CORE SEMINAR 1

Tool Wear Monitoring and AlarmTool Wear Monitoring and AlarmSystem Based on PatternSystem Based on PatternRecognition With LogicalRecognition With Logical

Analysis of DataAnalysis of Data

Presented By:Presented By: Name: Nehem TuduName: Nehem Tudu Roll No.: M150360MERoll No.: M150360ME Branch: Manufacturing Technology Branch: Manufacturing Technology

CORE SEMINAR 2

CONTENTCONTENT

IntroductionIntroduction Logical Analysis of DataLogical Analysis of Data Working of LADWorking of LAD Design of ExperimentDesign of Experiment Knowledge Extracting and LearningKnowledge Extracting and Learning The Statistical Proportional Hazards Model (PHM)The Statistical Proportional Hazards Model (PHM) LAD Online Alarm System DevelopmentLAD Online Alarm System Development Comparison With the PHM Alarm FunctionComparison With the PHM Alarm Function DiscussionDiscussion ConclusionConclusion ReferenceReference

CORE SEMINAR 3

INTRODUCTIONINTRODUCTION Tool wear in machining is analyzed by:Tool wear in machining is analyzed by:

1.1. Theoretical & numerical approachTheoretical & numerical approach2.2. Data driven approachData driven approach

Our objective is to report & discuss the results obtained experimentallyOur objective is to report & discuss the results obtained experimentally The work-piece material is TiMMCs The work-piece material is TiMMCs Differentiate between two types of covariates;Differentiate between two types of covariates;

1.1. Internal (diagnostic) covariates, which carry direct information about the wear processInternal (diagnostic) covariates, which carry direct information about the wear process2.2. External (environmental or/& machining conditions) covariates, which affect the wear processExternal (environmental or/& machining conditions) covariates, which affect the wear process

Internal covariates are observed by online monitoring of time dependent factorsInternal covariates are observed by online monitoring of time dependent factors Combination of internal & external covariates is used before in order to develop accurate Combination of internal & external covariates is used before in order to develop accurate

modelmodel Tool wear monitoring system based on LAD during turning TiMMCs under variable Tool wear monitoring system based on LAD during turning TiMMCs under variable

conditions were implementedconditions were implemented The platform of PXI & LABVIEW were used to develop the tool wear alarm systemThe platform of PXI & LABVIEW were used to develop the tool wear alarm system

CORE SEMINAR 4

TOOL WEARTOOL WEAR The rate at which the cutting edge of a The rate at which the cutting edge of a

tool wears away during machiningtool wears away during machining In cutting process, produced by contact In cutting process, produced by contact

& relative sliding between & relative sliding between Cutting tool & the work-piece Cutting tool & the work-piece Between the cutting tool & the chip Between the cutting tool & the chip

under the extreme conditions of cutting areaunder the extreme conditions of cutting area Type of tool wear Type of tool wear

Crater wear on rake face Crater wear on rake face Flank wear on flank faceFlank wear on flank face

Tool wear mechanisms in metal cutting Tool wear mechanisms in metal cutting include include Abrasive wearAbrasive wear Adhesive wearAdhesive wear Diffusion wearDiffusion wear Oxidation wear, etc.,Oxidation wear, etc.,

CORE SEMINAR 5

LOGICAL ANALYSIS OF DATA (LAD)LOGICAL ANALYSIS OF DATA (LAD) LAD is a data-driven combinatorial optimization technique that allows the LAD is a data-driven combinatorial optimization technique that allows the

classification of phenomena based on pattern recognitionclassification of phenomena based on pattern recognition LAD is applied in two consecutive stages:LAD is applied in two consecutive stages:

1.1. Training or learning stage, part of the data is used to extract special patterns of some Training or learning stage, part of the data is used to extract special patterns of some phenomenaphenomena

2.2. Testing or the theory formation stage, the remainder of the data is used to test the Testing or the theory formation stage, the remainder of the data is used to test the accuracy of the previously learned knowledgeaccuracy of the previously learned knowledge

LAD is based on supervised learningLAD is based on supervised learning In this work, we have two classes of cutting toolIn this work, we have two classes of cutting tool::

