy machine typically
consumes power over time. In this example, can be defined as a function of the
following parameters: , , , and . Since a typical milling work is the
combination of material cutting and air cutting, the total spent energy for this process
is the sum of power consumption of material cutting multiplied by the time
duration and power consumption of air cutting multiplied by the time
duration .
: This power signature is used to represent the average power consumption of a
nominal power idling state. Thus is the amount of power a machine spends in a
nominal power idling state for a unit time.
: This power signature is used to represent the average power consumption of a
low power idling state. Thus is the amount of power a machine spends in a low
power idling state for a unit time. The state is defined only under , and
therefore when under .
Again values of and depend on an adopted energy control policy between and
. Under there does not exist as does not a low power idling state. On the other hand
is defined as the above description since a machine in the state is well defined under as
well as .
Figure 1-3: Power Signature of Milling in a Processing State. Source: [8]
Pcut
Pair
Time
Tair
Tcut
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Chapter 2
Simulation Model for Energy Consumption Analysis in Discrete
Manufacturing System1
2.1 HySPEED
In order to deal with energy consumption problems in manufacturing systems, the
simulation software HySPEED (Hybrid Simulator for Production, Energy, and Emission
Dynamics) was developed so that the software can simulate the total energy consumption of a
single or multiple machines with various parameters on a given specific time horizon. In the
consideration of familiar user interface and easy accessibility for students and researchers, Excel
2007/2010 VBA was used to develop HySPEED. This choice of a development tool is for
providing broad opportunities to use this tool throughout academia and research institutions.
Since one of objectives of HySPEED development is to provide a tool for discrete event
simulation for energy consumption analysis of manufacturing systems, HySPEED considers
discrete events, and its main architecture is shown in Figure 2-1.
Figure 2-1: Architecture of HySPEED
1 Much of the material in this chapter is based on:
Prabhu, V. V., Jeon, H. W., and Taisch, M.: Simulation Modeling of Energy Dynamics in Discrete
Manufacturing Systems, In Service Orientation in Holonic and Multi Agent Manufacturing and Robotics,
Eds. T. Borangiu, A. Thomas, and D. Trentesaux, Springer-Verlag Berlin Heidelberg, pp. 293-311,
(2013)
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As seen in Figure 2-2 before the beginning of HySPEED simulation running, users can
setup simulation parameters, and based on these initial parameters important workstation
(machine) data are generated. Simulation results are calculated from generated parameters, and
they are shown after each run. In the following subsections, detailed description about how
HySPEED runs is suggested.
2.2 HySPEED User Interface
Figure 2-2: HySPEED Setup Worksheet. Source: [8].
After opening HySPEED, users are supposed to see ‗HySPEED Setup‘ worksheet as
shown in Figure 2-2. The worksheet is the main worksheet among seven worksheets, and there
users can setup most of simulation parameters. In relationships among parameters while the most
of parameters are independent, some of them depend on others. Thus the dependent parameters
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are painted with grey color and inactivated so that users cannot change the value of dependent
parameters. Values of these dependent parameters are supposed to be refreshed after each run of
simulation is finished. Major parameters in the worksheet are introduced as follows:
Number of Simulations: The total number of replications of simulation experiments with
the same set of parameters. This parameter is an integer type variable.
Total Simulation Time horizon (sec): This parameter defines the maximum time for
which the simulation runs, and its unit is of seconds in default. A variable type of this
parameter is double for storing a decimal number (time).
Inter-Arrival Time (mean): Average inter-arrival time for arrival distributions. This
parameter is of a double type for storing a decimal number (time).
[Dependent Variable] Inter-Arrival Time (stdev): This parameter defines standard
deviation for arrival distribution and is only active for the normal distribution. In default
this is automatically calculated as the mean multiplied by CV (coefficient of variation),
and therefore this is a dependent variable. This is also set as 1 for exponential
distributions even when different values are shown. This parameter is a double type
variable.
Inter-Arrival time CV (Coefficient of variation): CV is defined as standard deviation
divided by mean. This parameter is a double type variable.
Processing Time (mean): This parameter defines average processing time for each
workstation. This parameter is a double type variable.
[Dependent Variable] Processing Time (stdev): This parameter defines standard deviation
for processing time distribution and is only active for normal distributions. In default this
is automatically calculated as the mean multiplied by CV. This is set as 1 for exponential
distributions even when different values are shown. This parameter is a double type
variable.
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Processing time CV: This defines a coefficient of the variation of processing time
distribution. It is calculated as standard deviation divided by the mean. This parameter is
a double type variable.
Number of workstations: This defines the total number of machines in a serial line.
Minimum is 1. This parameter is an integer type variable.
[Inactive/Dependent Variable] Number of jobs: This shows the total number of parts
which have been processed within the total simulation run time. After each simulation
run is made, a new value will be displayed. This number will be approximately (total
simulation time) divided by (inter-arrival time). This parameter is an integer type variable.
Queueing model: This parameter defines the probability distribution for inter-arrival and
processing time between normal and exponential distributions. If 1 is entered, G/G/1
model will be selected, and inter-arrival and processing times will be normally distributed
with given average and standard deviation values. If 2 is entered, M/M/1 model will be
selected, and inter-arrival/processing times will be exponentially distributed with given
average values above. This parameter is an integer type and between 1 and 2.
Simulation random number seed: This defines the random number seed for newly
generated random numbers following the uniform distribution (0, 1). The objective of this
parameter is to guarantee having the same random number stream in each simulation
replication. This parameter is an integer type variable.
EC setting: ‗ON‘ selects energy control on ( ) and ‗OFF‘ selects energy control off
( ). This parameter is of a string type between ‗ON‘ and ‗OFF‘.
Workstation Power Trace: ‗ON‘ will collect power trace data in ‗WS Power‘ worksheet.
This parameter is a string type variable, and its value is between ‗ON‘ and ‗OFF‘.
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Part Power Trace: ‗ON‘ will collect power trace data in ‗HySPEED PartPower Result‘
worksheet. This parameter is a string type variable, and its value is between ‗ON‘ and
‗OFF‘.
[Inactive] Generate Workstation Data: ‗ON‘ will newly create workstation data as the
simulation run starts. This is a fixed parameter and can‘t be changed in order to guarantee
that workstation data always be generated for each simulation run.
Departure: ‗ON‘ will collect departure time of each part from each workstation in
‗Departure‘ worksheet. This parameter is a string type variable, and its value is between
‗ON‘ and ‗OFF‘.
: This defines the power consumption level of a nominal idling state of each machine.
Generally it is greater than and less than . This parameter is defined to be a double
type variable.
: This defines the power consumption level of a low power idling state of each
machine. Generally it is less than . This parameter is defined to be a double type
variable.
