Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process

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Published Dec 21, 2022
DOI https://doi.org/10.3221/IGF-ESIS.63.18

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Akshansh Mishra
School of Industrial and Information Engineering, Politecnico di Milano, Milan, Italy
https://orcid.org/0000-0003-4939-359X Vijaykumar S Jatti
Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, India
https://orcid.org/0000-0001-7949-2551 Nitin K Khedkar
Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
Rahul B. Dhabale
Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
Ashwini V Jatti
Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India

Abstract

A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated by testing with higher accuracy.

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    Issue
    Vol. 17 No. 63 (2023): January 2023
    Section
    Fracture

    How to Cite

    Mishra, A. (2022) “Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process”, Frattura ed Integrità Strutturale, 17(63), pp. 234–245. doi: 10.3221/IGF-ESIS.63.18.
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