Automatic Characterization of WEDM Single Craters Through AI Based Object Detection
-
Published:2024-03-05
Issue:2
Volume:18
Page:265-275
-
ISSN:1883-8022
-
Container-title:International Journal of Automation Technology
-
language:en
-
Short-container-title:IJAT
Author:
Gonzalez-Sanchez Eduardo1, Saccardo Davide1ORCID, Esteves Paulo Borges1ORCID, Kuffa Michal1, Wegener Konrad1
Affiliation:
1. Eidgenössische Technische Hochschule (ETH) Zürich, Technoparkstrasse 1, Zürich 8005, Switzerland
Abstract
Wire electrical discharge machining (WEDM) is a process that removes material from conductive workpieces by using sequential electrical discharges. The morphology of the craters formed by these discharges is influenced by various process parameters and affects the quality and efficiency of the machining. To understand and optimize the WEDM process, it is essential to identify and characterize single craters from microscopy images. However, manual labeling of craters is tedious and prone to errors. This paper presents a novel approach to detect and segment single craters using state-of-the-art computer vision techniques. The YOLOv8 model, a convolutional neural network-based object detection technique, is fine-tuned on a custom dataset of WEDM craters to locate and enclose them with tight bounding boxes. The segment anything model, a vision transformer-based instance segmentation technique, is applied to the cropped images of individual craters to delineate their shape and size. Geometric analysis of the segmented craters reveals significant variations in their contour and area depending on the energy setting, while the wire diameter has minimal influence.
Publisher
Fuji Technology Press Ltd.
Reference19 articles.
1. P. Esteves, M. Sikora, M. Kuffa, and K. Wegener, “Single crater dimensions and wire diameter influence on Wire-EDM,” Procedia CIRP, Vol.113, pp. 232-237, 2022. https://doi.org/10.1016/j.procir.2022.09.151 2. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen, “Recent advances in convolutional neural networks,” Pattern Recognition, Vol.77, pp. 354-377, 2018. https://doi.org/10.1016/j.patcog.2017.10.013 3. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Proc. of the 31st Int. Conf. on Neural Information Processing Systems (NIPS’17), pp. 6000-6010, 2017. 4. H. Matsumoto, Y. Mori, and H. Masuda, “Extraction of guardrails from MMS data using convolutional neural network,” Int. J. Automation Technol., Vol.15, No.3, pp. 258-267, 2021. https://doi.org/10.20965/ijat.2021.p0258 5. S. Yamane and K. Matsuo, “Gap detection using convolutional neural network and adaptive control in robotic plasma welding,” Int. J. Automation Technol., Vol.13, No.6, pp. 796-802, 2019. https://doi.org/10.20965/ijat.2019.p0796
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|