[Dependent Variable] (mean cutting power watts): This defines the power
consumption level of a busy state of each machine. Since this value is dependent on other
variables such as MRR (material removal rate), it is a dependent variable. This parameter
is defined to be a double type variable and calculated as follows:
( ) ( )
: This parameter is about a time threshold for workstations. If a machine idle time is
longer than , the workstation enters a low power idling state, and its power
consumption will be dropped into . Otherwise the consumed power would be or
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. Typically the half of the mean processing time is used for simulation runs. This
parameter is a double type variable.
2.3 HySPEED Worksheets
HySPEED consists of seven worksheets, and each of them is introduced below.
2.3.1 HySPEED Setup Worksheet
Users can set all variables and parameters of HySPEED application in this worksheet.
This is also the default screen users see in opening the HySPEED file. After setting all parameters
and variables, users can run HySPEED by clicking the button on the right-upper side of the
worksheet as seen in Figure 2-2. After each simulation running, a screen uses are seeing is
automatically re-directed to the KPI worksheet for showing users the simulation result.
2.3.2 Workstations Worksheet
In this worksheet, data for workstations is stored for HySPEED running. Generally users
do not need to consider this worksheet except for reference purposes about how each workstation
data is generated.
2.3.3 HySPEED KPI Result
After each simulation run is finished, the result is shown in this worksheet. Important
results are shown as follows:
Sim Num: Replication number for this running.
Throughput
Average Flow Time
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Energy productive: the total amount of energy spent during a busy state.
Energy waste: the total amount of energy spent during nominal/low power idling states.
Mean IAT Used: Average of all inter-arrival times randomly generated.
Mean p Used: Average of all processing time randomly generated.
Experimental Factors: This is the automatically assigned character string about the
simulation run. As each simulation result accumulates in the KPI worksheet, this
information allows users to have easy identification of the simulation parameters used for a
specific run. Typical example could be: ―EC=ON; DD Type=DIS; IAT=40; Num Sim=10;
Num Iter=1; Queueing model =Model 2: M/M/1‖ suggesting ; mean inter-arrival time =
40; total number of simulation = 10; exponential random number used. Other information is
about DATC [9] and irrelevant to this thesis.
2.3.4 Parts
This worksheet shows time-series data of each part. In default, this data is not collected
unless it is ON in the Setup worksheet.
2.3.5 HySPEED PartPower Result
This worksheet shows time-series data of power consumed for each part. In default, this
data is not collected unless it is ON in the Setup worksheet.
2.3.6 WS Power
This worksheet shows time-series data of power consumed for each workstation. In
default, this data is not collected unless it is ON in the Setup worksheet.
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2.3.7 Departure
This worksheet shows time-series data of departure process of each part leaving
workstations. In default, this data is not collected unless it is ON in the Setup worksheet.
2.4 HySPEED Discrete Event Simulation Algorithm
The discrete event simulation algorithm of HySPEED consists of three different loops to
repeat what‘s given for each step. How these loops work is shown in Figure 2-3 and below.
2.4.1 Outer Most Loop
This loop repeats simulation as many as the number of replications given in the Setup
worksheet. In each repetition a stream of random numbers is generated for inter-arrival times, and
simulation is repeated with the same set of parameters.
2.4.2 Middle Loop
This loop repeats simulation for the number of DATC (Distributed Arrival Time Control)
iterations [9]. Since this analysis does not consider DATC, this loop is repeated once.
2.4.3 Inner Most Loop
This loop is repeated based on discrete events. The algorithm makes events sorted in an
ascending order of occurring time on simulation time horizon and executes the event on the top of
the list one by one. After executed, each event is removed from the list, and simulation is
terminated when there are no events to be executed in the list. In executing events, the algorithm
generates various time series data. These time series data include power consumption, parts, inter-
arrival time, processing time, departure, etc.
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Figure 2-3: Structure of HySPEED Discrete Event Algorithm
2.5 Simulation Result and Analysis
Two different scenarios are tested to validate and verify HySPEED. As a control group,
Simio is used to make comparison between results of HySPEED and Simio. Thus the parameters
used for simulation remain the same for both Simio and HySPEED. Simulation parameters can be
found in Table 2-1, and description for each parameter is as follows:
Scenario 1 2
Distribution Exponential Normal Exponential Normal
0.025 0.025 0.025 0.025
0.033 0.033 0.067 0.067
0.750 0.750 0.375 0.375
15.000 15.000 7.500 7.500
2140 2140
1000 1000
100 100
Replication 30 30
Table 2-1: HySPEED Simulation Parameters
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Distribution: probability distributions for inter-arrival process and average processing time in
workstations. Normal distributions and exponential distributions are considered respectively.
: arrival rate to workstations. The arrival rate is defined as the number of part arrivals
divided by a unit time.
: processing rate of each workstation. The processing rate is defined as the number of parts
processed divided by a unit time.
: workstation utilization. System utilization is defined as the arrival rate divided by the
processing rate .
: time threshold. This defines when an idle workstation can enter a low power idling state. If
the idle duration of a workstation is greater than , a workstation is assumed to enter a low
power idling state. On the other hand, if the idle duration of a machine is less than or equal to
, a machine is supposed to enter a nominal power idling state.
: power consumption level of a busy state. A busy machine is assumed to spend per a
unit time.
: power consumption level of a nominal power idling state. A machine in the state is
assumed to spend per a unit time.
: power consumption level of a low power idling state. A machine in the state is assumed
to spend per a unit time.
Replication: this parameter defines the total number of simulation runs in the same set of
simulation parameters.
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Table 2-2: HySPEED Simulation Result
First of all, let us discuss what appears from HySPEED and Simio in common. Since
utilization is greater in scenario 1 (0.75) than scenario 2 (0.375), consumed energy for scenario
1 is larger than 2. The difference is approximately 30% for normal distributions of with
HySPEED and is 98% for with the same parameters. This large difference can be explained
in that low utilization is supposed to cause more frequent occurrences of low power idling
states consuming less energy. Also the difference between two distributions is worthy of note.
Since CV (coefficient of variation) is set as 0.1 for normal distributions in experiments, the most
significant difference between normal and exponential distributions seems to be CV. As CV is 1
for all exponential distributions, any spent energy difference between two distributions could be
regarded as caused by larger (exponential) or less (normal) variation of the mean values. In this
sense, the result does not seem definitive because either case shows the similar results. As the
analysis to look for influential parameters on energy consumption is performed in Chapter 2, it
seems better to move on to issues of HySPEED validation and verification in what follows.
2.6 Validation and Verification of HySPEED
In checking whether a simulation model is properly built, validation and verification are
considered [10], [11], [12]. In order to check validation and verification together, this thesis
Scenario Distribution HySPEED Simio Delta = (H-S)/S
1 Normal 55976083 51330910 55582700 52875900 0.7% -2.9%
Exponential 55963844 49841974 55566900 48680700 0.7% 2.4%
2 Normal 43026926 25970286 42780100 25899100 0.6% 0.3%
Exponential 43376421 25934264 42773000 26167300 1.4% -0.9%
18
makes comparison between results of HySPEED and the discrete event simulation software Simio.