1.1. Worn-out toolWorn-out tool2.2. A fresh toolA fresh tool

Has certain advantages over other techniques:Has certain advantages over other techniques: It is a non-statistical approachIt is a non-statistical approach It does not need any prior assumptions regarding the posteriori class probabilitiesIt does not need any prior assumptions regarding the posteriori class probabilities User are able to track back any results (phenomena or effects) to its possible causesUser are able to track back any results (phenomena or effects) to its possible causes

CORE SEMINAR 6

Working of LADWorking of LAD

Each observation carries the values of the internal & external covariates & a Each observation carries the values of the internal & external covariates & a labellabel

Internal covariates are the radial force (fx), the feed force (fy), and the Internal covariates are the radial force (fx), the feed force (fy), and the cutting force (fz)cutting force (fz)

External covariates are the cutting speed (v) and the feed rate (f )External covariates are the cutting speed (v) and the feed rate (f ) After accomplishment of the two phases worn patterns & fresh patterns are After accomplishment of the two phases worn patterns & fresh patterns are

found by LADfound by LAD Worn patterns are used in order to develop tool wear monitoring modelWorn patterns are used in order to develop tool wear monitoring model Model is later incorporated in the platform of PXI & LABVIEW in order to Model is later incorporated in the platform of PXI & LABVIEW in order to

monitor the tool wear online & to give an alarm when the tool worn patterns monitor the tool wear online & to give an alarm when the tool worn patterns are detectedare detected

Observations are classified as either +ve (fresh, πObservations are classified as either +ve (fresh, π+ + , class 1) or -ve (worn-, class 1) or -ve (worn-out,out, ππ-- , class 2) , class 2)

LAD generates collections of patterns which characterizes each classLAD generates collections of patterns which characterizes each class Patterns represent interactions between variables separatelyPatterns represent interactions between variables separately

CORE SEMINAR 7

First steps involved in LADFirst steps involved in LAD Data binarization is the process of transformation of data into a Boolean Data binarization is the process of transformation of data into a Boolean

databasedatabase This technique substitutes each numerical variable by at least one binary This technique substitutes each numerical variable by at least one binary

attribute attribute For e.g, binarization of a continuous numerical variable For e.g, binarization of a continuous numerical variable AA is done by ranking, in is done by ranking, in

ascending order, all the distinct values of the numerical variable ascending order, all the distinct values of the numerical variable AA as follows: as follows: uuA A

(1) (1) < u < uAA (2) (2) <..... <u<..... <uA A (q) (q) (q≤Q) (1)(q≤Q) (1)

where q is the total number of distinct values of the variable where q is the total number of distinct values of the variable AA, & , & QQ is the total is the total number of observations in the training set. number of observations in the training set.

The cut-points The cut-points δδA,,jA,,j, where , where j j is the number of cut-points for each variable, are is the number of cut-points for each variable, are found between each pair of values that belong to different classes. found between each pair of values that belong to different classes.

By using Eq. (2), the cut-points are calculated as follows:By using Eq. (2), the cut-points are calculated as follows: δδA,j A,j =(u=(uAA

(k)(k)+u+uAA(k+1)(k+1))/2 (2))/2 (2)

where where uuA A (k)(k)ϵ πϵ π+ + & & uuAA

(k+1)(k+1)ϵ πϵ π- - or vice versa. or vice versa. A binary attribute A binary attribute bb is then formed from each cut-point is then formed from each cut-point Each cut-point Each cut-point δδA,jA,j has a corresponding binary attribute has a corresponding binary attribute bbδA,jδA,j with defined valuewith defined value bbδA,j δA,j = 1 if u= 1 if uA A ≥ δ≥ δA,jA,j

0 if u0 if uAA < δ < δA,j A,j (3)(3)

CORE SEMINAR 8

Second steps involved in LADSecond steps involved in LAD Pattern generation, the key building block Pattern generation, the key building block

in LAD knowledge extractionin LAD knowledge extraction Linear programming is used to generate Linear programming is used to generate

patternspatterns It is assumed that each generated pattern It is assumed that each generated pattern

p is associated with a Boolean pattern p is associated with a Boolean pattern vector vector W=(wW=(w11,w,w22,...,w,...,wqq,w,wq+1q+1,....,w,....,w2q 2q )) with with size n where size n where n=2qn=2q, , qq is the size of binary is the size of binary observation vectorobservation vector

If If wwj+qj+q=1=1 then literal then literal x xj j is included in is included in pattern pattern pp

