VRM - Variation Response Method

VRM - Variation Response Method Application to Single- and Multi-Station Assembly Systems with Compliant non-Ideal Parts Main focus: Fixturing and Tooling Design Dr Pasquale Franciosa, Prof. Darek Ceglarek Manufacturing Systems & Digital Lifecycle Management Warwick Manufacturing Group (WMG), University of Warwick, Coventry, UK [email protected]

Warwick University - July 2018

Agenda Venue: WMG – IMC Building, room 105 (PC Lab) PART A: Background - 9.00 to 11.00 Introduction Short background Example of Applications PART B: Hands-on session - 11.00 to 12.00 & 13.00 to 14.00 Software: Fixture Analyser & Optimiser Applications: (1) Fixture design and optimization (2) Generation of “defect patterns” (3) Integration to ML (Machine Learning) modules PART C: Exercise - 14.00 to 17.00 Case study to be resolved with discussion and presentation of results Dr P. Franciosa, WMG, 27th July 2018

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PART A Background

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VRM – What is it? ..a bit of history… When: 2008 What: SVA-FEA (Statistical Variation Analysis – Finite Element Analysis) Application: Variation simulation of single- and multi-station assembly system with compliant parts (ideal parts only) Software platform: Matlab linked to MSC Nastran FEA processor When: 2010 What: FEMP (Finite Element Method & Programming) Application: Open-source FEM solver with capability to model thin and thick shell elements (applications to sheet-metal parts) Software platform: first release in Scilab and then translated to Matlab When: 2013 What: VRM 1.0 (Variation Response Method) Application: Modular simulation toolkit with capability to model and optimize (stochastic optimisation) assembly system with non-ideal compliant sheet-metal parts (single-stage assembly) Software platform: C++ with OpenMP and Matlab When: 2016 What: VRM 2.0 (Variation Response Method) Application: Integration of new modules: (a) robotics (task sequencing; path planning; collision checking); (b) laser weld simulation in keyhole & conduction mode Software platform: C++ with OpenMP and Matlab When: 2018 What: VRM 3.0 (Variation Response Method) Application: Integration of new modules: (a) thermal simulation; (b) multi-stage assembly simulation; (c) interface for Machine Learning Software platform: C++ with OpenMP and Matlab

Dr P. Franciosa, WMG, 27th July 2018

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VRM – What is it? ..focus for today… VRM 1.0 (Variation Response Method) Modular simulation toolkit with capability to model and optimize (stochastic optimisation) assembly system with non-ideal compliant sheet-metal parts (single-stage assembly) => Main focus: fixture and tooling optimization

Software platform: C++ with OpenMP and Matlab


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VRM – What is it? NPI process Concept design

Early design stage

Detailed design

Late design stage

Fabrication

Commissioning

… cascading of quality requirements: GD&T, cycle time, cost, etc.


VRM – What is it? ..focus for today… Part variation modelling

Fixture modelling and optimisation

Deviation [mm]

Deviation [mm]

Deviation [mm]

Why do we need to optimize fixture design (i.e., no. of clamps, location of clamps, etc.)? Dr P. Franciosa, WMG, 27th July 2018

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VRM-Principles Example: fixture design process… Objective: to determine optimum fixture layout at

(part placement – non-ideal parts)

early design stage for assembly systems of sheet-metal parts; by considering dimensional/geometrical quality, joint quality, accessibility of robotic arm and cycle time requirements

Challenges:

(clamp closing)

(1) complex non-linear relationship between input process parameters and output key performance indicators (1) heterogeneous and coupled design models

I. II. III. IV.

Part variation (non-ideal parts) Clamping Welding Robot path

(welding)

robot path Infeasible clamp location

(2) lack of integration of variation stochastic model with capability to expand current CAD tools to emulate real product variations (non-ideal product) at earlystage design Dr P. Franciosa, WMG, 27th July 2018

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VRM-Principles Example: fixture design process… Part form erorr(1)

Clamp layout(1)

“which clamp layout gives the smaller part-to-part gap at joint level?” … Clamp layout(M)

joint clamp

joint


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VRM-Principles Example: fixture design process… Part form erorr(N)

Clamp layout(1)

“which clamp layout gives the smaller part-to-part gap at joint level?” … Clamp layout(M)

joint clamp

??

joint


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VRM-Principles Example: fixture design process… Part form erorr(1)

Part form erorr(N) …

Clamp layout(1)

Clamp layout(1) …

…

… Clamp layout(M)

Clamp layout(M) …

joint clamp

joint

o Product/process uncertainty (i.e., variation) impact final quality performance o Can we optimise the quality performance for given product/process variation?


