Process Optimization Through Discovery and Integration of Materials and Processing Properties

The objective is to increase the productivity of materials research by automating the infrastructure of such efforts. Global competition in manufacturing, limitations in fiscal and physical resources, ecological constraints, and technically challenging mission goals require that productivity be increased in the design, fabrication, and utilization of new or improved materials. The technical approach is based on unified learning and interpretation of combined process and product composition data in terms of resulting product properties. The approach builds on the results of two successful prior technical efforts. One is the CAD/Chem methodology by AI WARE, Inc. for exploring relationships between product formulation and product properties. CAD/Chem is based on neural-net computing and evolutionary programming. Another contributing methodology is the QPA work (US Patent #5032525) relating process sensor data, process control actions and product properties. The result of this work will be a methodology for building material models for interpolation and extrapolation from existing experimental data. The models will be accurate and useful because they will realistically incorporate process information instead of just nominal compositions; structure will be included in the model implicitly and explicitly if feasible. The models can be used to predict and discover. OPTION TASK: None submitted.