In result comparison between HySPEED and Simio, the difference is less than 3% even in the
worst case, and therefore it is not well supported to assert that HySPEED and Simio show
significantly different results. However this conjecture is not a statistical result, and we need to
have more rigorous evidence that the two simulation tools show the same result in statistical or
more reliable sense. Even though there are many points to look into for comparison of HySPEED
and Simio, it seems to be better to focus on random numbers generated by two tools if we can
agree that fundamentals of HySPEED algorithm such as basic arithmetic operations are correct.
Figure 2-4: Random IAT (Inter-arrival Time) and Processing Time
Since a random number generator adopted by the Microsoft Excel failed some empirical
tests for good random numbers [13], let us make comparison of random numbers created by
HySPEED and Simio at the beginning. In Figure 2-4, sequences of all random IAT (inter-arrival
time) and processing times are plotted. In both cases of HySPEED and Simio, the average of IAT
is 15, and that of processing time is 40. Between distributions, an exponential distribution is
chosen since it has larger CV in expectation to see greater variation or difference for either case.
19
Figure 2-5: Exponential Random IAT Histogram (Mean = 15)
The Figure 2-5 and 2-6 are plots of probability density-like functions. Figure 2-5 shows
exponential random numbers with mean 15, and Figure 2-6 with mean 40 for HySPEED, Simio,
and theoretical values respectively. In both figures HySPEED and Simio are somewhat following
the trajectory of a theoretical case. Even though trajectories of Simio and HySPEED show slight
difference in some cases (e.g., x=25-40 of Figure 2-6), it is premature to think that random
numbers by Simio and HySPEED are from different distributions.
Figure 2-6: Exponential Random Processing Time Histogram (Mean = 15)
20
In Figure 2-7 and 2-8, plots of proportional cumulative frequency are illustrated for
HySPEED, Simio, and theoretical cases. As in Figure 2-5 and 2-6, it is thought that two sets of
random numbers out of HySPEED and Simio seem to be following the same distribution.
However we can‘t draw the definitive conclusion from this observation since it is based on an
empirical observation. Thus it is necessary to more rigorously check the difference between
random numbers of HySPEED and Simio.
Figure 2-7: Exponential Random IAT (Mean = 15)
Figure 2-8: Exponential Random IAT Histogram (Mean = 40)
21
To see the difference between streams of random numbers from HySPEED and Simio,
SFD (squared frequency deviation) between the theoretical value and each simulation tool is
calculated for each random number interval as follows:
* +
Since there are 20 intervals in Figure 2-9 and 41 intervals in the Figure 2-10, x axis of
each plot is defined so as to have the number of intervals respectively.
Figure 2-9: Squared Frequency Deviation
Generally the SFD values are greater in lower x values than in higher x values. This
observation can be explained in that the lower x values with higher frequency can cause larger
difference between the theoretical distribution and numbers from samples of each distribution.
This difference can be observed both in Figure 2-9 and 2-10, and therefore SFD decreases as x
increases.
22
Figure 2-10: HySPEED Discrete Event Algorithm Engine
Even though the above SFD analysis could show that the deviation of each random
number stream of two simulation tools from theoretical values is quite small, it is also an
empirical analysis. To draw a statistical conclusion about difference between two random number
streams, Kolmogorov-Smirnov test is performed to see whether or not each random number
stream is following an exponential distribution. The null hypothesis that each random number
stream from HySPEED and Simio is following exponential distribution is rejected when
√
| ( ) ( )|
( )
∑ * +
( )
( ) √
∑ ( )
( )
23
Since the level of confidence is 95%, , and correspondingly . This K-
S test is preformed 4 times, and the results are given below:
Table 2-3: K-S Test Result
As shown in Table 2-3, HySPEED and Simio both passed K-S test, suggesting that two
random number streams are following the theoretical exponential distribution with the
corresponding mean. In the next chapter, we will analyze which factors have greater impact on
energy consumption with HySPEED.
HySPEED Simio
√ Comparison
IAT 0.670715 0.72232 < 1.36
Processing Time 1.122953 0.632068 < 1.36
24
Chapter 3
Energy Consumption Analysis in Discrete Manufacturing System Based on
Design of Experiment
3.1 Introduction
Many factors are supposed to get involved in energy consumption for manufacturing
products. Among those factors having influence on energy consumption of machining processes,
some could have greater impact on electricity usage of machines than others. In this chapter
therefore related factors are categorized, and best independent factors are drawn from them so
that experiments can be conducted to see which independent factors have greater/less impact on
energy consumption of manufacturing products by a machine. These factors with significant
effects are compared with those by the regression analysis later.
3.2 Methodology
Since the new simulation tool HySPEED was developed for the energy consumption
analysis of machining, this chapter considers a methodology which could be fully using the
simulation tool. This point is what makes a methodology of this thesis different from existing
energy or power consumption methods of machining. Methods currently broadly used for
approximating power or energy consumption of machining are based on calculations with
textbook table values [14]. Whereas these methods are handy to estimate required time, power,
and energy for each machining process, they provide crude values rather than accurate values
since required time, power, and energy of manufacturing processes are dependent on a specific
machine and its MRR (material removal rate) [3], [15]. Thus to better estimate them, a method
25
specifying a machine and MRR with a detailed process plan needs to be in consideration since
once a specific machine type and MRR are determined from a predefined process plan,
processing times can be estimated as volume to be removed divided by MRR. The processing
time calculated above is then used for getting HySPEED parameters such as mean processing
time and machine utilization. After all parameters are available, experiments are designed to see
most influential parameters of interest. Based on various combinations of parameters with
different levels, an experimental design is determined, and experiments are performed with 2
replications for each HySPEED run. Setting the total energy spent or the total energy saved as a
response provides an opportunity to see influential parameters with ANOVA, and consequently
this will have a main focus of this chapter. In fact, experiment approach with simulation is not
new in analyzing energy consumption. For example some research [16] also used experiment to
see how much each set of parameters has influence on energy consumption of a machine.
However while two methods rely on experiments, they show difference in that a method of
previous research did not design experiments and used experimental results in a form of average.
Contrary to that, in this thesis a factorial design is adopted to fully use results from experiments
with help of ANOVA. In the next, a piece for which analysis will be conducted is introduced.
Figure 3-1: Pennsylvania State University Chess Set Pieces
26
3.3 Work Piece (Penn State Chess Set Pieces)
As a product to be manufactured, a piece of Penn State Chess Set is considered, and a
rook piece (the most left piece in Figure 3-1) of Figure 3-2 is selected since it has a relatively
simple shape and a short process plan to manufacture. The original CAD design and process plans
for a rook have been added in Figure 3-2, 3-3, and 3-4. Whereas the CAD design is quite useful,
the process plan provides sketchy data for the processing time for each step in manufacturing.
