2026-04-17T17:05:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1967832025-12-05T09:57:33Zcom_2072_1057col_2072_478917col_2072_478920

00925njm 22002777a 4500 dc Zoumpekas, Thanasis author Leutgeb, Alexander author Puig Puig, Anna author Salamó Llorente, Maria author 2023-04-14T08:32:35Z 2023-04-14T08:32:35Z 2023-03-25 2023-04-14T08:32:35Z 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. Aprenentatge automàtic Fabricació Xarxes neuronals (Informàtica) Machine learning Manufacturing processes Neural networks (Computer science) Machining tool identification utilizing temporal 3D point clouds