Machining tool identification utilizing temporal 3D point clouds

Thanasis Zoumpekas (), Alexander Leutgeb (), Anna Puig () and Maria Salamó ()
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Thanasis Zoumpekas: University of Barcelona
Alexander Leutgeb: RISC Software GmbH
Anna Puig: University of Barcelona
Maria Salamó: University of Barcelona

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 3, No 15, 1232 pages

Abstract: Abstract The manufacturing domain is regarded as one of the most important engineering areas. Recently, smart manufacturing merges the use of sensors, intelligent controls, and software to manage each stage in the manufacturing lifecycle. Additionally, the increasing use of point clouds to model real products and machining tools in a virtual space facilitates the more accurate monitoring of the end-to-end production lifecycle. Thus, the conjunction of both, intelligent methods and more accurate 3D models allows the prediction of uncertainties and anomalies in the manufacturing process as well as reduces the final production costs. However, the high complexity of the geometrical structures defined by point clouds and the high accuracy required by the Quality Assurance/Quality control parameters during the process, pave the way for continuous improvements in smart manufacturing methods. This paper addresses a comprehensive analysis of machining tool identification utilizing temporal point cloud data. Specifically, we deal with the identification of machining tools from temporal 3D point clouds. To do that, we propose a process to construct and train intelligent models utilizing such data. Moreover, in our case study, we provide the research community with two labeled temporal 3D point cloud datasets, and we experiment with the pioneering PointNet neural network and three of its variants demonstrating an accuracy of 95% in the identification of the utilized machining tools in a machining process. Finally, we provide a prototype end-to-end intelligent system of machining tool identification.

Keywords: Deep learning; Point clouds; Manufacturing; Identification; Intelligent systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02093-5

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