Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
This addresses the cost and time inefficiencies in experimental design for materials scientists and engineers, though it is incremental as it reframes an existing regression task into classification.
The paper tackles the problem of inefficient trial-and-error experimental design in advanced manufacturing by proposing a machine learning framework called differential property classification (DPC), which reframes property prediction as a classification task to choose between parameter sets, and demonstrates its success on AA7075 tube manufacturing with ShAPE technology, achieving good performance.
Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.