Since the processing time is considered as important in this model, a better way of estimating
time for processing in each step is in need, and an alternative way is adopted, a method to
calculate required time for each process by dividing the removed volume by the material removal
rate. To have the accurate volume to be removed from each process, the following calculations
are performed:
Figure 3-2: Penn State Chess Set CAD Data (Rook). Source: [17].
27
3.3.1 Turn Decorative Groove (No. 10 Process of Figure 3-3)
Description about the volume to be removed: The volume of the inner half of the torus with the
smaller radius 0.1 and the larger radius 0.625
∫ *( ) + √
( )
3.3.2 RH Rough Turn OD (No. 20 Process of Figure 3-3)
Description about the volume to be removed: the volume of rough cutting around the obelisk =
the volume of the cylinder of the diameter 1.25 – the volume of the cylinder of the diameter
√ :
( ) (
√ )
( ) ( )
3.3.3 Mill Sloped Sides of Obelisk (No. 10 Process of Figure 3-4)
Description about the volume to be removed for shaping the hexahedron (Main part of obelisk)
The volume of the hexahedron (Height = 1.35 inches)
∫ {
( )}
The volume of the cylinder (Height = 1.35 inches) left behind from (3.3.2)
(
√ )
( )
( )
3.3.4 Mill Sloped Sides of Obelisk (No. 20 Process of Figure 3-4)
Description: the volume to be removed for shaping the pyramid (Top of obelisk)
The volume of the pyramid (Height = 0.1 inches)
28
∫ ( )
The volume of the cylinder (Height = 0.1 inches) left behind from (3.3.2)
(
√ )
( )
Figure 3-3: Process Plan for a Rook Piece (1 of 2). Source: [17].
Figure 3-4: Process Plan for a Rook Piece (2 of 2). Source: [17].
29
3.4 Modified Process Plan for a Rook Piece
For each processing step, the volume to be removed is calculated and shown in Figure 3-
5. Then as mentioned, the estimated time for each step is calculated by dividing each volume by
the material removal rate, and this is more specifically discussed in the following section.
Figure 3-5: Modified Process Plan for a Rook Piece
3.5 Factors of Interest for Experimental Design
After the consideration about the independency of many factors, the following five factors are
chosen for experiments:
3.5.1 Machine Type [3]
(+1) Production Machining Center 2000
(-1) Automated Milling Machine 1988
3.5.2 Demand (this factor is about adjusting arrival rates since processing rate is dependent on the
material removal rate and the volume to be removed.)
(+1) Machine utilization 90%
(-1) Machine utilization 50%
30
3.5.3 Material Type (adjusting the max material removal rate recommended by a reference for
each machine in 3.5.1) [3]
(-1) Aluminum: Max MRR for each machine
(+1) Steel: Max MRR for each machine
3.5.4 Production Level (adjusting the processing time)
(-1) High: 100% of the max material removal rate
(-1) Low: 50% of the max material removal rate
3.5.5 Volume to Be Removed
(+1) High: 120% of the current values (assuming a larger piece would be manufactured.)
(-1) Low: The current volume
Figure 3-6: Parameters for Factorial Design
31
3.6 Experimental Design
Each parameter or factor in Figure 3-6 is:
Machine Type: This factor is the same one as in 3.5.1.
Demand: This factor is the same one as in 3.5.2.
Utilization (1): This parameter is the machine utilization of the first process (turning/lathe
station) and depends on the demand above.
Utilization (2): This parameter is the machine utilization of the second process (milling
station) and depends on the demand above.
Inter-arrival Time: Since a queueing model is used for machines, the inter-arrival time is
defined as the average interval length between arrivals of raw stocks to the first process.
As two machines are connected in a serial line, and departures of the first machine are
supposed be arrivals to the second machine, inter-arrival time is defined only for the first
machine.
Material Type: Total two different types of materials are considered between steel and
aluminum. This factor is also the same on as in 3.5.3.
Production Level: This factor is the same as in 3.5.4.
Max MRR: Maximum material removal rate of a machine for each material type.
MRR: Material removal rate adjusted by production level of 3.5.4.
Product Size: This factor is the same as in 3.5.5.
VTR (1): This parameter is about the volume to be removed by the first machine (turning)
and depends on other factors such as the product size and the machine type.
VTR (2): This parameter is about the volume to be removed by the second machine
(milling) and depends on other factors such as the product size and the machine type.
32
Mean Processing Time (1): This parameter is the average processing time of the first
machine (turning) and depends on other factors such as material removal rates, machine
types, and demands.
Mean Processing Time (2): This parameter is the average processing time of the second
machine (milling) and depends on other factors such as material removal rates, machine
types, and demands.
Mu (1): This is a reciprocal of the mean processing time of the first machine.
Mu (2): This is a reciprocal of the mean processing time of the second machine.
Constant Startup Operation: Power consumption level of an idle machine in watts for
each machine type. This number is from the reference [3].
Run-time Operation: Power consumption level of a busy (e.g. positioning) machine in
watts for each machine type. This number is from the reference [3].
Material Removal: Power consumption level of a busy machine in watts for each
machine type. This number is from the reference [3].
: This parameter is assumed to 10% of .
: This parameter is the same one as in the constant startup operation.
: This parameter is sum of power consumption levels with the constant startup
operation and the material removal.
Tau (1): This parameter is a time threshold to define a low power idling state of the first
machine. Any idling period greater than this parameter is regarded as in low power idling.
Tau (2): This parameter is a time threshold to define a low power idling state of the
second machine. Any idling period greater than this parameter is regarded as in low
power idling.
Simulation Run Time: This is defined as 5,000 time units in HySPEED.
33
Number of Replication: To reduce the impact of outliers somewhat, two replications are
made for each treatment.
Number of Machines: As mentioned above, a turning machine and a milling machine are
considered in this experiment.
3.7 Model Assumptions
Some assumptions to be notified are as follows:
The probability distributions for inter-arrival times and processing times are normal
distributions with CV (coefficient of variation) 0.3. Exponential distributions are not
adopted since the case can be relatively easily solved analytically [21].
For scarcity of power consumption data of various machines, , , and are
assumed to be same between turning and milling machines, and milling machine data are
used [3].
Utilization of machines is based on a machine which has a larger mean processing time to
keep utilization of the other machine less than 1.0.
Since this is not a physical experiment, randomization of the experiment order of 32
treatments is ignored and experiments are conducted in a standard order.
3.8 HySPEED
For all experiments, HySPEED is used, and the description about all parameters in
conducting experiments is given above. To have energy consumption with/without an energy
control policy, twice of the total treatments of experiments are run, and the total number of
experiments is 64.
