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

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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.

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