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Non-ideal assembly process

Product performance

VRM-Principles “to maximize the strength of a mechanical joint or to maximize the dimensional quality of the assembly”

-Deterministic assembly process -Maximum (case by case) performance

-Stochastic assembly process -Maximum capability (max probability of satisfying the design constraints)

Design constraints Response function (deterministic)


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Assembly variables


Product performance




Design constraints Response function (deterministic)


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Assembly variables


Product performance




Assembly variables Design constraints Assembly response function (deterministic)


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Product performance

VRM-Principles

Assembly variables Design constraints Assembly response function (deterministic)

• •

•

To introduce the concept of fixture capability Fixture capability represents the capability of the fixture to deliver quality requirements under given product and process variations Fixture capability is a measure of the probability that a given fixture satisfies product quality requirements during production


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VRM-Principles Conceptual approach

ψ KPIs  Deterministic KPIs   ξ KPIs  Stochastic

ψ KCCs  Deterministic KCCs   ξ KCCs  Stochastic

The dimensional quality of a manufactured product is evaluated by its key performance indicators (KPIs), which are delivered by key control characteristics (KCCs). Design constraints (DCs) in terms of lower (DCL) and upper (DCU) allowance limits (as defined by quality and design specifications) are defined for each KPIs and KCCs.

Let  and  be the stochastic and deterministic parameters, respectively. Stochastic parameters are imputed to manufacturing errors. Deterministic parameters are the result of design decisions (e.g. placing and parametrising clamps) and are assumed independent of manufacturing errors. Dr P. Franciosa, WMG, 27th July 2018

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VRM-Principles Conceptual approach  KPI  Fi ( )  KCCs  , i  1,..., N KPI i

- Deterministic Response Function

 KPI  Fi ( )  KCCs , KCCs  , i  1,..., N KPI i

- Stochastic Response Function

Which approach could we use to estimate the “deterministic” response function? … what about the “stochastic” response function? Dr P. Franciosa, WMG, 27th July 2018

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VRM-Principles Conceptual approach  KPI  Fi ( )  KCCs , KCCs  , i  1,..., N KPI i


• Is Monte Carlo Simulation applicable here? • Do you see any limitations?


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• Is Monte Carlo Simulation applicable here? • Do you see any limitations? • Any alternative?


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• Is Monte Carlo Simulation applicable here? • Do you see any limitations? • Any alternative?

Fi

( )

N PC

 KCCs , KCCs     t ,i Pt  KCCs , i  1,..., N KPI t 0

(Approximation by Polynomial Chaos Expansion) Dr P. Franciosa, WMG, 27th July 2018

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VRM-Principles • How do we model stochastic variations for sheet metal parts? Morphing Mesh


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VRM-Principles • How do we model stochastic variations for sheet metal parts? Morphing Mesh – conceptual approach


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VRM-Principles • Let’s now compare Polynomial Chaos and Monte Carlo o No. of Monte Carlo samples=1000 o Max Pol Chaos expansion=12 o No. of stochastic variables=1

What do we notice?


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VRM-Applications Reference: Franciosa, P; Gerbino, S; Ceglarek, D; Fixture Capability Optimisation for Early-stage Design of Assembly System with Compliant Parts Using Nested Polynomial Chaos Expansion; Procedia CIRP; Volume 41, 2016, Pages 87-92

Aerospace Wing Assembly

 Operation: Riveting  Number of clamps: 3

 Number of rivets: 11  Requirement: 𝑔𝑎𝑝∈ [0, 0.5] 𝑚𝑚

Target: Larger gaps are to be avoided to reduce the spring-back force after the removal of the clamps Dr P. Franciosa, WMG, 27th July 2018

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VRM-Applications Reference: Franciosa et al., Fixture Design Synthesis for Assembly System with Compliant Non-ideal Sheet Metal Parts using Stochastic Fixture Capability Approach, Computer Aided Design, 2017 (under review)

Automotive Door System Benefits:    

Fixture design synthesis of assembly systems Fixture design tasks’ integration at early-stage design Robust fixture layout optimisation with non-ideal sheet-metal parts Novel concept of fixture capability as a representation of the most likely feasible design solutions

