Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing

TL;DR

This paper introduces a machine learning framework called differential property classification (DPC) that helps experimental design in advanced manufacturing by predicting which process parameters yield better material properties, reducing reliance on trial-and-error.

Contribution

The paper presents DPC, a novel ML-based classification approach that simplifies experimental design by predicting better process parameters for desired material properties.

Findings

DPC accurately predicts superior process parameters in manufacturing.

Reframes property prediction as a classification problem for better ML performance.

Demonstrated on AA7075 tube manufacturing with successful results.

Abstract

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.