2. Industry 4.0: novel sensors, control algorithms, and servo-presses Presenter: Prof. Wei Chen, Northwestern PIs: Wei Chen, Jian Cao, Northwestern Brad Kinsey, UNH

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Industrial Needs and Relevance: Identify metal forming uncertainties Parameter variations in materials, tooling and processes Imperfect characterization Compensate sheet metal springback Increase of the material formability Control material property / microstructure Increase the process flexibility Variations in product properties Problems in sheet metal forming Lim et al., 2008 NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Industrial Needs and Relevance: Novel sensors measure forces, material draw-in, and strain history Control algorithms to follow prescribed critical trajectory and achieve accurate feedforward and feedback controls Efficient and flexible actuators to achieve controlled motion Allwood et al, CIRP, 2016 NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Project Objectives: Identifying and characterizing uncertainties in metal forming system: Model errors Disturbances Identifying effective actuators, sensors and control algorithms Integrating data analytics with sensing data for improved product properties NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Approach/Methodologies: Identify various actuators and sensors for implementation of control process Transducer to detect draw-in distance Mahayotsanun, et al, 2005 Segmented die with local adaptive controller Cao et al, 2001 NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Approach/Methodologies: Develop a test component with critical features Collect data on material, process, tooling and products for analysis; Use data analytics tools for developing effective process control algorithms. Actuators for control in deep drawing Allwood et al, 2016 NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Deep Models Recurrent Neural Networks Reinforcement Learning Approach/Methodologies: Use state-of-the-art machine learning techniques to extract features based on prior manufacturing knowledge Train (deep) Machine Learning models Iteratively adjust feature space to achieve high predictive accuracy Verify on validation dataset x1 h1 y x2 h2 Prediction x3 Hidden States Variables Shallow Models ARIMA (Autoregressive integrated moving average ARIMA – Autoregressive integrated moving average NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses 𝐱 𝐲 Experiment 𝛉 Simulator Approach/Methodologies: Employ Bayesian model calibration and uncertainty quantification approach. Identify unknown model parameters based on physical data Correct model bias (model error) in predicting product properties Experiment Model Experimental Error Bias Function Inputs Low-fidelity model Tests / High-fidelity model Prediction mean 95% PI (prediction interval) NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Approach/Methodologies: Develop an adaptive Bayesian interface framework Integrate data from off-line physics-based models, machine learning models, and on-line sensors Design sensor locations and adaptive sampling strategies Adaptive Bayesian interface framework for additive manufacturing process (DMDII sponsored project) NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Deliverables: Robust (validated) uncertainty prediction models for critical properties Scientific characterization of non-linear features of forming process Comprehensive analysis of the effect of different actuations and control algorithms Potential control example of deep drawing Polyblank et al, 2008 NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Budget and Timeline: Estimated cost of project is $500K for three years. Task / Milestone Year 1 Year 2 Year 3 Q1 Q2 Q3 Q4 Test Specimen Standardization   Novel Sensor Identification Flexible Actuator Identification Access Control Algorithm Process Uncertainty Modeling Achieve Desired Process Control NSF I/UCRC Planning Meeting

2. Industry 4.0: novel sensors, control algorithms, and servo-presses Discussions: Are the industrial needs and relevance accurately captured? Are the objectives realistic and complete? Are the approaches technically sound and appropriate? Are there alternative implementation paths or better approaches? Are the deliverables impactful to industrial partners? Are the budget and timeline reasonable? Are there conflicts with intellectual property or trade secrets? NSF I/UCRC Planning Meeting