34
3.9 Analysis
After all experiments, the total spent energy for each treatment is given from HySPEED.
Using this result, we analyze spent energy for EC on and EC off with the experimental design
technique and ANOVA (analysis of variance). To see different aspects of the result by
HySPEED, we consider two different response variables.
3.9.1 Response = Energy Saving (EC OFF – EC ON)
Since there are problems with normality and equality of variance as seen in Figure 3-7(a),
a square root is taken from the response. After the square root transformation on a response, the
residuals versus fitted values plot looks better as shown in Figure 3-7(b).
Fitted Value
Re
sid
ua
l
10000008000006000004000002000000
15000
10000
5000
0
-5000
-10000
-15000
Residuals Versus the Fitted Values(response is Saving)
Figure 3-7(a): Residuals VS. Fitted Values before Transformation
35
Fitted Value
Re
sid
ua
l
10008006004002000
10
5
0
-5
-10
Residuals Versus the Fitted Values(response is SR_C13)
Figure 3-7(b): Residuals VS. Fitted Values after Square Root Transformation
Residual
Pe
rce
nt
151050-5-10
99.9
99
95
90
80
7060504030
20
10
5
1
0.1
Normal Probability Plot of the Residuals(response is SR_C13)
Figure 3-7(c): Normal Probability Plot for Residuals after Square Root Transformation
36
Standardized Effect
Pe
rce
nt
6005004003002001000-100
99
95
90
80
70
60
50
40
30
20
10
5
1
Factor
Production Lev el
E Product S ize
Name
A Machine Ty pe
B Market Demand
C Material Ty pe
D
Effect Type
Not Significant
SignificantADE
AC
AB
B
A
Normal Probability Plot of the Standardized Effects(response is SR_C13, Alpha = .05)
Figure 3-7(d): Normal Probability Plot of Effects
Although there is still the normality issue as seen Figure 3-7(c), generally a little
departure from the normal line is not serious concern [18]. Thus it seems reasonable to draw a
conclusion that machine type, market demand, and the interaction of the two factors are
significant as seen in Figure 3-7(d). In other words, other factors are not significant, and therefore
machine type, market demand, and the interaction are sufficient to explain the change of the
variation in the response (energy saving). Since there is a normality issue, and we in fact already
know that the change of level of some factors is not in a linear relationship, the regression
analysis might not be meaningful, and it is not shown here. Regardless of the normality issue, R
square of this model is almost 99%, and it tells us that the three driving factors are able to explain
99% of the variation of the response. More details of Minitab result are added to Appendix A.
3.9.2 Response = (Energy Spent with EC ON / Number of Products)
37
As the case with a response of energy saving could be trivial, one more case is analyzed
with a response variable, energy consumption (EC1) for each product. Since the average number
of arrivals can be known for each treatment, EC1 (energy spent with the energy control policy) is
divided by the number of arrivals, and it is a response in this analysis. As there are similar
normality and equal variance problems to the previous case, a natural log is taken from a response
variable. The figures below are all plotted after the natural log transformation.
Fitted Value
Re
sid
ua
l
87654
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
Residuals Versus the Fitted Values(response is Ln_C15)
Figure 3-8(a): Residuals VS. Fitted Values after Log Transformation
38
Residual
Pe
rce
nt
0.040.030.020.010.00-0.01-0.02-0.03-0.04
99.9
99
95
90
80
7060504030
20
10
5
1
0.1
Normal Probability Plot of the Residuals(response is Ln_C15)
Figure 3-8(b): Normal Probability Plot for Residuals after Log Transformation
Standardized Effect
Pe
rce
nt
5004003002001000-100-200
99
95
90
80
70
60
50
40
30
20
10
5
1
Factor
Production Lev el
E Product S ize
Name
A Machine Ty pe
B Market Demand
C Material Ty pe
D
Effect Type
Not Significant
Significant
ACDE
CDE
ADE
DE
AB
E
D
C
B
A
Normal Probability Plot of the Standardized Effects(response is Ln_C15, Alpha = .05)
Figure 3-8(c): Normal Probability Plot of Effects
39
Figure 3-8(a) does not show any obvious pattern, and 3-8(b) shows that residuals are a
little apart from the normal distribution line. Since the existence of even very obvious pattern has
only slight impact on F test result, and the moderate departure from the normal line is not a
serious problem [18], it seems safe to draw a conclusion from Figure 3-8(c) that machine type,
material type, and production level are far from the normal probability line. This observation
suggests that the three factors are significant compared to other factors. Thus we can conclude
that machine type, material type, and production level have statistically significant influence on
energy required to manufacture a unit product. Even though many factors and interactions are
marked as significant (red squares in Figure 3-8(c)), their effects on a response can be added to
effects of the constant since their effects are very close to the normal probability line. In other
words, those effects are statistically significant but very small. Thus their influence on a response
can be negligible. As R square value is quite high (99%), this model seems able to explain
variation of a response very much. Even though R square will get lower after adding effects
nearby the normal probability line into a constant term, the change is expected to be very limited
as effects of factors to be added into a constant are low. More details of Minitab result are added
to Appendix B.
40
Chapter 4
Regression Analysis on Data of Industrial Assessments Centers Data
4.1 Introduction
In a previous chapter, more influential factors on energy spending have been identified.
However since the experiments were conducted within simulation, it is difficult to generalize the
result from the experiments for general cases. Thus there is a need to see if the analysis based on
a large amount of general industry data is suggesting that the similar factors have more impact on
energy consumption.
For this purpose, data of the IAC (Industrial Assessments Centers) is noteworthy. It is a
program the U.S. Department of Energy has financially supported since 1981 and has provided
more than 14,000 assessments about how each company spends energy for manufacturing final
products in various forms such as pieces, bushels, and tons of liquid [19]. Even though company
names are unidentifiable from the IAC dataset, it provides enough information for connecting
energy consumption with characteristics of each company in numeric values. In what follows let
us see the detail of the IAC dataset.
4.2 IAC Dataset
The IAC dataset consists of five different worksheets ASSESS, RECC1, RECC2, RECC3,
and RECC4 [19]. However since four of them are just recommendation data for companies, any
clue could not be found there to connect energy consumption of each company with various
parameters of the company. Thus only data of the assessment worksheet is supposed to deserve
attention, and data in other worksheets is excluded from this analysis.
41
The assessment data has 56 kinds of characteristics of a company (columns)
corresponding to 15,760 companies (rows) [19]. Every detail of the entire large amount of data is,
however, not useful in this analysis because the objective of this analysis is looking for energy
consumption characteristics of manufacturing companies which would have produced the
example chess set pieces in previous chapter. Hence among 56 characteristics of each company
the following columns are chosen:
SALES: Annual sales in the U.S. dollar.
EMPLOYEES: Total number of employees.
PLANT_AREA: Total amount of area for production and office in square feet.
