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Applied Sciences
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Applications of Vision Measurement System on Product Quality Control
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Applications of Vision Measurement System on Product Quality Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1418

Special Issue Editor

Prof. Dr. Andrzej Sioma

E-Mail Website
Guest Editor
Department of Process Automation, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: vision system; imaging methods; quality control; measurements; production automation; manufacturing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vision systems are employed in industry in an extensive range of control and measurement tasks, enabling the implementation of the comprehensive quality assessment of product parameters. They have become part of quality assurance systems and, at the same time, are a source of data regarding the products and processes employed in accounting and management systems for the entire production line. One of the most significant challenges faced by vision systems is the performance of measurement tasks. The vision measurement system that operates on the production line is exposed to various types of disturbances present on the production line. The presence of vibrations, gas vapors, changes in the ambient temperature, variations in the lighting conditions and a wide variety of process changes affect the measurement's repeatability and uncertainty.

Simultaneously, there have been dynamic advances made regarding vision measurements and the available imaging methods, sensor arrays, lighting techniques, optics and measurement algorithms. 

This Special Issue encourages the contribution of articles that present the application of vision systems in measurement tasks performed in order to control product parameters. Preference is given to works depicting the application of various imaging methods, image calibration and techniques employed to verify the quality of visual post-marc systems in industrial conditions. Presenting the possibilities of applying image analysis in a wide range of measurement tasks carried out as part of product quality control is vital from the perspective of improving product performance quality and minimizing production losses. The main purpose of this Special Issue is to present the scientific achievements of the authors submitting their work to this journal in order to provide innovations in a changing industrial environment.

Prof. Dr. Andrzej Sioma
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vision system
  • imaging methods
  • image analysis, quality control
  • measurements
  • production automation
  • manufacturing systems

Published Papers (3 papers)

