National Institute of Standards and Technology (NIST) Advanced Manufacturing Technology Consortia (AMTech) Program Award Number: 70NANB14H056 Development of Roadmap and Consortium for Innovation in Sheet Metal Forming Challenge Code Group 3 Project Titles 3.1 Ultrasonic-Assisted Forming for Process Enhancement 3.3 Electrically-Assisted Forming for Enhancing Formability and Joinability 2.6 Stochastic Modeling and Prediction for Improved Data Mining

2.6 Stochastic Modeling and Prediction for Data Mining Objective: To improve effectiveness, efficiency, and robustness in production anomaly detection, pattern recognition, and product quality prediction by: Integrating process-embedded in-situ sensing with physics-based process models that are established based on prior knowledge Treating sensor measurement data in the Bayesian framework to handle uncertainty from sensing and process variations Exploring Particle Filter (PF) as a stochastic tool for big data mining under nonlinearity and non-Gaussianity P. Wang & R. Gao, “Adaptive resampling-based particle filtering for tool life prediction”, JMS, 2015  UNH - 28 October 2016 Sheet Metal Forming

2.6 Stochastic Modeling and Prediction for Data Mining Project Benefits: Effective tracking of both gradual and abrupt changes in data streams measured by sensors during sheet forming process Improved process observability for equipment operation and quality control, leading to reduced scraps and increased cost saving Technical Benefits: Innovation in sheet metal forming process monitoring and control, by leveraging distributed and parallel computing capability enabled by the cloud infrastructure Improved computational efficiency and robustness as compared to existing methods Advanced data mining through efficient pre-processing of measured sensor data to facilitate feature extraction and pattern recognition Effective human-machine interface for communication with different users groups (machine operators, process experts, decision makers), expediently and reliably UNH - 28 October 2016 Sheet Metal Forming

2.6 Stochastic Modeling and Prediction for Data Mining Technical and Non-Technical Obstacles: Availability of process-embedded sensors that capture process physics comprehensively and reliably, with high signal-to-noise (S/N) ratio Access to sensor data through proper data sharing policies in the community Consistency in sensor data, through standard data formalization protocol and process UNH - 28 October 2016 Sheet Metal Forming

2.6 Stochastic Modeling and Prediction for Data Mining Path to Implementation: A hybrid stochastic modeling approach combining data-driven and model-based techniques. Research will consider: effects of machine settings in the sheet metal forming process effects of material properties effects of maintenance actions Uncertainty caused by sensor measurements, performance degradation, maintenance actions, etc., will be treated probabilistically, with final evaluation as the integration of multiple probability distributions Potential research partners: prior collaboration between Northwestern University (J. Cao), Case Western Reserve University (R. Gao), and GM on model-augmented and multi-physics sensing and stamping process characterization S. Sah, N. Mahayotsanun, M. Peshkin, J. Cao, and R. Gao, “Pressure and draw-in maps for stamping process monitoring”, ASME Journal of Manufacturing Science and Engineering, 2016 UNH - 28 October 2016 Sheet Metal Forming

2.6 Stochastic Modeling and Prediction for Data Mining Discussion: Timing: priority, resources available when needed? Obstacles: technical & business? Resources: level, type & issues? Anything missing for active industry support? Estimated benefits realistic & complete? Technical and non-technical obstacles complete or over reaching? Alternative implementation paths or better approaches? Conflicts with intellectual property or trade secrets? UNH - 28 October 2016 Sheet Metal Forming