“Stochastic Fixture Capability” clamp[3] (mm)

Stochastic Fixture Capability allows for the estimation of a desired process fallout rate in the case of product quality failures or violation of process requirements during production, in the presence of stochastic variations as generating from real manufacturing process

 KCC

2

[mm]

clamp[1]  (mm) [mm] KCC1


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VRM-Applications Reference: Franciosa et al., Fixture Design Synthesis for Assembly System with Compliant Non-ideal Sheet Metal Parts using Stochastic Fixture Capability Approach, Computer Aided Design, 2017 (under review)

Automotive Door System

Target: Stochastic simulation as design review toolkit 0.7 0.6 0.5

1

Gap (mm)

0.4 0.3 0.2 0.1 0

6

6

7

8

8

9

9

9

9

9

9

9

9

9

9 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 13

-0.1

Effect of no. of clamp: no clamp in the mirror area =>excessive deformation/bending in Y


Effect of no. of clamp: Seam[11] exhibits most of the variation. However, it’s under the limit of 0.5 mm gap.

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VRM-Applications Reference: Glorieux, Emile; Franciosa, P; Ceglarek, D; End-Effector Design Optimisation and Multi-Robot Motion Planning for Handling Compliant Parts, Structural and Multi-disciplinary Optimistion, DOI 10.1007/s00158-017-1798-x, 2017

Stamping and material handling

Target: Co-adaptive optimisation

of the end-effectors’ structure with the robot motion planning

to obtain the highest productivity and to avoid excessive part deformations


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VRM-Applications Isolation and Detection of GD&T Patterns Quality patterns generated in stage (1) – and imputed to part variations mm

1

Some representative measured patterns

3

mm

…

ξ KCC 1

 

ξ KCC 1

Quality patterns propagated at stage (1) – and imputed to fixture variations mm

KPI S  2 

2

KPI S  2 


 Ns 

KPI M  2 

1

Multi-physics Variation Modeller T1.1 Initialise classifier matrix Repeat until Ns Repeat for each Stage Repeat for each KCCs T1.2 Generate random set of KCCs T1.3 Calculate quality patterns T1.4 Update classifier matrix next j next i next

…

1

KPI M  2 

 

1

KPI M  2 

 2

 3

Self-evolving Measurement System T2.1 Collect measurement data at stage k T2.2 Evaluate KPIs Multi-physics Defect Tracker T3.1 Train classification model T3.2 Solve classification model S ?

3

2

(N)

(Y)

T4 – Defect Pattern Correction Go to stage “i” and correct j-th KCC

4

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PART B Hands-on Session

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Fixture Analyser & Optimiser (1) MODEL INITIALISATION Load product data Load process data Load production CAD CAM data

Set KCCs parameters

Solver settings

(4) FIXTURE ANALYSIS

(5) FIXTURE SYNTHESIS

(4.1) What-if analysis

(5.1) Calculate Regression

(4.2) Plot results

(5.2) Solve optimisation problem


(6) EXPORT FIXTURE CONFIGURATION

AUTOMATIC CONFIGURATION

MANUAL CONFIGURATION

(2) SIMULATE ASSEMBLY

Fixture Analyser & Optimiser 3D renderer panel

Simulation builder

Setting panel

Log panel

2D renderer panel

Graphical Renderer Window


Options and Setting Window

Fixture Analyser & Optimiser

1

Import product data (i) (ii)

product geometry GD&T/ISO tolerance specifications

2

Import process data

(i) (ii)

Locator layout Joint layout

3

Analysis & Synthesis

4

Export configuration

Product data: CATIA, SolidWorks, Inventor, ProE, UG, etc. Process data can be handled by MS Excel© spreadsheets and formatted ASCII files 32 Dr P. Franciosa, WMG,

27th

July 2018

Fixture Analyser & Optimiser Short demo • http://www2.warwick.ac.uk/fac/sci/wmg/research/manufacturing/downloads/


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PART C Exercise

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Case Study

OBJECTIVES o to identify the position of the 4 clamps in such a way the part-to-part gap is between [0.0, 0.4] mm


Case Study


Thanks for your attention!

Any questions? Dr. Pasquale Franciosa Digital Lifecycle Management (DLM), WMG, University of Warwick, UK E-mail: [email protected] Websites: http://www2.warwick.ac.uk/fac/sci/wmg/research/manufacturing/