Y=(yY=(y11, y, y22,....., y,....., yDD++)) is Boolean coverage is Boolean coverage

vector whose number of elements = vector whose number of elements = number of +ve observation number of +ve observation DD++ & where & where yyi i = 0= 0 if a pattern if a pattern pp++ covers +ve observation covers +ve observation ii & & 11 otherwise otherwise

Each +ve observation Each +ve observation i ϵ πi ϵ π++ is is represented as a Boolean observation represented as a Boolean observation vector vector aai,j i,j =1=1 if the binary attribute if the binary attribute bbj j =1=1 & & aai,j+qi,j+q=1=1 if if bbj j =0=0

CORE SEMINAR 9

Third steps involved in LADThird steps involved in LAD

Theory formation or testing stage is the final step in the LAD decision modelTheory formation or testing stage is the final step in the LAD decision model Zero value means that LAD cannot classify the observation where Zero value means that LAD cannot classify the observation where NN++ (N (N--) is ) is

the number of the number of +ve (-ve)+ve (-ve) patterns that are generated, patterns that are generated, ZZii++(o)(Z(o)(Zii

--(o))=1(o))=1if patternif pattern (i) (i) covers observation O, & zero otherwisecovers observation O, & zero otherwise

σσii+ + (σ(σii

--)) is the weight of the is the weight of the +ve (-ve)+ve (-ve) pattern pattern The calculated value of The calculated value of Δ(o)Δ(o) of any new observation gives an indication to of any new observation gives an indication to

whether the observation belongs to fresh or worn-out classwhether the observation belongs to fresh or worn-out class To measure the accuracy, the quality of classification To measure the accuracy, the quality of classification vv is used is used where where a a & & bb are the proportion of observations, are the proportion of observations, +ve+ve & & -ve-ve, which are , which are

correctly classifiedcorrectly classified cc & & ee are the proportion of observations, are the proportion of observations, +ve+ve & & -ve-ve, which are unclassified, which are unclassified

CORE SEMINAR 10

Design of ExperimentDesign of Experiment TThe experiment was conducted in the he experiment was conducted in the

machining laboratory at Ècole machining laboratory at Ècole Polytechnique de MontrèaPolytechnique de Montrèa

Cylindrical bar of Ti-6Al-4V alloy Cylindrical bar of Ti-6Al-4V alloy reinforced with 10-12% volume fraction reinforced with 10-12% volume fraction of TiC ceramic particle is usedof TiC ceramic particle is used

The wear is measured at discrete The wear is measured at discrete points of time through inspections using points of time through inspections using an Olympus SZ-X12 microscopean Olympus SZ-X12 microscope

The procedure continues until the tool The procedure continues until the tool wear reached predefined threshold wear reached predefined threshold (VB(VBBmax Bmax =0.2mm)=0.2mm)

This procedure is repeated for 28 toolsThis procedure is repeated for 28 tools

CORE SEMINAR 11

Knowledge Extraction and LearningKnowledge Extraction and Learning Cutting tool is failed when the tool is getting dull & no longer operates with Cutting tool is failed when the tool is getting dull & no longer operates with

acceptable qualityacceptable quality Cutting tool fails after reaching the worn-out stageCutting tool fails after reaching the worn-out stage Classification limits is considered Classification limits is considered

To distinguish between fresh & worn-out toolsTo distinguish between fresh & worn-out tools In some cases where the velocity is high, progressive wear is rapidly evolving & In some cases where the velocity is high, progressive wear is rapidly evolving &

there is just one observation for wear value above 0.15mmthere is just one observation for wear value above 0.15mm

CORE SEMINAR 12

This classification procedure is repeated for the 28 toolsThis classification procedure is repeated for the 28 tools Our objective is to use the data presented in Table 2 to train LAD to detect Our objective is to use the data presented in Table 2 to train LAD to detect

automatically the worn patterns & without human interferenceautomatically the worn patterns & without human interference The software CBMLAD is used, in order to extract the knowledge from the The software CBMLAD is used, in order to extract the knowledge from the

collected data, and then to train LADcollected data, and then to train LAD

CORE SEMINAR 13

Set O of the 273 observations is also divided into two sets of training, L, Set O of the 273 observations is also divided into two sets of training, L, and testing, Tand testing, T

