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Published VersionCC BY V4.0, Open Access
Journal article
Automated quality control inspection of geometric tip defects in medical needle manufacturing
International Journal of Advanced Manufacturing Technology, Vol.123(7-8), pp.2371-2384
2022
Abstract
The manufacture of In Vitro Fertilization (IVF) needles is subject to the most stringent quality demands. This makes automated inspection challenging due to difficulty in reliably classifying conforming and non conforming (defective) products due to factors including multidimensional variation of their tip geometry and the lack of an explicit quality standard. In addition, developing an IVF needle image dataset, which broadly contains the visual characteristics of qualified and defective products, is difficult without commissioning large and costly production runs. The most important original contribution of this work is a new solution to investigate and quantify the uncertainty in the quality standard of IVF needles by integrating inter disciplinary techniques. This work utilizes a low cost, virtual dataset of synthetic images, generated by the automated photo realistic rendering of a three dimensional (3D) parametric model to simulate manufacturing variation. Then, the unknown numerical (critical) quality thresholds are obtained by estimating the relationship between quality response and measurement predictors using an Ordinal Logistic Regression (OLR) algorithm on the synthetic images. The fitted models exhibited increased overall predictive accuracy of up to 11.02% than the machine learning models (available in MATLAB) and could provide objective guidance on classifying specific quality aspects of a product.
Details
- Title
- Automated quality control inspection of geometric tip defects in medical needle manufacturing
- Authors
- Xiaoying Wang (Corresponding Author) - RMIT UniversityCasey Jowers (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringMaciej Mazur (Author) - RMIT UniversityAlexander Buddery (Author) - The University of QueenslandDamon Kent (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringAlireza Bab-Hadiashar (Author) - RMIT UniversityMark Easton (Author) - RMIT University
- Publication details
- International Journal of Advanced Manufacturing Technology, Vol.123(7-8), pp.2371-2384
- Publisher
- Springer UK
- Date published
- 2022
- DOI
- 10.1007/s00170-022-10249-5
- ISSN
- 1433-3015; 0268-3768
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99687998302621
- Output Type
- Journal article
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web Of Science research areas
- Automation & Control Systems
- Engineering, Manufacturing