Geometric error modeling of the contact probe in a three-dimensional screw thread measuring machine

Yu, Jae Sik; Yao, Yang; Cai, Jun; Li, Dongsheng

doi:10.1016/j.measurement.2022.111026

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…Detection methods can be broadly classified into two categories: contact and non-contact. Contact methods encompass techniques such as thread gauge detection (qualitative), measuring needle detection (including two-needle and three-needle methods), thread micrometer inspection (single inspection), sample inspection, three-coordinate measurement [ 1 ], and contact scanning. Although these methods tend to be more accurate, they require skilled inspectors using specialized tools for precise operation, leading to higher labor costs and lower efficiency.…”

Section: Introductionmentioning

confidence: 99%

Internal thread defect detection system based on multi-vision

Dou,

Xue,

Zhang

et al. 2024

PLoS ONE

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In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.

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Section: Introductionmentioning

confidence: 99%