PRODLEVEL: Total number of units annually produced.
PRODHOURS: Annual production hours.
The above variables are regarded as numerical variables. Even though many
characteristics are included to the dataset, most of them could not be considered since they are
irrelevant (e.g. usage of natural gas and coal). From a large number of rows (companies), only
assessments without any usage record in resources which are unlikely to be consumed in the
chess piece manufacturing process are included. For example, any observation (company) with
usage in natural gas, paper, or woods has been discarded for this analysis. Eventually 517
observations are left, and these assessments are used for building regression models. The
following subsection will give more details about this regression analysis.
4.3 Response Variable
For this analysis, a response variable electricity usage is considered. Although a
regression analysis can be performed with a different response variable electricity cost, the test is
not conducted here since the electricity usage seems to be a more appropriate response.
42
4.4 Regression Analysis
In this analysis, we expect to build a regression model and to see whether each factor is
significant or not. Thus a possible, resulting regression model is as follows:
Since there are five independent variables, let us build a multiple linear regression model
after performing a basic analysis about each independent variable.
4.4.1 Basic Analysis
Sales Employees Plant Area Prod. Level Prod. Hours
Regression
Equation
13.8% 12.1% 1.8% 5.9% 14.8%
Test for
Test for
(significant)
(significant)
(significant)
(significant)
(significant)
Residual
Normality
(assumption
violated)
(assumption
violated)
(assumption
violated)
(assumption
violated)
(assumption
violated)
Lack of fit
(assumption
violated)
(assumption
violated)
(assumption
violated)
(assumption
violated)
(assumption
violated)
Table 4-1: Simple Linear Regression Result
When checked with the response, every independent variable implies possible problems
such as issues of normality or curvature, and Table 4-1 shows these problems. Even though all
five independent variables are significant, a transformation is obviously in need to fix problems
described, and a natural log is taken from the response variable.
4.4.2 Multiple Regression Model
43
After the natural log transformation is performed from the response variable, a stepwise
(forward and backward) regression analysis is conducted to have only appropriate predictors in
the final model. As parameters, 0.15 is used for alpha to enter and remove, and the result from
Minitab is as follows:
Step 1 2 3 4 5
Constant 13.39 13.10 13.05 13.04 13.07
PRODHOURS 0.00025 0.00025 0.00024 0.00024 0.00024
T-Value 11.60 12.37 12.33 12.27 11.57
P-Value 0.000 0.000 0.000 0.000 0.000
SALES 0.00000 0.00000 0.00000 0.00000
T-Value 9.98 6.33 6.24 6.14
P-Value 0.000 0.000 0.000 0.000
EMPLOYEES 0.00080 0.00078 0.00079
T-Value 4.01 3.87 3.95
P-Value 0.000 0.000 0.000
PLANT_AREA 0.00000 0.00000
T-Value 1.55 1.52
P-Value 0.122 0.129
PRODLEVEL 0.00000
T-Value 1.47
P-Value 0.143
S 1.08 0.987 0.973 0.972 0.971
R-Sq 20.73 33.60 35.62 35.92 36.19
R-Sq(adj) 20.57 33.35 35.24 35.42 35.56
Mallows C-p 121.8 20.7 6.6 6.2 6.0
As shown in the above result, only production hours, sales, and employees are found
significant by the stepwise regression method, and therefore plant area and production level
should be removed from the final regression model. Since the objective of this regression analysis
is seeing whether each independent variable is significant or not rather than building an accurate
regression model, the final multiple regression model is not given here. More details of the
stepwise regression result and the final regression model with three variables are found in
Appendix C.
4.5 Discussion
44
This regression model clearly shows that production hours, the number of employees, and
sales are significant factors in determining electricity usage for American manufacturing
companies which mainly consume electricity for energy source. In the sense of important factors
on manufacturing electricity consumption, this regression result can be compared with that of the
previous experimental design, in which machine type, market demand, material type, and
production level are significant factors. After machine type and material type are ruled out
because the IAC data does not consider these factors, market demands can be directly related to
sales as well as production levels (processing time) can be connected to production hours and the
number of employees in the regression analysis. Thus results of the experimental design and the
regression analysis are similar to each other, and this observation suggests the experimental
design result of Chapter 3 can be generalized into broader industrial/manufacturing cases.
45
Chapter 5
Conclusion and Future Work
5.1 Conclusion
This thesis suggests the energy consumption analysis approach based on the simulation
software HySPEED. To give fundamental grounds of the simulation modeling, machine states
and corresponding power consumption levels are defined and illustrated in detail. Considering
different machine states and power consumption levels, HySPEED (simulation software) is able
to simulate multiple machines in a serial line with different parameters such as average
processing rates. Since accuracy and reliability of the new software is required to proceed with
this analysis, the validity of the software is checked, and verification is performed in comparison
with the simulation software Simio.
After HySPEED is validated and verified, experiments are designed to see which factors
are more influential on energy consumption of manufacturing an example work piece. When the
amount of potential energy saving is a response variable, machine type, demand, and the
interaction of the two factors are significant. For a response with the total energy spent for
producing one product, machine type, material type, and production level are significant and
influential in the written order of descending. In the regression analysis to see if the result of the
experimental design corresponds to general industry data, it turns out to be that product hours, the
number of employees and annual sales are significant factors in determining the total electricity
spent. Since these factors from two analyses can be related to each other as described in Chapter 4,
we can conclude that the experimental analysis by simulation shows results consistent with the
general industry data analysis.
46
5.2 Future Research
Since this thesis suggests the empirical approach for capturing dynamics of energy
consumption of machining processes, analytical approaches with mathematical models would be
good addition to this research. Especially as queueing models with M/M/1 could give a closed
form solution for the total amount of spent energy for manufacturing systems [21], the
consideration for more generalized arrival/processing processes provides an auspicious direction
toward further research.
47
References
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Release Reference Case (2012)
http://www.eia.gov/pressroom/presentations/howard_01232012.pdf
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http://www.eia.gov/forecasts/aeo/pdf/0383(2012).pdf
(3) Dahmus, J. B. and Gutowski, T. G.: An Environmental Analysis of Machining, 2004
ASME International Mechanical Engineering Congress and RD&D Expo, Anaheim, CA,
November pp. 13-19, 2004 (2004)
(4) Duque Ciceri, N., Gutowski, T.G., and Garetti, M.: A tool to estimate materials and
manufacturing energy for a product, 2010 IEEE International Symposium on Sustainable
Systems and Technology (ISSST), Arlington, VA, May 17-19 2010 (2010)
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W.P., Suh, S., Weidema, B.P., Pennington, D.W.: Life cycle assessment: Part 1:
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international 30(5), pp. 701–720 (2004)
(6) Pennington, D.W., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O., Rydberg, T.,
Rebitzer, G.: Life cycle assessment part 2: Current impact assessment practice.
