A novel image feature descriptor for SLM spattering pattern classification using a consumable camera
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ORIGINAL ARTICLE
A novel image feature descriptor for SLM spattering pattern classification using a consumable camera Ze Ji1
· Quanquan Han2
Received: 28 February 2020 / Accepted: 24 August 2020 / Published online: 18 September 2020 © The Author(s) 2020
Abstract In selective laser melting (SLM), spattering is an important phenomenon that is highly related to the quality of the manufactured parts. Characterisation and monitoring of spattering behaviours are highly valuable in understanding the manufacturing process and improving the manufacturing quality of SLM. This paper introduces a method of automatic visual classification to distinguish spattering characteristics of SLM processes in different manufacturing conditions. A compact feature descriptor is proposed to represent spattering patterns and its effectiveness is evaluated using real images captured in different conditions. The feature descriptor of this work combines information of spatter trajectory morphology, spatial distributions, and temporal information. The classification is performed using support vector machine (SVM) and random forests for testing and shows highly promising classification accuracy of about 97%. The advantages of this work include compactness for representation and semantic interpretability with the feature description. In addition, the qualities of manufacturing parts are mapped with spattering characteristics under different laser energy densities. Such a map table can be then used to define the desired spatter features, providing a non-contact monitoring solution for online anomaly detection. This work will lead to a further integration of real-time vision monitoring system for an online closed-loop prognostic system for SLM systems, in order to improve the performance in terms of manufacturing quality, power consumption, and fault detection. Keywords Selective laser melting · Spattering · Computer vision · Machine learning
1 Introduction Additive manufacturing (AM) technologies, such as selective laser melting (SLM), adopt a layer-by-layer manufacturing principle to fabricate metallic components in a broad range of sectors [17, 18, 20, 45]. Despite the increasing attention from both academia and industrial fields, several challenges have represented a barrier for the breakthrough of SLM technology; one of the most significant challenges
Ze Ji
[email protected] Quanquan Han
[email protected] 1
School of Engineering, Cardiff University, Queen’s buildings, The Parade, Cardiff, CF24 3AA, United Kingdom
2
Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, China
is the quality control of the laser-material interaction [13, 28, 32, 35]. Job failures and defects such as porosity and cracks could take place and can be only detected after the manufacturing process. All control parameters, such as laser power or scan patterns, are pre-determined empirically. The work will need to be repeated with different contr
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