Automated Industrial PTFE Billet Sintering Profile Optimization

Transcription

1 Automated Industrial PTFE Billet Sintering Profile Optimization Hansong Huang a, Xiangbin Yu b May, 2012 a Saint-Gobain Northboro R&D Center, US b Saint-Gobain Research Shanghai, China

2 Saint-Gobain, one of the world s TOP 100 industrial groups Operations in 64 countries About 190,000 employees 15 R&D centers 361 patents in Bn Sales 40.1 Operating income 3.1 Recurring net income 1.3 Capital expenditure 1.4 Cash flow from operations 3.0 2

3 Distribution by Business Sector 25% 43% Building Distribution Construction Products * #1 in Europe #1 worldwide 9% Verallia (Packaging) 23% #1 in Europe #2 worldwide Innovative Materials Glass #1 in Europe - #2 worldwide High Performance Materials (HPM) #1 worldwide World or European LEADER 3

4 HPM: Sales Breakdown by Business Ceramic Materials 37% 18% Performance Plastics 11% Saint-Gobain Adfors 34% Abrasives Sales 4.1 Billion 4

5 6 large R&D multi-business centers, many dedicated centers and local teams 5

6 PTFE Billet Sintering PTFE powder Compact into billet De-mold Sinter Machine -> parts skive -> film Because of high melt viscosity, PTFE parts are often machined from sintered billet Residual stresses are of concern in billets for Quality control deformation, cracking Dimension stability for subsequent fabrication process

7 Agenda FEA model for residual stress prediction Optimize temperature profile for residual stress reduction Workflow automation, encapsulation, and publication through webtop

8 Motivation: Democratize Simulation Democratize: to make (something) available to all people Democratize Simulation: to make simulation available to all engineers Advanced simulation/optimization are only accessible in central R&D from dedicated analysts knowledge and skills can never be substituted by software software cost, IT cost, computation power. «Democratization» are limited not by technology but by practicality and cost: operation cost: Even for developed/validated models, special skills dedicated analysts -- still required to use them properly. availablility: Engineers scattered across the global, need 24x7 acess and fast turn-around Solution: encapsulation and remote execution

9 Thermo-Mechanical Material Property Green Body Melt (>350 C) Crystallization ( C) Elasticity Temp. Dependent Young s modulus (*) Plasticity CTE Melt/crystallization volume changes Thermal conductivity Specific heat Elastic Perfect plasticity s y = 0.01 MPa s y = f ( temp.) (*) Cool Down (< 300C ) Measured in-house on small billet in dilatometer (two directions) Measured in-house on small billet in dilatometer (two directions) Always considered at fully relaxed condition due to slow temp. ramp rate From (*), temp. dependent Measured in-house with DSC * Andena, L.; Rink, M., Simulation of PTFE Sintering: Thermal Stresses and Deformation Behavior Billet Axial CTE Billet Radial CTE

10 FEA Model External convection H=40 Internal convection H=20 Uniform ambient temperature following sintering profile Hot Air Axi-symmetric model with uniform surface thermal condition Industrial sinter ovens are specifically designed to maximize temp. uniformity Surface heat-transfer is not critical due to the large resistance of PTFE

11 Temperature (ºC) Optimization Setup Heat up Sinter Cool down Objective: Single - minimize max residual stress for a given total cycle time Multi - minimize max residual stress AND total cycle time Variables : coordinates of control points in the cool down segments of the profile Constraints: Sinter stage: all locations in the billet should at above 365 C for > 2 hrs Cool down stage: monotonically decreasing temperature

12 Optimization Results Initial attempt of manual optimization 1-2 variables at a time Isight Optimization algorithms yielded lower stress at shorter cycle time

13 Optimization Algorithms Single Objective Evol: 1.55 MPa Hooke-Jeeves: 1.95 MPa Min. stress for given total cycle time Direct/Exploratory Algr. Many local optimal Large optm. Variables Pointer: 1.59MPa DownhillSimplex: 1.68MPa Pointer is effective, but convergence not reliable Downhill Simplex converges fast and reliably

14 DownhillSimplex: best objective function = 2.2 (Stress=1.1MPa, Cycle time=97 hrs) Optimization Algorithms Multi-Objective Residual Stress Cycle Time Min. stress and cycle time 0.1 Mpa stress ~ 1 hr time Pointer: best objective function = 2.5 (Stress=1.5MPa, Cycle time=88 hrs) Pointer is not as effective in multi-obj. Residual Stress Cycle Time Downhill Simplex converges fast and reliably

15 Isight Automation and Encapsulation A B C D C-1 C-2

16 Isight Automation and Encapsulation Building Blocks A B C D Input Output C-1 C-2 Stress Calculation Input/Output via Excel template Auto results to user Encapsulate in components for easy maintenance

17 Isight Automation and Encapsulation Workflow Reference cycle Optimize Sinter Time Optimize Cool down To realize true automation, necessary to intelligently terminate optimization loop A script was used to terminate loop if no better solution is obtained in x iterations

18 Remote Execution SEE & WebTop A well prepared Isight workflow is converted/published to SEE/webtop in minutes Hosted in corporate central R&D, accessible globally within intranet Users only exchange information through Excel file via website and s

19 Temp. Must Not Decrease Temp. Must Not Increase Time Must Monotonically Increase Temperature (C) Remote Execution -- Interface PTFE Billet Sinter Cycle Optimization Request/Result Form version 1 to send r hansong.huang@saint-gobain.com Part # Max Cycle Time: 120 (hrs) Optimize Cycle Time? 1 Size: OD_Radius (m) Out of Oven Temperature: 21 (C ) Weight of Time 1 ID_Radius (m) PTFE sinter Temperature: 355 (C ) Weight of Stress 1 Height (m) PTFE Sinter Duration: 2 (hrs) Optimization Algorithm Parameters Optimization Termination Cycle 5 Original Cycle Optimized Cycle Optimization Termination Threshold 0.1 Time Temp Time Temp (hr) (C ) (hr) (C ) Original Cycle Optimized Cycle Time (hrs) Max Stress (Mpa) Max Stress (Mpa)

20 Summary Explore the approach to make adv. simulation available to all engineers at low operation cost and high availability CAD integrated solver is not the answer for Saint-Gobain Encapsulation of complex optimization/simulation into black-box Robustness, design space limit/check, friendly user interface, up-front effort Publish through webtop for remote execution Available at anytime to all Saint-Gobain engineers at no operation (human) cost Centrally maintained and upgraded