A Data Driven Approach to Attaining 100% Automatic Quality Assurance David Kazmer Univ. Mass. Lowell 06-Apr-06

Agenda Introduction Experimental Data Feature Generation Data Feature Selection Data Feature Validation Conclusions

Modern Manufacturing

Standard Molder #Operators#Machines#Eng/MgtEnergy Use “Lights Out” Molder

“Lights Out” Molding Methodology Mold commissioning –Process characterization –Process capability validation –Process optimization Lights out injection molding –Automatic materials/parts handling –Automatic process monitoring –Automatic quality assurance –Automatic process correction & model adaptation       

Mold Commissioning Lights Out Molding Automatic Quality Assurance Process monitored Data features, E: –Verify process settings, X –Estimate part quality, Y X: Machine Settings Y: Part Qualities E: Data Features

Agenda Introduction Experimental Data Feature Generation Data Feature Selection Data Feature Validation Conclusions

Molding Machine & Instrumentation Electra 50Ton Machine Machine sensors –Ram position/velocity –Nozzle pressure –Nozzle steel thermocouple –Intrusive melt thermocouple Mold sensors –4 Piezoelectric pressure sensors –2 Unshielded cavity thermocouples Priamus EDaq data acquisition

Experimentation Electra injection molding machine Instrumented mold –2 temperature sensors –4 pressure transducers Priamus eDAQ data acquisition system

Ram Position Trace Four stages –Initial delay –Filling stage –Packing stage –Cooling stage Auto-ID –Mold closed –1% of ram displacement –Maximum filling pressure –Maximum ram displacement –Mold opening DELAYFILLINGPACKINGCOOLING & PLASTICATION MOLD OPEN

Ram Velocity Trace Derivative of ram position Filtering –Filtered forward & backward in time domain –3 rd order Butterworth filter 100 Hz cut-off frequency

Pressure Traces Locations –Machine nozzle –Runner (bottom of sprue) –Gates of Impact bar Tensile specimen Stepped plaque Purpose –Melt arrival, velocity, viscosity, and shrinkage

Cavity Temperature Traces Unshielded thermocouples End of cavities: –Impact bar –Tensile specimen Purpose: –Mold temperature –Arrival of melt –Melt temperature

Nozzle Temperature Traces Type J Thermocouple Locations –Nozzle steel –1/8” into 3/8” melt channel Purpose: –Melt temperature entering mold

Additional Traces Velocity vs. position –Total shot size –Velocity averages/steps –Kick back Estimated clamp tonnage – –Possible mold opening

Agenda Introduction Experimental Data Feature Generation Data Feature Selection Data Feature Validation Conclusions

Data Feature Generation Purpose: –Condense time-varying traces into a set of representative single point data –One set of data features per cycle s t 10,000 data points across cycle per channel ~14 features across 3 stages per channel

Data Feature 1: Average s t FILLINGPACKINGRECOVERY Simple aggregate measure of process signal

Data Feature 2: Maximum Measure of peak signal s t FILLINGPACKINGRECOVERY

Data Feature 3: Minimum Measure of minimum signal s t FILLINGPACKINGRECOVERY

Data Feature 4: Range Measure of total change in signal s t FILLINGPACKINGRECOVERY

Data Feature 5: Derivative w.r.t. time, ds/dt Measure of average rate of change with respect to time s t FILLINGPACKINGRECOVERY

Data Feature 6: Derivative w.r.t. position, ds/dx Measure of average rate of change with respect to injected volume of plastic s t FILLINGPACKINGRECOVERY

Data Feature 7: Integral w.r.t. time,  s dt Measure of cumulative “energy” of signal with respect to time s t FILLINGPACKINGRECOVERY

Data Feature 8: Integral w.r.t. position,  s dx Measure of cumulative “energy” of signal with respect to position s x FILLINGPACKINGRECOVERY

Data Feature 9: Slope at start w.r.t. time, ds/dt Measure of process dynamic with respect to time at start of stage s t FILLINGPACKINGRECOVERY

Data Feature 10: Slope at start w.r.t. position, ds/dx Measure of process dynamic with respect to injected volume of material at start of stage s x FILLINGPACKINGRECOVERY

Data Feature 11: Slope at end w.r.t. time, ds/dt Measure of process dynamic with respect to time at end of stage s t FILLINGPACKINGRECOVERY

Data Feature 12: Slope at end w.r.t. position, ds/dx Measure of process dynamic with respect to injected volume of material at end of stage s x FILLINGPACKINGRECOVERY

