FIA Technology Roadmap Workshop Sensors and Controls Tzyy-Shuh Chang, Ph.D. OG Technologies, Inc. Ann Arbor, MI 48103 August 14-15, 2001 1
About Smartsmith A Project Sponsored by Advanced Technology Program National Institute of Standards and Technology and State of Ohio Target applications: forging and rolling processes Goals: To develop in- line sensing device for real- time production information To develop methods/processes to best utilize the information To improve OEE, yield and profitability 2
Industrial Participation 3
Scope Data Acquisition System Focus on hot-state dimensions and surface quality Deal with distortions caused by heat Thermo- mechanically Induced Geometric Error Estimator Model the states of forged parts Compare the measured/estimated dimensions to CAD model Predictive Process Control System Develop an observer for production information Tie the production information for predictive process control 4
Use of Smartsmith Blank for machining Ti, Steel Super alloy ±? V, Volume Material porperties Sensor Deployment Information Induction heating or furnace 1500-2300 F ± 10 F Forging of part ±? d, Deflection of press Hot Data Acquisition Hot information Trim Table cool Cool down conditions Production (process and product) Information Geometric Error Estimation Predicted Final Product Information Predictive Process Control Higher OEE, Better yield, More Profits 5
Data Acquisition, 1 HotEye Technology Imaging based Dimensional measurements at high temperature Surface quality inspection at high temperature Challenges Excessive heat on sensor survivability/performance Optical distortion from radiation/natural convection Excessive vibrations Debris Lub drops Scale covering 6
Data Acquisition, 2 Capable of dimensions measurements such as closure, diameters, bends, TIR/mismatches, etc. Capable of surface qualities such as underfills, pin marks, tool wear marks, etc. Designed and protected to work in forging shops 7
Data Acquisition, 3 Pilot installation in a forging site Gauge R&R at 10%, no special fixturing / no vibration insulation for the part 10 to 15 categories can be distinguished over a range of 1 mm standard deviation of measurements at 0.030 mm (0.0012 ) Conditions: > 1,200ºC ~ 1G Debris Scale Maintenance free 8
Geometric Error Estimator, 1 Cooling Process Information DAS Info. (Hot part surface Temp. and Dimensions) IDE (Internal Distribution Estimator) GVE (Geometrical Variance Estimator) GC (Geometric Comparator) Dimensional Error Cold part dimensions TIG Dimensional Specifications PPCS 9
Geometric Error Estimator, 2 Flange Diameter 98.9 Diameter (mm) 98.8 98.7 98.6 98.5 98.4 1 4 7 10 13 16 19 22 25 Sampling interval (10 sec) 28 31 34 Main bearing Diameter 74.3 74.2 Diameter (mm) 74.1 74 73.9 73.8 73.7 73.6 73.5 1 4 7 10 13 16 19 22 25 28 31 34 Sampling interval (10 sec) 10
Geometric Error Estimator, 3 Flow Stress f (?,?, T, S ) FEM/FDM Metal Flow and Heat Transfer Microstructural evolution (S) Recrystallization Grain growth Phase transformation 11
Geometric Error Estimator, 4 Verification of Geometric Conformity Human review/comparison Software Comparing to a Golden Part Comparing to a CAD solid (Picture provided by SDRC) Geometric Comparator is a New Solution! 12
Predictive Process Control, 1 Tasks Conventional SPC PPC (1) Process faults (2) Product defects Detection Inspection Detection, isolation and Identification Prevention (3) Problem Resolution Off-line operator intervention Systematic Approach Expand the use of sensors from equipment control / protection product quality verification To have complete quality and process control with in-line monitoring of the interaction between equipment and parts 13
Predictive Process Control, 2 Where and how many sensors ( deployment) What information is in the sensor signals ( observability) How to apply the information for healthy production ( controllability) 14
Predictive Process Control, 3 operator intervention Statistical Process Control SPC Controller Machines and Processes Product Measurement sensors Statistical analysis Action/Compensation Fusion Knowledge Base Engineering Model 15
Predictive Process Control, 4 Tonnage Process Variables (material, die, blank interactive, and press variables) Blank Measurement (Tonnage, shut height, punch speed, vibration, die temperature, acoustic emission) Press Die 0 0 120 160 200 240 280 Crank Angle Part Stamped Part (part variation) 16
Predictive Process Control, 5 Process Variable Measurement tonnage (ton) 400 Loose Tie Rod 350 300 Worn Bearing Tie Rod Stamping Press 250 200 150 100 Worn Gib Excessive Snap Bearing 50 0 Punch Speed Tonnage Sensors Linkage Gib Slide -50 120 140 160 180 200 220 240 crank angle (degree) Billet Shut Height Upright Die Engineering: Statistics: Press/Die Mechanics, Forming Theory, Working Stage Segmentation SPC, DOE, Wavelets, Classification, Pattern Recognition 17
Predictive Process Control, 6 18
The SmartSmith Hot Data Acquisition Information Geometric Error Estimation Predictive Process Control Higher OEE, Better yield, More Profits 19
Vision of the Future Less experience dependent Faster change over Less waste Less variability Linked and Integrated Taking Information to Technology Your Production Line 20
From Here to There OG Technologies and our partners are committed to work with the forging industry 21