IJAT Vol.18 No.2 pp. 265-275
doi: 10.20965/ijat.2024.p0265

Research Paper:

Automatic Characterization of WEDM Single Craters Through AI Based Object Detection

Eduardo Gonzalez-Sanchez, Davide Saccardo ORCID Icon, Paulo Borges Esteves ORCID Icon, Michal Kuffa, and Konrad Wegener

Eidgenössische Technische Hochschule (ETH) Zürich
Technoparkstrasse 1, Zürich 8005, Switzerland

Corresponding author

June 15, 2023
November 7, 2023
March 5, 2024
wire electric discharge machining, machine learning, image processing, computer vision, instance segmentation

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.

Cite this article as:
E. Gonzalez-Sanchez, D. Saccardo, P. Esteves, M. Kuffa, and K. Wegener, “Automatic Characterization of WEDM Single Craters Through AI Based Object Detection,” Int. J. Automation Technol., Vol.18 No.2, pp. 265-275, 2024.
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Last updated on Jul. 12, 2024