Tenfold cross validation procedure is conductedTenfold cross validation procedure is conducted The quality of classification is calculated on the testing setThe quality of classification is calculated on the testing set Thus, the learning procedure is repeated ten times with different training Thus, the learning procedure is repeated ten times with different training

setssets The results show that the quality of classificationThe results show that the quality of classification v v =97.2% =97.2% The obtained five worn patterns do not cover any observation in fresh tool The obtained five worn patterns do not cover any observation in fresh tool

spacespace Patterns will lead us to build the online tool wear alarm systemPatterns will lead us to build the online tool wear alarm system

CORE SEMINAR 14

The Statistical Proportional Hazards Model (PHM)The Statistical Proportional Hazards Model (PHM) A proportional hazards model (PHM) of the wear process is developed from A proportional hazards model (PHM) of the wear process is developed from

the obtained experimental datathe obtained experimental data We calculate the time to failure (TTF) by interpolating between two We calculate the time to failure (TTF) by interpolating between two

measurements around the failure thresholdmeasurements around the failure threshold TTF is calculated when tool wear threshold is reachedTTF is calculated when tool wear threshold is reached The concept of a PHM is that the failure rate of the cutting tool is not only The concept of a PHM is that the failure rate of the cutting tool is not only

dependent on the age of the tool but also is affected by the internal & dependent on the age of the tool but also is affected by the internal & external covariatesexternal covariates

CORE SEMINAR 15

We consider the Weibull distribution as a baseline functionWe consider the Weibull distribution as a baseline function The failure hazard rate is written asThe failure hazard rate is written as

The conditional survival function can thus be given asThe conditional survival function can thus be given as

The conditional survival function The conditional survival function R(t;Y,Z) R(t;Y,Z) & its derivative & its derivative R(t;Y,Z)= R(t;Y,Z)= h(t;Y,Z)R(t;Y,Z)h(t;Y,Z)R(t;Y,Z) are used to estimate the parameters are used to estimate the parameters (β,η,α(β,η,α11,γ,γ11,γ,γ22)) by using the by using the maximum likelihood functionmaximum likelihood function

EXAKT software estimates the PHM parameters as shownEXAKT software estimates the PHM parameters as shown

Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)

CORE SEMINAR 16

Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)

The PHM model with all significant The PHM model with all significant variables is found by eliminating variables is found by eliminating the variables whose impact on the the variables whose impact on the probability of failure is lowprobability of failure is low

It is concluded that the effects of It is concluded that the effects of the radial force and the cutting the radial force and the cutting force are higher than the effect of force are higher than the effect of the feed force on the progressive the feed force on the progressive flank tool wearflank tool wear

EXAKT produces the EXAKT produces the Kolmogorov–Smirnov test which Kolmogorov–Smirnov test which evaluates the model fitevaluates the model fit

CORE SEMINAR 17

EXAKT gives a control-limit, EXAKT gives a control-limit, d > 0d > 0 which is used in order to find the minimum which is used in order to find the minimum expected machining cost per unit timeexpected machining cost per unit time

Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)

CORE SEMINAR 18

LAD Online Alarm System DevelopmentLAD Online Alarm System Development

The platform of PXI and LABVIEW were used to develop the online alarm The platform of PXI and LABVIEW were used to develop the online alarm systemsystem

For each transmitted set of measurements, the system search for worn For each transmitted set of measurements, the system search for worn patterns until color-coded lamp turns to red, when worn pattern is detectedpatterns until color-coded lamp turns to red, when worn pattern is detected

CORE SEMINAR 19

Comparison With the PHM Alarm FunctionComparison With the PHM Alarm Function

In order to compare the results, the recommended optimal time to In order to compare the results, the recommended optimal time to replacement is calculated by using the covariate's valuesreplacement is calculated by using the covariate's values

The recommended optimal replacement time according to certain covariate's The recommended optimal replacement time according to certain covariate's values using PHM are calculated using the below equationvalues using PHM are calculated using the below equation

CORE SEMINAR 20

CORE SEMINAR 21

DiscussionDiscussion

PHM decisions are based on the assumption of a statistical goodness of fit PHM decisions are based on the assumption of a statistical goodness of fit of a suitable hazard function & the cost's ratioof a suitable hazard function & the cost's ratio

LAD alarm points are based on pattern recognitionLAD alarm points are based on pattern recognition LAD replacement decision gave warning alarm before the tool wear reached LAD replacement decision gave warning alarm before the tool wear reached

the maximum flank wear the maximum flank wear VBVBBmax Bmax =0.2mm=0.2mm & without losing valuable resource & without losing valuable resource due to early replacementdue to early replacement

