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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Original
Published: 2024-06-11
DOI: https://doi.org/10.56294/sctconf2024859

Automatic Machining Setup via Deep Learning and Image Processing

By
Weam A. Al-khaleeli ,
Weam A. Al-khaleeli

Department of Production Engineering and Metallurgy, The University of Technology, Iraq

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Mohanned M. H. AL-Khafaji ,
Mohanned M. H. AL-Khafaji

Department of Production Engineering and Metallurgy, The University of Technology, Iraq

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Mazin Al-wswasi ,
Mazin Al-wswasi

Department of Production Engineering and Metallurgy, The University of Technology, Iraq

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Abstract

Computer Numerical Control (CNC) machines are widely used in different processes, such as milling, turning, drilling, etc., due to their high accuracy, rapidity, and repeatability. While these machines are fully controlled using G-code, the manual setup between the cutting tools and the initial stock can be time-consuming and requires skilled and experienced operators. This study utilizes artificial intelligence, supported by Deep Learning and image processing techniques, to automatically set up the machine by computing the distance between the tool and the workpiece. Firstly, a You Only Look Once (YOLO V4) algorithm has been developed via MATLAB programming specifically for the recognition of tools and workpieces. This algorithm has been trained using 1700 images, which are captured by a Rapoo C260 Webam, in the machine configuration environment for both the tools and workpieces. After recognizing the tool and workpiece, the algorithm provides information in terms of coordinates to specify where these objects are located within the image by drawing bounding boxes around them. Because the edges of the bounding boxes do not accurately depict the actual edges of the tool or the workpiece, the implementation of image processing techniques is necessary to correct these differences and determine the precise distance between the tool and the workpiece. Finally, an automatic G-code correction is generated to adjust the existing G-code, resulting in an automatic machining setup. The proposed methodology has been implemented and evaluated on a CNC turning machine, and it showed promising results in terms of reducing the required machining setup time.

Keywords:

Machining setup,
Deep learning,
YOLO V4,
Image processing,

How to Cite

1.
Al-khaleeli WA, H. AL-Khafaji MMH, Al-wswasi M. Automatic Machining Setup via Deep Learning and Image Processing. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jun. 11 [cited 2024 Jul. 1];3:859. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/859

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 3 (2024)
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