1. ADAPTIVE-MODEL-BASED OPTIMIZATION AND CONTROL OF AUTOMOTIVE PAINT SPRAY Jia Li and Yinlun Huang Department of Chemical Engineering and Materials Science Wayne State University Paint Spray Process Characters  Large scale:  90 measured points on vehicle panel  Hundreds of variables and parameters  Non-linear relationship  Big disturbance in process  Time-varying parameters Research Focus  Objectives  To Improve topcoat topology  To achieve higher transfer efficiency  Development  Modular neural network models  On-line adaptive learning algorithm  Integrated optimization method  Model-based control system ABSTRACT Paint spray is vital to the quality of vehicle coating, particularly the topcoat. To Improve the topcoat topology with higher transfer efficiency, a family of tools have been developed for modeling, optimization, and control. A group of neural network based data-driven models are developed to characterize the nonlinear spray operation. The modeling method consists of three components: (i) a distribution matrix that is for dividing the spray system into a number of subsystems based on vehicle body geometry, (ii) a data preprocessing approach that is based on cluster analysis for minimizing large disturbances in training data, and (iii) a model adaptive approach with sample selection so that the model parameters can be undated on-line. The NN-based spray models are embedded into an optimization and control system for proactive quality control of topcoat application. Extensive simulation shows that the topcoat topology can be improved by 10% in terms of film uniformity, and the transfer efficiency can increased by 5% ~ 30% through optimization. A computer-aided tool has been used in Ford. Methodologies in Data-Driven Modeling  Prior knowledge based fluid flowrate distribution matrix  Neural network models trained by Bayesian regularization  Major disturbance detection based on fuzzy C-means cluster  Model adaptation with the sample selection algorithm Zone-based NN Zone-based flow rate Downdraft_b Downdraft_r Temperature Humidity Avg. film thickness FF Distributio n Matrix Zone-based FF NN model 1 NN model 2 NN model n T, H, DDb DDr FF setpoits Avg. thickness for each zone Spray Process Training Data Collection Diagram Neural Network Model Structure Disturbance Detection  Generate clusters in training data set  Calculate distance between data and cluster centers  Pick up data when they are distinct from clusters Disturbance Data Detection Sample Select Algorithm  Objective: enlarging input data space with recent samples  Evaluating factors: spatial and temporal distributions  Methodology: Sample Distance Matrix Control Law  Fit trendline on Thickness-FF curve in the range of FF(k)±ΔFF max  Calculate ΔFF using the slope of trendline and feedback Δthickness  Add dead zone and constraints to make control action feasible Model Validation Model-based Optimization  Objective  Min. thickness deviation  Max. transfer efficiency  Expression J – objective function α – weight [0, 1] Ý– predictive thickness Y s – thickness setpoint () Y FF YY s DDHTFF ˆ 1) ˆ ( JMin 2,,, aa-+-= Simulation Results FB Thickness Transfer Efficiency Model-based Control  Feedback information: Thickness, DD_b, DD_r, T, H  Thickness-FF diagram generation Based on NN model and fixing the other 4 inputs, a thickness- FF diagram can be generated Thickness-FF diagram generation DD controller DD for Booth FF controller FF for Bell Paint Spray Process Controller based on NN Optimization Modular System Diagram including Optimization and Control Contributions  Data-driven modeling for paint spray  FF distribution matrix for process decoupling  Cluster analysis for distribution detection  Sample selection algorithm for model adaptation  Model-based optimization of spray parameters  Model-based control system design Application  MATLAB based programming  Functions: case selection, training data generated from Excel file, sample selection, modeling, raw data shown, 3-D shown, single prediction, validation, optimization, optimal result shown