State space model for online monitoring selective laser melting process using data mining techniques

Abstract

Selective laser melting (SLM) is one the most popular additive manufacturing technologies due to its ability to produce the complex parts. Online monitoring of the SLM process has been considered to be an effective approach to ensuring the safety of operations during the build and improve the part quality. Current researches largely focus on investigating the relationship between a single factor (such as temperature) and the process quality. In this paper, a systematic methodology for online monitoring the SLM process is a proposed, taking into consideration the relationships between multiple factors during melting, and their impact on the status of the devices and the parts being created. A framework of SLM process monitoring based on state space model is demonstrated, providing an integrated data structure for introducing data mining methods into SLM process online monitoring. The development of the state space model together with the parameters selection is described. [Submitted 12 February 2017; Accepted 21 November 2017]