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Research

15 pages, 1033 KiB  
Article
Training of a Neural Network System in the Task of Detecting Blue Stains in a Sawmill Wood Inspection System
by Piotr Wolszczak, Grzegorz Kotnarowski, Arkadiusz Małek and Grzegorz Litak
Appl. Sci. 2024, 14(9), 3885; https://0-doi-org.brum.beds.ac.uk/10.3390/app14093885 - 01 May 2024
Viewed by 262
Abstract
This article presents the operation of an automatic pine sawn timber inspection system, which was developed at the Woodinspector company and is offered commercially. The vision inspection system is used to detect various wood defects, including knots, blue stain, and mechanical damage caused [...] Read more.
This article presents the operation of an automatic pine sawn timber inspection system, which was developed at the Woodinspector company and is offered commercially. The vision inspection system is used to detect various wood defects, including knots, blue stain, and mechanical damage caused by worms. A blue stain is a defect that is difficult to detect based on the color of the wood, because it can be easily confused with wood defects or dirt that do not impair its strength properties. In particular, the issues of detecting blue stain in wood, the use of artificial neural networks, and improving the operation of the system in production conditions are discussed in this article. While training the network, 400 boards, 4 m long, and their cross-sections of 100 × 25 [mm] were used and photographed using special scanners with laser illuminators from four sides. The test stages were carried out during an 8-hour workday at a sawmill (8224 m of material was scanned) on material with an average of 10% blue stain (every 10th board has more than 30% of its length stained blue). The final learning error was assessed based on defective boards detected by humans after the automatic selection stage. The system error for 5387 boards, 550 m long, which had blue staining that was not detected by the scanner (clean) was 0.4% (25 pieces from 5387), and 0.1 % in the case of 3412 boards, 610 mm long, on which there were no blue stains, but were wrongly classified (blue stain). For 6491 finger-joint boards (180–400 mm), 48 pieces were classified as class 1 (clean), but had a blue stain (48/6491 = 0.7%), and 28 pieces did not have a blue stain, but were classified as class 2 (28/3561 = 0.7%). Full article
(This article belongs to the Special Issue Applications of Vision Measurement System on Product Quality Control)
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16 pages, 14912 KiB  
Article
Application of 3D Imaging for Analyzing the Chip Groove Shapes of Cutting Inserts
by Grzegorz Struzikiewicz
Appl. Sci. 2024, 14(7), 3134; https://0-doi-org.brum.beds.ac.uk/10.3390/app14073134 - 08 Apr 2024
Viewed by 416
Abstract
An effective chip formation process is significant for an efficient metal-cutting process. Long continuous chips can lead to scratches on the machined surface, increasing the risk to operator safety and stability of the machining process. The use of chip grooves on cutting inserts [...] Read more.
An effective chip formation process is significant for an efficient metal-cutting process. Long continuous chips can lead to scratches on the machined surface, increasing the risk to operator safety and stability of the machining process. The use of chip grooves on cutting inserts allows for control of the chip formation and breaking process during machining. The shape of the rake surface and the design of the chip groove also affect the efficiency of the machining process. The article presents the use of 3D imaging to analyze changes in the selected chip groove shapes depending on the cutting depth ap = 0.10, 0.25, and 0.50 mm and the angular location of the cutting insert relative to the machined surface of the workpiece (i.e., major cutting-edge angle K = 60° and K = 90°). The analysis methodology was based on the use of 3D image registration and surface shape modeling. In the analysis based on the 3D imaging presented, the novelty was the adaptation of methods typically used to map and model the terrain surface, which have not been used previously in cutting processes. The evaluation of the shape of the chip groove surface was carried out using, e.g., watershed maps and 3D surface maps. The obtained results indicated a significant influence of the cutting depth and major cutting-edge angle on the surface shape, profile, and length of the chip former; chip groove volume; and the theoretical contact area of the formed chip with the cutting insert. It was observed that for small depths of cut, i.e., ap < 0.25 mm, the chip-curling process may be difficult due to the flattened shape of the rake surface. In addition, the influence of the convexity of the rake surface of the cutting insert on the chip formation process was demonstrated. The results of the experimental research that verified the conclusions are presented. The developed results may be useful in the process of selecting the parameters and conditions of the metal finishing through use of tools with a shaped rake surface. Full article
(This article belongs to the Special Issue Applications of Vision Measurement System on Product Quality Control)
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18 pages, 2347 KiB  
Article
A Methodology for Advanced Manufacturing Defect Detection through Self-Supervised Learning on X-ray Images
by Eneko Intxausti, Danijel Skočaj, Carlos Cernuda and Ekhi Zugasti
Appl. Sci. 2024, 14(7), 2785; https://0-doi-org.brum.beds.ac.uk/10.3390/app14072785 - 26 Mar 2024
Viewed by 407
Abstract
In industrial quality control, especially in the field of manufacturing defect detection, deep learning plays an increasingly critical role. However, the efficacy of these advanced models is often hindered by their need for large-scale, annotated datasets. Moreover, these datasets are mainly based on [...] Read more.
In industrial quality control, especially in the field of manufacturing defect detection, deep learning plays an increasingly critical role. However, the efficacy of these advanced models is often hindered by their need for large-scale, annotated datasets. Moreover, these datasets are mainly based on RGB images, which are very different from X-ray images. Addressing this limitation, our research proposes a methodology that incorporates domain-specific self-supervised pretraining techniques using X-ray imaging to improve defect detection capabilities in manufacturing products. We employ two pretraining approaches, SimSiam and SimMIM, to refine feature extraction from manufacturing images. The pretraining stage is carried out using an industrial dataset of 27,901 unlabeled X-ray images from a manufacturing production line. We analyze the performance of the pretraining against transfer-learning-based methods in a complex defect detection scenario using a Faster R-CNN model. We conduct evaluations on both a proprietary industrial dataset and the publicly available GDXray dataset. The findings reveal that models pretrained with domain-specific X-ray images consistently outperform those initialized with ImageNet weights. Notably, Swin Transformer models show superior results in scenarios rich in labeled data, whereas CNN backbones are more effective in limited-data environments. Moreover, we underscore the enhanced ability of the models pretrained with X-ray images in detecting critical defects, crucial for ensuring safety in industrial settings. Our study offers substantial evidence of the benefits of self-supervised learning in manufacturing defect detection, providing a solid foundation for further research and practical applications in industrial quality control. Full article
(This article belongs to the Special Issue Applications of Vision Measurement System on Product Quality Control)
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