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Metal additive manufacturing is gaining broad interest and increased use inthe industrial and academic fields. However, the quantification andcommercialization of standard parts usually require extensive experiments andexpensive post-characterization, which impedes the rapid development andadaptation of metal AM technologies. In this work, a similarity-basedacceleration (S-acceleration) method for design of experiments is developed toreduce the time and costs associated with unveiling process-property (porositydefects) relationships during manufacturing. With S-acceleration, part semanticfeatures from machine-setting parameters and physics-effects informedcharacteristics are explored for measuring mutual part similarities. Auser-defined simplification rate of experiments is proposed to purposely removeredundant parts before conducting experiments printing without sacrificinginformation gain as original full factorial experiment design. ThisS-acceleration design of experiments is demonstrated on a Concept Laser M2machine for the experimental plan of modeling relationships between processparameters and part porosity defects. The printed part has 2 mm diameter by 4mm tall pin geometry considering variations in build location and orientation,laser settings and powder feedstock are held constant. In total, 242 parts aremeasured to create a ground truth data set of porosity levels by using X-raytomography microscopy. The S-acceleration method is assessed for performanceconsidering 40%, 50%, and 60% of user-defined experiment simplification rates.The repeated experiments are removed without ignoring the minority experimentsoutlier, assuring a similar process-property relation in the originalexperiment plan. The experiment number is significantly reduced based on partsimilarity with minimal compromise of model accuracy and obtained knowledge.

Unsupervised Learning of Part Similarity for Goal-Guided Accelerated Experiment Design in Metal Additive Manufacturing