Data Feature 13: Curvature w.r.t. time, d 2 s/dt 2 Measure of changing process dynamics with respect to time across stage s t FILLINGPACKINGRECOVERY

Data Feature 14: Curvature w.r.t. position, d 2 s/dx 2 Measure of changing process dynamics with respect to injected volume of material across stage s x FILLINGPACKINGRECOVERY

Data Feature Summary Number of data features per cycle: But given this multitude of data features: –Which are statistically significant? –Which are indicative of part quality? –What set provides “good” observability of: Machine settings Material properties Environmental conditions Mold/machine states

Agenda Introduction Experimental Data Feature Generation Data Feature Selection –Multi-Variate Data Analysis Data Feature Validation Conclusions

Approach: Multi-Variate Data Analysis (MVDA) Relate –Independent variables (X, machine settings) To –Dependent variables (E, data features) And Relate –Independent variables (E, data features) To –Dependent variables (Y, part qualities) Mold Commissioning X: Machine Settings Lights Out Molding Y: Part Qualities E: Data Features

Design of Experiments Investigate significant machine settings –Velocity –Shot Size –Pack Pressure –Barrel Temp –Mold Temp –Screw RPM –Back Pressure –Pack Time –Cooling Time –Mold Delay –Material Resulting DOE – Partial factorial design –Resolution IV design Main effects not confounded with second order interactions –10 parts/run –340 moldings Quality measurements –Thicknesses –Lengths –Mass –Short shot –Flash

MVDA: X  E Example: Ram Velocity Regress data features to ram velocity –Identify features with highest correlation

MVDA: X  E Example: Ram Velocity F1-3-2: Maximum ram velocity during filling stage –Excellent correlation, R 2 =0.9999

MVDA: X  E Example: Melt Temperature Regress data features to melt temperature –Identify features with highest correlation

MVDA: X  E Example: Melt Temperature F1-7-3: Minimum melt thermocouple temperature during filling Varies with barrel temp, plast pressure, RPM, velocity

MVDA Question: Which Is Better? Y as a function of X? –Conduct DOE & use X as independent variables –Machine is in control –If machine settings are known, quality can be predicted Y as a function of E? –Conduct DOE & use E as independent variables –Machine is not perfect –Quality may be better predicted with data closer to the mold X: Machine Settings Y: Part Qualities E: Data Features Y: Part Qualities

MVDA: Model Comparison R 2 : fraction of behavior captured by model DOE based models are not as predictive as data feature based models Sensors Add Value

MVDA: Y=f(X) Predict Quality from Machine Settings Effect of machine settings on flash

MVDA: Y=f(E) Predict Quality from Data Features Effect of data features on flash

MVDA: Y=f(E) Importance of Data Features Many different sensors are important –Need to gain ‘observability’ of process Estimators Pressures Mold Temp’s Times Machine Temp’s Velocities Positions

Agenda Introduction Experimental Data Feature Generation Data Feature Selection Data Feature Validation Conclusions

“Blind” Validation Ten molding trials conducted under random conditions –Velocity, Shot Size, Pack Pressure, Barrel Temp, Mold Temp, Screw RPM, Back Pressure, Pack Time, Cooling Time, Mold Open Delay –Materials Low MFI High MFI Mixed Low & High MFI These process conditions and resulting moldings were not known prior to the following predictions:

Predictions: Machine Settings from Data Trial 2, Part 7 Trial 9, Part 7

Predictions: Machine Settings from Data Ram velocity –Very good correlation –Good velocity control Material MFI –Good correlation –Prediction doesn’t include effect of melt temperature  T melt

Predictions: Part Quality from Data Lightest Molding Trial 1, Run 7 Heaviest Molding Trial 7, Run 7

Predictions: Part Quality from Data Part mass –Good correlation Quantitative offset Qualitatively correct Flash thickness –Fair correlation Quantitative offset error Qualitatively useful  T melt

Agenda Introduction Experimental Data Feature Generation Data Feature Selection Data Feature Validation Conclusions

Conclusions: Current State of Molding Technology “Scientific molding” is increasingly common, but not fully leveraged Processes are not fully optimized –Trade-off between multiple qualities & cost Automatic quality control is not yet realized –Simple diagnostics but not on-line automatic quality assurance –No automatic diagnosis & correction

Conclusions: Summary of Presented Work Data features defined as state estimators Design of experiments & MVDA used to: –Characterize & optimize process –Relate data features to: Process settings Part qualities Blind validation shown: –Excellent prediction of process settings –Good prediction of part qualities Process SPC better than Machine SPC

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