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Discrete Manufacturing Systems, Service Orientation in Holonic and Multi Agent
Manufacturing and Robotics, Eds. T. Borangiu, A. Thomas, and D. Trentesaux, Springer-
Verlag Berlin Heidelberg, pp. 293-311, (2013)
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distributed discrete-event scheduling. Automatica, 38(9), pp. 1499-1515, (2002)
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2010 Winter Simulation Conference (2010)
(12) Banks, J., Carson II, J. S., Nelson, B. L., and Nicol, D. N.: Discrete-Event System
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(13) L‘ecuyer, P. and Simard, R.: TestU01: A C Library for Empirical Testing of Random
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Publishing Company (1995)
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(15) Diaz, N. Redelsheimer, E. and Dornfeld, D.: Energy Consumption Characterization and
Reduction Strategies for Milling Machine Tool Use. Glocalized Solutions for
Sustainability in Manufacturing 2011, pp. 263-267 (2011)
(16) Mouzon, G., Mehmet, B. Y., and Twomey, J.: Operational methods for minimization of
energy consumption of manufacturing equipment, International Journal of Production
Research, 45:18-19, pp. 4247-4271 (2007)
(17) Pennsylvania State University, Department of Industrial and Manufacturing Engineering:
FAME Lab. Virtual Information Center
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(18) Montgomery, D. C.: Design and Analysis of Experiments, 7th Edition, Wiley (2009)
(19) The U.S. Department of Energy: Industrial Assessment Centers Database
http://iac.rutgers.edu/database
(20) Kutner, M. H., Nachtsheim, C. J., and Neter, J.: Applied Linear Regression Models, 4th
Edition, McGraw Hill (2008)
(21) Prabhu, V. V., Jeon, H. W., and Taisch, M.: Modeling green factory physics—An
analytical approach, In Proceedings of Automation Science and Engineering (CASE),
2012 IEEE International Conference on, pp. 46-51. IEEE (2012)
49
Appendix A
Minitab Result for 3.9.1
Estimated Effects and Coefficients for SR_C13 (coded units)
Term Effect Coef SE Coef T P
Constant 489.65 0.6997 699.82 0.000
Machine Type 733.44 366.72 0.6997 524.13 0.000
Market Demand -141.69 -70.85 0.6997 -101.26 0.000
Material Type 2.36 1.18 0.6997 1.69 0.101
Production Level 1.10 0.55 0.6997 0.79 0.436
Product Size 2.18 1.09 0.6997 1.56 0.129
Machine Type*Market Demand -107.83 -53.92 0.6997 -77.06 0.000
Machine Type*Material Type 4.05 2.02 0.6997 2.89 0.007
Machine Type*Production Level 2.40 1.20 0.6997 1.72 0.096
Machine Type*Product Size 0.14 0.07 0.6997 0.10 0.921
Market Demand*Material Type 0.27 0.14 0.6997 0.20 0.846
Market Demand*Production Level 0.50 0.25 0.6997 0.36 0.722
Market Demand*Product Size 1.24 0.62 0.6997 0.89 0.381
Material Type*Production Level -1.65 -0.83 0.6997 -1.18 0.246
Material Type*Product Size 1.93 0.97 0.6997 1.38 0.177
Production Level*Product Size -2.73 -1.36 0.6997 -1.95 0.060
Machine Type*Market Demand* 1.80 0.90 0.6997 1.28 0.209
Material Type
Machine Type*Market Demand* 0.65 0.33 0.6997 0.47 0.644
Production Level
Machine Type*Market Demand* 0.58 0.29 0.6997 0.42 0.680
Product Size
Machine Type*Material Type* 0.42 0.21 0.6997 0.30 0.767
Production Level
Machine Type*Material Type* -1.05 -0.52 0.6997 -0.75 0.460
Product Size
Machine Type*Production Level* 4.58 2.29 0.6997 3.28 0.003
Product Size
Market Demand*Material Type* 1.87 0.93 0.6997 1.34 0.191
Production Level
Market Demand*Material Type* 0.97 0.48 0.6997 0.69 0.495
Product Size
Market Demand*Production Level* -1.92 -0.96 0.6997 -1.38 0.179
Product Size
Material Type*Production Level* -2.45 -1.23 0.6997 -1.75 0.089
Product Size
Machine Type*Market Demand* 0.58 0.29 0.6997 0.42 0.680
Material Type*Production Level
Machine Type*Market Demand* 0.52 0.26 0.6997 0.37 0.712
Material Type*Product Size
Machine Type*Market Demand* 0.99 0.50 0.6997 0.71 0.483
Production Level*Product Size
Machine Type*Material Type* 2.73 1.37 0.6997 1.95 0.059
Production Level*Product Size
Market Demand*Material Type* -2.21 -1.11 0.6997 -1.58 0.123
Production Level*Product Size
Machine Type*Market Demand* -0.87 -0.43 0.6997 -0.62 0.539
Material Type*Production Level*
Product Size
S = 5.59741 R-Sq = 99.99% R-Sq(adj) = 99.98%
50
Appendix B
Minitab Result for 3.9.2
Estimated Effects and Coefficients for Ln_C15 (coded units)
Term Effect Coef SE Coef T P
Constant 6.0805 0.001900 3200.05 0.000
Machine Type 1.6900 0.8450 0.001900 444.72 0.000
Market Demand -0.0804 -0.0402 0.001900 -21.15 0.000
Material Type 1.4356 0.7178 0.001900 377.76 0.000
Production Level -0.6974 -0.3487 0.001900 -183.52 0.000
Product Size 0.1798 0.0899 0.001900 47.30 0.000
Machine Type*Market Demand -0.0191 -0.0096 0.001900 -5.04 0.000
Machine Type*Material Type 0.0059 0.0029 0.001900 1.54 0.132
Machine Type*Production Level -0.0028 -0.0014 0.001900 -0.73 0.471
Machine Type*Product Size 0.0003 0.0001 0.001900 0.07 0.942
Market Demand*Material Type -0.0010 -0.0005 0.001900 -0.26 0.797
Market Demand*Production Level 0.0030 0.0015 0.001900 0.80 0.432
Market Demand*Product Size -0.0015 -0.0007 0.001900 -0.39 0.697
