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

TL;DR

This paper introduces an unsupervised similarity-based method to accelerate experiment design in metal additive manufacturing, reducing costs and time while maintaining accuracy in modeling process-property relationships.

Contribution

It develops a novel S-acceleration approach that uses part similarity to remove redundant experiments, optimizing the design of experiments in metal AM.

Findings

Significant reduction in experiment count with minimal loss of information.

Maintains model accuracy despite removing up to 60% of experiments.

Effective identification of redundant parts based on semantic and physics-informed features.

Abstract

Metal additive manufacturing is gaining broad interest and increased use in the industrial and academic fields. However, the quantification and commercialization of standard parts usually require extensive experiments and expensive post-characterization, which impedes the rapid development and adaptation of metal AM technologies. In this work, a similarity-based acceleration (S-acceleration) method for design of experiments is developed to reduce the time and costs associated with unveiling process-property (porosity defects) relationships during manufacturing. With S-acceleration, part semantic features from machine-setting parameters and physics-effects informed characteristics are explored for measuring mutual part similarities. A user-defined simplification rate of experiments is proposed to purposely remove redundant parts before conducting experiments printing without sacrificing information gain as original full factorial experiment design. This S-acceleration design of experiments is demonstrated on a Concept Laser M2 machine for the experimental plan of modeling relationships between process parameters and part porosity defects. The printed part has 2 mm diameter by 4 mm tall pin geometry considering variations in build location and orientation, laser settings and powder feedstock are held constant. In total, 242 parts are measured to create a ground truth data set of porosity levels by using X-ray tomography microscopy. The S-acceleration method is assessed for performance considering 40%, 50%, and 60% of user-defined experiment simplification rates. The repeated experiments are removed without ignoring the minority experiments outlier, assuring a similar process-property relation in the original experiment plan. The experiment number is significantly reduced based on part similarity with minimal compromise of model accuracy and obtained knowledge.