LAD can detect worn patterns online & in real time by monitoring covariates LAD can detect worn patterns online & in real time by monitoring covariates over timeover time

Important requirement for using LAD is the availability of a database that Important requirement for using LAD is the availability of a database that represents accurately the phenomena under studyrepresents accurately the phenomena under study

CORE SEMINAR 22

ConclusionConclusion

A new online tool wear alarm system based on LAD is developedA new online tool wear alarm system based on LAD is developed Alarm system is constructed based on data collected during turning Alarm system is constructed based on data collected during turning

TiMMCs, under changeable machining conditionsTiMMCs, under changeable machining conditions Platform of PXI and LABVIEW were used to develop the alarm systemPlatform of PXI and LABVIEW were used to develop the alarm system LAD alarm system is validated by comparing it to the PHM warning LAD alarm system is validated by comparing it to the PHM warning

functionfunction Results show that the proposed alarm system detects the worn patterns Results show that the proposed alarm system detects the worn patterns

and gives “warning alarm” in order to replace the cutting tool at a and gives “warning alarm” in order to replace the cutting tool at a working age that is relatively closer to the actual observed failure timeworking age that is relatively closer to the actual observed failure time

CORE SEMINAR 23

Future Scope of WorkFuture Scope of Work

The performance of the alarm system will be improved by including The performance of the alarm system will be improved by including additional variables, such as vibration signal, AEs, & cutting temperaturesadditional variables, such as vibration signal, AEs, & cutting temperatures

In order to distinguish between different tool wear phases, a multiclass LAD In order to distinguish between different tool wear phases, a multiclass LAD technique will be testedtechnique will be tested

The quality of the detected patterns will be improved, & nonpure patterns The quality of the detected patterns will be improved, & nonpure patterns which can cover more than one class will be used, & give more details which can cover more than one class will be used, & give more details about the characteristics of LAD’s patternsabout the characteristics of LAD’s patterns

CBMLAD and our alarm system will be incorporated in a CNC machineCBMLAD and our alarm system will be incorporated in a CNC machine The learning stage can be done online thereby eliminating the need for The learning stage can be done online thereby eliminating the need for

offline analysisoffline analysis

CORE SEMINAR 24

ReferenceReference

Shaban, Y., Yacout, S., and Balazinski, M., 2015, "Tool Wear Monitoring Shaban, Y., Yacout, S., and Balazinski, M., 2015, "Tool Wear Monitoring and Alarm System Based on Pattern Recognition With Logical Analysis of and Alarm System Based on Pattern Recognition With Logical Analysis of Data," ASME J. Manuf. Sci. Eng., 137(4), p. 041004.Data," ASME J. Manuf. Sci. Eng., 137(4), p. 041004.

Li, B., 2012, “A Review of Tool Wear Estimation Using Theoretical Analysis Li, B., 2012, “A Review of Tool Wear Estimation Using Theoretical Analysis and Numerical Simulation Technologies,” Int. J. Refract. Met. Hard Mater., and Numerical Simulation Technologies,” Int. J. Refract. Met. Hard Mater., 35, pp. 143–151.35, pp. 143–151.

Ryoo, H. S., and Jang, I. Y., 2009, “MILP Approach to Pattern Generation in Ryoo, H. S., and Jang, I. Y., 2009, “MILP Approach to Pattern Generation in Logical Analysis of Data,” Discrete Appl. Math., 157(4), pp. 749–761.Logical Analysis of Data,” Discrete Appl. Math., 157(4), pp. 749–761.

Makis, V., 1995, “Optimal Replacement of a Tool Subject to Random Makis, V., 1995, “Optimal Replacement of a Tool Subject to Random Failure,” Int. J. Prod. Econ., 41(1), pp. 249–256.Failure,” Int. J. Prod. Econ., 41(1), pp. 249–256.

Chik, Z., Aljanabi, Q. A., Kasa, A., and Taha, M. R., 2014, "Tenfold cross Chik, Z., Aljanabi, Q. A., Kasa, A., and Taha, M. R., 2014, "Tenfold cross validation artificial neural network modeling of the settlement behavior of a validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment," Springer Arab J Geosci., stone column under a highway embankment," Springer Arab J Geosci., 7:4877–4887.7:4877–4887.

CORE SEMINAR 25