DATA DRIVEN INTEGRATED DESIGN SPACE EXPLORATION USING iSOM

Design preferences or targets are typically available at system level. A designer is usually interested in understanding patches of design space at component levels, across different stages and process that corresponds to such system targets or preferences. This demands a thorough design space exploration permitting both forward and inverse designs. Such exploration becomes cumbersome with large variables and complex systems with conflicting goals. Current work proposes using interpretable self organising map (iSOM) as a visual enabler of decision support system for exploring designs to understand the trade off in system targets/goals. The novelty lies in being able to use a visual form to compare greater than three conflicting goals simultaneously while accounting for design variables. The proposed approach is demonstrated using two test problems: i) hot rolling and cooling process chain design for the production of steel rods and ii) head and neck-injury risk evaluations for vehicular crashworthiness. Using the first problem, we demonstrate the capability of iSOM for the solution space exploration of a many-objective steel manufacturing process chain problem to realize the design of a steel product, in the context of a compromise Decision Support Problem formulation. In the second problem, we demonstrate the capability of iSOM for early-stage design space exploration to identify critical injury-risk regions of interest for different car crash scenarios. These two case studies illustrate the capability to carry out a forward and inverse design, by the proposed approach.