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  • 1

    Webinar

    Machine Tool Optimization with ANSYS optiSLang

  • 2

    Machine Tool Optimization with ANSYS optiSLang

    © Dynardo GmbH

    Outline • Introduction

    • Process Integration

    • Design of Experiments & Sensitivity Analysis

    • Multi-objective Optimization

    • Single-objective Optimization

    • Summary

    Thomas Most Dynardo GmbH

  • 3

    Machine Tool Optimization with ANSYS optiSLang

    © Dynardo GmbH

    Introduction to optiSLang

  • 4

    Machine Tool Optimization with ANSYS optiSLang

    • 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

    © Dynardo GmbH

  • 5

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 6

    Machine Tool Optimization with ANSYS optiSLang

    • 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

    © Dynardo GmbH

    Excellence of optiSLang

  • 7

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

    Fraunhofer IPA Stuttgart Germany

  • 8

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 9

    Machine Tool Optimization with ANSYS optiSLang

    Example: Simulation Model

    • Three load cases in

    ANSYS Workbench

    • Deformations of 3 load cases

    as outputs in parameter set

    © Dynardo GmbH

  • 10

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 11

    Machine Tool Optimization with ANSYS optiSLang

    © Dynardo GmbH

    Process Integration

  • 12

    Machine Tool Optimization with ANSYS optiSLang

    © 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

  • 13

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 14

    Machine Tool Optimization with ANSYS optiSLang

    © Dynardo GmbH

    ANSYS Workbench optiSLang Plugin • optiSLang modules connect directly to

    ANSYS Workbench parameter set

  • 15

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 16

    Machine Tool Optimization with ANSYS optiSLang

    © Dynardo GmbH

    Sensitivity Analysis

  • 17

    Machine Tool Optimization with ANSYS optiSLang

    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!

    © Dynardo GmbH

  • 18

    Machine Tool Optimization with ANSYS optiSLang

    Solver

    Sensitivity Evaluation

    • Correlations • Variance-based quantification

    Regression Methods

    • 1D regression • nD polynomials

    • Sophisticated metamodels

    Design of Experiments

    • Deterministic • (Quasi-)Random

    © Dynardo GmbH

    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

  • 19

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 20

    Machine Tool Optimization with ANSYS optiSLang

    • 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

    © Dynardo GmbH

    Response Surface Method

  • 21

    Machine Tool Optimization with ANSYS optiSLang

    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

    © Dynardo GmbH

  • 22

    Machine Tool Optimization with ANSYS optiSLang

    © Dynardo GmbH

    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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