Automatic Characterization of WEDM Single Craters Through AI Based Object Detection
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 in-fluenced by various process parameters and affects the quality and efficiency of the machining. To understand and optimize the WEDM process, it is essential to iden-tify 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 set-ting, while the wire diameter has minimal influence. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000666146Publication status
publishedExternal links
Journal / series
International Journal of Automation TechnologyVolume
Pages / Article No.
Publisher
Fuji Technology PressSubject
wire electric discharge machining; machine learning; image processing; computer vision; instance segmentationMore
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