Machine Tool Optimization with ANSYS optiSLang - Dynardo Gmbh · Machine Tool Optimization with...

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Webinar

Machine Tool Optimization with ANSYS optiSLang

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© Dynardo GmbH

Outline • Introduction

• Process Integration

• Design of Experiments & Sensitivity Analysis

• Multi-objective Optimization

• Single-objective Optimization

• Summary

Thomas Most Dynardo GmbH

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© Dynardo GmbH

Introduction to optiSLang

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• CAE-based virtual prototyping needs significant computational resources

• Physical phenomena may be coupled, non-linear and high dimensional

• Need to deal with failed designs (design creation, meshing or simulation may fail, license failure)

• Need to deal with many parameters (at least in the uncertainty domain)

Real world CAE-Applications

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optiSLang

• is an general purpose tool for variation analysis using CAE-based design sets (and/or data sets) for

the purpose of

• sensitivity analysis

• design/data exploration

• calibration of virtual models to tests

• optimization of product performance

• quantification of product robustness and product reliability

• Robust Design Optimization (RDO) and Design for Six Sigma (DFSS)

serves arbitrary CAX tools with support of process

integration, process automation and workflow

generation

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• optiSLang is an integration toolbox for

Process automation,

Design variation,

Sensitivity analysis,

Optimization,

Robustness evaluation,

Reliability analysis and

Robust design optimization (RDO)

• Functionality of stochastic analysis to

run real world CAE-based industrial

applications

Easy and safe to use:

• Predefined workflows,

• Algorithmic wizards and

• Robust default settings

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Excellence of optiSLang

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Example: Machine Tool Device

• Robotic system for drilling large

structural components

• Rating of robot kinematics and

machining quality

• Test device is used as a dummy

for large structural components

• High stiffness and light weight

(because of manual adjustment)

are required

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Fraunhofer IPA Stuttgart Germany

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Example: Optimization Task

Objective functions

• Minimization of mass

• Minimization of deformation

of the beam structure

for a positioning in

0°, 90° and 180°

Initial Design

• Mass: 207,2 kg

• Deformations:

• 0°-position: 0,12 mm

• 90°- position : 0,10 mm

• 180°- position : 0,07 mm

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Example: Simulation Model

• Three load cases in

ANSYS Workbench

• Deformations of 3 load cases

as outputs in parameter set

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Example: Optimization Parameters

• Thickness of upper plate (initial: 10 mm)

• Width, height and thickness of upper beams (50 x 50 x 3 mm³)

• Width, height and thickness of middle beams (40 x 40 x 3 mm³)

• Width, height and thickness of lower beams (70 x 70 x 4 mm³)

• Steel beam structure and upper plate is Aluminium

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© Dynardo GmbH

Process Integration

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© Dynardo GmbH

Process Integration Parametric model as base for

• User-defined optimization (design) space

• Naturally given robustness (random) space

Design variables Entities that define the design space

Response variables Outputs from the system

The CAE process Generates the results according to the inputs

Scattering variables Entities that define the robustness space

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optiSLang Integrations & Interfaces

Direct integrations ANSYS Workbench MATLAB Excel Python SimulationX

Supported connections ANSYS APDL Abaqus Adams AMESim LS-Dyna …

Arbitary connection of text-based solvers

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© Dynardo GmbH

ANSYS Workbench optiSLang Plugin • optiSLang modules connect directly to

ANSYS Workbench parameter set

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CAX-Interfaces – the ANSYS Workbench Node

• optiSLang Integrations provides the flexibility to extend the process chain

• ANSYS Workbench can be coupled with different other solvers

like MATLAB, SimulationX or Abaqus

• External geometry or mesh generators can work together with

the ANSYS Workbench node

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© Dynardo GmbH

Sensitivity Analysis

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Automatic workflow

with a minimum of solver runs to:

• identify the important parameters for each response

• Generate best possible metamodel (MOP) for each response

• understand and reduce the optimization task

• check solver and extraction noise

Sensitivity Analysis Understand the most important input variables!

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Solver

Sensitivity Evaluation

• Correlations • Variance-based quantification

Regression Methods

• 1D regression • nD polynomials

• Sophisticated metamodels

Design of Experiments

• Deterministic • (Quasi-)Random

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Sensitivity Analysis Flowchart

1. Design of Experiments generates a specific number of designs which are all evaluated by the solver

2. Regression methods approximate the solver responses to understand and to assess its behavior

3. The variable influence is quantified using the approximation functions

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Deterministic DoE

• Complex scheme required to detect multivariate dependencies

• Exponential growth with dimension

• Full factorial:

• Koshal linear:

Advanced Latin Hypercube Sampling

• Reduced sample size for statistical estimates

compared to plain Monte Carlo

• Reduces unwanted input correlation

How to Generate a Design of Experiments

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• Approximation of response variables as

explicit function of all input variables

• Approximation function can be used for

sensitivity analysis and/or optimization

• Global methods (Polynomial

regression, Neural Networks, …)

