Development of a health monitoring and diagnosis framework for fused deposition modeling process based on a machine learning algorithm

Abstract

In this article, a data-driven approach is applied to develop a health monitoring and diagnosis framework for a fused deposition modeling process based on a machine learning algorithm. For the data-driven approach, three accelerometers, an acoustic emission sensor, and three thermocouples are installed, and associated data are collected from those sensors. The collected data are processed to obtain root mean square values, and they are used for constructing health monitoring and diagnosis models for the fused deposition modeling process based on a support vector machine algorithm, which is one of machine learning algorithms. Among various root mean square values, those of acceleration data from the frame were most effective for diagnosing health states of the fused deposition modeling process with the non-linear support vector machine–based model.