| Issue |
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
|
|
|---|---|---|
| Article Number | 02009 | |
| Number of page(s) | 15 | |
| Section | Machine Learning / Deep Learning | |
| DOI | https://doi.org/10.1051/itmconf/20235302009 | |
| Published online | 01 June 2023 | |
Vision-Based Quality Control Check of Tube Shaft using DNN Architecture
1 Computer Science, KLE Technological University, Hubballi, Vidyanagar, 580031, Karnataka, India.
2 Dana Anand India Private Limited, Dharwad, Karnataka, India
* Corresponding Author: shreyadevagiri16@gmail.com
Quality control is the process of ensuring that a product or service meets certain predetermined standards of quality. This can involve testing, inspection, and other methods to ensure that the product or service is fit for its intended use. The tube shaft is a component used in the drive shaft of a vehicle. It undergoes several stages from raw material to final product to increase its structural properties. Following the first step, which is hardening, preliminary quality control is done by cutting the tube shaft into two parts lengthwise to check the intensity of hardening and decide whether to accept or reject the part. We present a machine vision-based quality control system that uses You Only Look Once (YOLO) v5 to assess hardening intensity by analyzing the pattern formed on the cut piece’s surface.
© The Authors, published by EDP Sciences, 2023
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