Predicting Quality of Castings via Supervised Learning Method

Abstract

The process input data which materials processing operations can collect for each unit of production is extensive. Large datasets have long been difficult to work with as computing power to execute analysis in a timely fashion was unavailable. Further, the great velocity at which the data is generated makes near real-time decision-making unwieldy without a new set of tools with which to do the work. When troubleshooting by a small dataset, such as the last few hours of production, observations made on the measured parameters can be misleading. Machine learning is opening doors to high-dimensional data analysis in material processing. In this work, high-pressure die-casting (HPDC) is explored as an exemplar of high-volume materials processing. HPDC process summary data from a full year of production data covering over 950,000 machine cycles is analyzed via supervised machine learning methods to successfully model the prediction of good parts and process scrap as determined by the die-casting machine. Additionally, the prediction of ultimate tensile strength via a classification method of extracted tensile bars is performed and the important features identified are discussed. Supervised learning is found to be a useful tool for materials processing applications.