• Local methods (Spline interpolation,

Moving Least Squares, Radial Basis

Functions, Kriging, …)

• Approximation quality decreases with

increasing input dimension

• Successful application requires

objective measures of the

prognosis quality

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Response Surface Method

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Metamodel of Optimal Prognosis (MOP)

• Objective measure of prognosis quality

• Determination of relevant parameter subspace

• Determination of optimal approximation model

• Approximation of solver output by fast

surrogate model without over-fitting

• Evaluation of variable sensitivities

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The Sensitivity Wizard

• Drop the sensitivity wizard on the final solver chain

• Define the lower and upper bounds of the input variables

• The sampling method and sample number is recommended depending on the chosen solver runtime and number of parameters

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Example: Definition of Parameter Bounds

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Example: Design of Experiments

• 200 Latin Hypercube Samples

• 6 failed designs due to conflicting geometry parameters (red, violet)

• 16 designs with implausible model behavior (orange)

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Example: MOP Results

• All responses can be explained very well

• Mass is almost linear, deformations are nonlinear but monotonic

w.r.t. the input parameters

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Example: Influence of Parameters

• Thickness of upper plate is most

important for the mass

• Parameters of lower beam

sections have highest

influence on deformations

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© Dynardo GmbH

Multi-Objective Optimization

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Optimization

using MOP

Optimization Optimize your product design!

Start

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• Work with the reduced subset of only important parameters

• Pre-optimization on meta-model (one additional solver run)

• Optimization with cutting edge algorithms

• Decision tree for optimization algorithms

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© Dynardo GmbH

optiSLang Optimization Algorithms: concepts

Gradient-Based Methods

• Go downhill

• In the context of black-box solver gradient

needs to be measured via DoE schemes

Adaptive Response Surface Method

• DoE and surface fit

• Scanned area shifts and shrinks

Nature-Inspired Optimization

• Inspiration sources: evolution, swarm motion, thermodynamics, state insects

• Mutation, recombination, selection, propagation, … go beyond nature by

tthinking in operators

Start

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© Dynardo GmbH

optiSLang Optimization Algorithms: applications

Gradient-based Methods

• Clear favorite if objective function smooth and without local minima

• Repeated local search as strategy when there are few local minima

Adaptive Response Surface Method

• ARSM is the default choice

• Balance between robustness and efficiency

• Best if dimension<20

Nature inspired Optimization

• Recommended for global search

• Widest applicability (binary or discrete parameters, …)

• Realize optimization potential also in challenging situations

Start

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The Multi-Objective Optimization Task

• Several optimization criteria are

formulated in terms of the input

variables x

• Uncountable set of solutions, if

criteria are contradicting

Balanced compromise is wanted

Scan of the Pareto-front as decision base

• Constraint restrictions are possible

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© Dynardo GmbH

Decision Tree for Optimizer Selection

Recommendation of most suitable optimizer depending on

Number and type of input parameters (automatic)

Number of constraints and objectives (automatic)

Analysis status (user setting)

Amount of failed designs (user setting)

Solver noise (user setting)

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Example: Definition of Optimization Criteria

• Goal 1: Minimization of the mass (initial 207 kg)

• Goal 2: Minimization of maximum deformation

in 0°, 90° und 180° position (initial 0.12 mm)

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Example: Multi-Objective Optimization using MOP

• As compromise solution a maximum deformation of 0.1 mm is chosen

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Example: Optimization using the MOP

• Optimization goal: minimization of the mass

• Constraints: Deformations (0°, 90° und 180°) smaller as 0,1mm

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• Validated best design violates the constraints slightly

• Validated mass is larger as approximated

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Initial Design

• Mass: 207,2 kg

• Deformations:


• 90°- position : 0,097 mm

• 180°- position : 0,067 mm

Optimized Design

• Mass: 184,7 kg (-11%)

• Deformations:

• 0°- position : 0,106 mm (-12%)

• 90°- position : 0,087 mm

• 180°- position : 0,060 mm


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Example: Optimization using the FEM model

• ARSM optimizer using the previous optimum as start design

• Deformation constraints are fulfilled

• Mass is increased

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Initial Design

• Mass: 207,2 kg

• Deformations:


• 90°- position : 0,097 mm

• 180°- position : 0,067 mm

Optimized Design

• Mass: 188,3 kg (-9%)

• Deformations:

• 0°- position : 0,100 mm (-17%)

• 90°- position : 0,084 mm

• 180°- position : 0,055 mm

Example: Optimization using the FEM model

© Dynardo GmbH

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© Dynardo GmbH

• Sensitivity analysis helps to better

understand the physical phenomena and

to check or validate the simulation model

• Indification of important parameters helps

to significantly simplify and accelerate the

optimization task

• MOP-approximation can be used for fast

pre-optimization step or

multi-objective case studies

Summary

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Need more information?

Support &

trial license: [email protected]

Training: [email protected]

www.dynardo.de

Machine Tool Optimization with ANSYS optiSLang - Dynardo Gmbh · Machine Tool Optimization with...

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