Estimation of Mass and Lengths of Sintered Workpieces Using Machine Learning Models

Powder metallurgy (PM) is the branch of metallurgy that deals with the design/production of near-net-shaped sintered workpieces with different shapes and characteristics. The produced sintered workpieces are used in the automotive, aviation, and aerospace industries, just to name a few. The quality of the produced sintered workpieces largely depends on powder compaction techniques and the accurate adjustments of process parameters. Currently, adjustments of these process parameters are done manually and thus resulting in laborious and time-intensive effort. To this end, this article explores the use of machine learning (ML) in the compaction process and proposes an accurate and lightweight ML-based pipeline to estimate the quality characteristics (QCs) of the produced workpieces in the PM domain. More specifically, it presents a pipeline for workpiece's mass and lengths estimation by exploiting some novel hand-crafted features and comparing well-selected ML prediction models, namely, random forest (RF), AdaBoost (ADA), and gradient boosting (GB). The chosen models are trained on a combination of features extracted from environmental and sensory raw data to estimate the mass and lengths of the next produced workpiece. We have implemented and evaluated our scheme on a dataset collected in a real production environment and we have found that GB is the most consistent and accurate one with the lowest root-mean-squared error (approximate to 0.0886%). The results of extensive experimentation have proven the relevance of the selected features and the accuracy of GB.