Material Type*Production Level 0.0053 0.0026 0.001900 1.38 0.176
Material Type*Product Size -0.0035 -0.0018 0.001900 -0.93 0.359
Production Level*Product Size 0.0080 0.0040 0.001900 2.10 0.044
Machine Type*Market Demand* 0.0020 0.0010 0.001900 0.52 0.606
Material Type
Machine Type*Market Demand* -0.0035 -0.0017 0.001900 -0.91 0.369
Production Level
Machine Type*Market Demand* -0.0007 -0.0003 0.001900 -0.17 0.864
Product Size
Machine Type*Material Type* -0.0025 -0.0012 0.001900 -0.66 0.517
Production Level
Machine Type*Material Type* 0.0032 0.0016 0.001900 0.84 0.407
Product Size
Machine Type*Production Level* -0.0129 -0.0065 0.001900 -3.39 0.002
Product Size
Market Demand*Material Type* -0.0034 -0.0017 0.001900 -0.90 0.374
Production Level
Market Demand*Material Type* -0.0021 -0.0010 0.001900 -0.55 0.585
Product Size
Market Demand*Production Level* 0.0043 0.0021 0.001900 1.12 0.269
Product Size
Material Type*Production Level* 0.0078 0.0039 0.001900 2.06 0.047
Product Size
Machine Type*Market Demand* -0.0030 -0.0015 0.001900 -0.78 0.439
Material Type*Production Level
Machine Type*Market Demand* 0.0002 0.0001 0.001900 0.04 0.966
Material Type*Product Size
Machine Type*Market Demand* -0.0007 -0.0003 0.001900 -0.17 0.865
Production Level*Product Size
Machine Type*Material Type* -0.0108 -0.0054 0.001900 -2.85 0.007
Production Level*Product Size
Market Demand*Material Type* 0.0034 0.0017 0.001900 0.90 0.377
Production Level*Product Size
Machine Type*Market Demand* 0.0030 0.0015 0.001900 0.78 0.441
Material Type*Production Level*
Product Size
S = 0.0152010 R-Sq = 99.99% R-Sq(adj) = 99.98%
51
Appendix C
Minitab Result for 4.4.2
Stepwise Regression: LN_UE versus SALES, EMPLOYEES, ... Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15
Response is LN_UE on 5 predictors, with N = 517
Step 1 2 3 4 5
Constant 13.39 13.10 13.05 13.04 13.07
PRODHOURS 0.00025 0.00025 0.00024 0.00024 0.00024
T-Value 11.60 12.37 12.33 12.27 11.57
P-Value 0.000 0.000 0.000 0.000 0.000
SALES 0.00000 0.00000 0.00000 0.00000
T-Value 9.98 6.33 6.24 6.14
P-Value 0.000 0.000 0.000 0.000
EMPLOYEES 0.00080 0.00078 0.00079
T-Value 4.01 3.87 3.95
P-Value 0.000 0.000 0.000
PLANT_AREA 0.00000 0.00000
T-Value 1.55 1.52
P-Value 0.122 0.129
PRODLEVEL 0.00000
T-Value 1.47
P-Value 0.143
S 1.08 0.987 0.973 0.972 0.971
R-Sq 20.73 33.60 35.62 35.92 36.19
R-Sq(adj) 20.57 33.35 35.24 35.42 35.56
Mallows C-p 121.8 20.7 6.6 6.2 6.0
Best alternatives:
Variable SALES EMPLOYEES PLANT_AREA PRODLEVEL
T-Value 9.09 8.55 1.85 1.50
P-Value 0.000 0.000 0.065 0.135
Variable EMPLOYEES PLANT_AREA PRODLEVEL
T-Value 8.21 2.81 1.27
P-Value 0.000 0.005 0.204
Variable PRODLEVEL PRODLEVEL
T-Value 3.85 1.53
P-Value 0.000 0.127
Variable PLANT_AREA
T-Value 3.04
P-Value 0.002
52
Regression Analysis: LN_UE versus SALES, EMPLOYEES, PRODHOURS The regression equation is
LN_UE = 13.0 + 0.000000 SALES + 0.000803 EMPLOYEES + 0.000244 PRODHOURS
Predictor Coef SE Coef T P VIF
Constant 13.0460 0.1100 118.55 0.000
SALES 0.00000001 0.00000000 6.33 0.000 1.4
EMPLOYEES 0.0008026 0.0002004 4.01 0.000 1.4
PRODHOURS 0.00024369 0.00001976 12.33 0.000 1.0
S = 0.973056 R-Sq = 35.6% R-Sq(adj) = 35.2%
Analysis of Variance
Source DF SS MS F P
Regression 3 268.711 89.570 94.60 0.000
Residual Error 513 485.728 0.947
Lack of Fit 511 485.143 0.949 3.25 0.265
Pure Error 2 0.585 0.292
Total 516 754.439
513 rows with no replicates
Source DF Seq SS
SALES 1 104.277
EMPLOYEES 1 20.440
PRODHOURS 1 143.995
Unusual Observations
Obs SALES LN_UE Fit SE Fit Residual St Resid
1 250000000 17.4770 15.7683 0.2661 1.7087 1.83 X
7 300000000 16.9307 19.1774 0.5607 -2.2467 -2.83RX
8 45567855 16.8952 14.6001 0.0487 2.2951 2.36R
9 20000000 16.8338 14.6763 0.0885 2.1575 2.23R
10 310000000 16.7593 16.5988 0.3292 0.1604 0.18 X
17 13000000 16.5985 13.5473 0.0819 3.0512 3.15R
24 63000000 16.4651 15.3894 0.1720 1.0757 1.12 X
35 400000000 16.2824 17.8569 0.4272 -1.5745 -1.80 X
52 300000000 16.0834 19.1774 0.5607 -3.0940 -3.89RX
210 49400000 14.9625 15.5908 0.2565 -0.6284 -0.67 X
227 250000000 14.8101 16.2531 0.2529 -1.4430 -1.54 X
378 160000000 13.9822 14.8633 0.1771 -0.8810 -0.92 X
487 500000 12.8689 15.1049 0.0899 -2.2360 -2.31R
502 1000000 12.3216 14.2995 0.0555 -1.9779 -2.04R
505 5000000 12.1937 14.4817 0.0543 -2.2880 -2.36R
508 1500000 11.8625 14.0667 0.0555 -2.2042 -2.27R
509 7500000 11.8353 14.2567 0.0526 -2.4214 -2.49R
510 3000000 11.8268 14.1235 0.0541 -2.2967 -2.36R
512 1500000 11.6619 14.0993 0.0549 -2.4374 -2.51R
513 400000 11.6411 13.6145 0.0742 -1.9733 -2.03R
514 2000000 11.5256 13.5687 0.0772 -2.0431 -2.11R
515 800000 11.4916 13.6030 0.0750 -2.1114 -2.18R
53
516 2450000 11.1537 13.6791 0.0711 -2.5254 -2.60R
517 20000000 3.5835 14.7735 0.0496 -11.1900 -11.51R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
Lack of fit test
Possible curvature in variable SALES (P-Value = 0.000 )
Possible interaction in variable SALES (P-Value = 0.000 )
Possible curvature in variable EMPLOYEE (P-Value = 0.000 )
Possible interaction in variable PRODHOUR (P-Value = 0.001 )
Possible lack of fit at outer X-values (P-Value = 0.001)
Overall lack of fit test is significant at P = 0.000