Fault diagnosis of FDM process based on support vector machine (SVM)

Abstract

Purpose

This paper aims to propose a method to diagnose fused deposition modeling (FDM) printing faults caused by the variation of temperature field and establish a fault knowledge base, which helps to study the generation mechanism of FDM printing faults.

Design/methodology/approach

Based on the Spearman rank correlation analysis, four relative temperature parameters are selected as the input data to train the SVM-based multi-classes classification model, which further serves as a method to diagnose the FDM printing faults.

Findings

It is found that FDM parts may be in several printing states with the variation of temperature field on the surface of FDM parts. The theoretical dividing lines between different FDM printing states are put forward by traversing all the four-dimensional input parameter combinations. The relationship between the relative mean temperature and the theoretical dividing lines is found to be close and is analyzed qualitatively.

Originality/value

The multi-classes classification model, embedded in FDM printers as an adviser, can be used to prevent waste products and release much work of labors for monitoring.

Citation

Hu, H., He, K., Zhong, T. and Hong, Y. (2020), "Fault diagnosis of FDM process based on support vector machine (SVM)", Rapid Prototyping Journal, Vol. 26 No. 2, pp. 330-348. https://doi.org/10.1108/RPJ-05-2019-0121