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Image Analysis for Product Quality Control
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Image Analysis for 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: closed (20 November 2022) | Viewed by 15763

Special Issue Editors

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
Dr. Grzegorz Struzikiewicz

E-Mail Website
Guest Editor
Production Engineering Institute, Mechanical Faculty, Cracow University of Technology, 31-155 Kraków, Poland
Interests: machining; manufacturing process mechanics; production engineering; computer-aided engineering; finite element analysis; FE analysis; materials; materials science; mechanical processes; CAD
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image analysis carried out in industry using various types of vision systems is performed for the purpose of quick, noncontact, and multiparameter quality assessment of the product. It is introduced in many industries, enabling the implementation of a very wide range of control and measurement tasks. It has become a part of quality control systems operating in factories and is at the same time a source of product data collected by company databases supporting MESs (manufacturing execution systems). At the same time, image analysis is used as part of controlling the parameters of the production process, e.g., in tasks of positioning machines and robots, controlling tool wear or checking the wear of machine parts.  

Expanding the scope of image analysis application is associated with the development of new 2D and 3D imaging methods, increasing the resolution of matrices and optical systems, and the introduction of new algorithms as part of using artificial intelligence. 

This Special Edition is intended to indicate and discuss innovative tasks implemented using image analysis carried out as part of the parameter control describing the quality of the product. Works that present innovative imaging methods, image calibration methods, and image analysis methods used on production lines are invited. The presentation of the possibility of using image analysis in a wide range of tasks carried out as part of product and process quality control is extremely important from the point of view of improving product quality and minimizing production losses. The main goals of the release are to present a combination of scientific achievements with the possibility of their application in a variable and difficult industrial environment. 

Prof. Dr. Andrzej Sioma
Dr. Grzegorz Struzikiewicz
Guest Editors

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

  • Shape inspection
  • Surface inspection
  • Color inspection
  • Measurement on image
  • Thermal and hyperspectral inspection
  • Assembly inspection
  • Packaging and print inspection

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Published Papers (5 papers)

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Research

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12 pages, 1829 KiB  
Article
Estimating Moisture Content of Sausages with Different Types of Casings via Hyperspectral Imaging in Tandem with Multivariate
by Chao-Hui Feng and Hirofumi Arai
Appl. Sci. 2023, 13(9), 5300; https://doi.org/10.3390/app13095300 - 24 Apr 2023
Cited by 4 | Viewed by 2208
Abstract
The moisture levels in sausages that were stored for 16 days and added with different concentrations of orange extracts to a modification solution were assessed using response surface methodology (RSM). Among the 32 treatment matrixes, treatment 10 presented a higher moisture content than [...] Read more.
The moisture levels in sausages that were stored for 16 days and added with different concentrations of orange extracts to a modification solution were assessed using response surface methodology (RSM). Among the 32 treatment matrixes, treatment 10 presented a higher moisture content than that of treatment 19. Spectral pre-treatments were employed to enhance the model’s robustness. The raw and pre-processed spectral data, as well as moisture content, were fitted to a regression model. The RSM outcomes showed that the interactive effects of [soy lecithin concentration] × [soy oil concentration] and [soy oil concentration] × [orange extract addition] on moisture were significant (p < 0.05), resulting in an R2 value of 78.28% derived from a second-order polynomial model. Hesperidin was identified as the primary component of the orange extracts using high-performance liquid chromatography (HPLC). The PLSR model developed from reflectance data after normalization and 1st derivation pre-treatment showed a higher coefficient of determination in the calibration set (0.7157) than the untreated data (0.2602). Furthermore, the selection of nine key wavelengths (405, 445, 425, 455, 585, 630, 1000, 1075, and 1095 nm) could render the model simpler and allow for easy industrial applications. Full article
(This article belongs to the Special Issue Image Analysis for Product Quality Control)
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13 pages, 8384 KiB  
Article
Detection of Fungal Infections on the Wood Surface Using LTM Imaging
by Andrzej Sioma and Bartosz Lenty
Appl. Sci. 2023, 13(1), 490; https://doi.org/10.3390/app13010490 - 30 Dec 2022
Cited by 3 | Viewed by 1922
Abstract
Wood used in production processes can be infected by various fungi growing on its surface. The presence of fungi on the wood surface results from the method of storage, handling and transport of the wood. However, the presence of fungi on wood carries [...] Read more.
Wood used in production processes can be infected by various fungi growing on its surface. The presence of fungi on the wood surface results from the method of storage, handling and transport of the wood. However, the presence of fungi on wood carries a high risk to the health of production operators and users. At the same time, it has a negative impact on the quality and durability of manufactured products. Because of the risks indicated, an attempt was made to develop an industrial, automated system for detecting fungal infections. This paper presents a vision method for detecting fungal infections on the wood surface. A description of the vision system using the laser triangulation method (LTM) to build a three-dimensional surface image is shown. The paper consists of an analysis of the imaging resolution and a description of the concept of using laser illuminator power selection for identifying fungal-infested surfaces. Imaging results for the selected wavelength of electromagnetic radiation are presented. Measurements and parameters describing the identified areas are shown. It was found that it is possible to choose imaging method parameters and laser illumination power allowing identification under industrial conditions of a fungus-infected region on a wood surface while using the image to determine product measurement parameters. Full article
(This article belongs to the Special Issue Image Analysis for Product Quality Control)
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19 pages, 7401 KiB  
Article
Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
by Songuel Polat, Alain Tremeau and Frank Boochs
Appl. Sci. 2021, 11(18), 8424; https://doi.org/10.3390/app11188424 - 10 Sep 2021
Cited by 3 | Viewed by 2202
Abstract
Successful recycling of electronic waste requires accurate separation of materials such as plastics, PCBs and electronic components on PCBs (capacitors, transistors, etc.). This article therefore proposes a vision approach based on a combination of 3D and HSI data, relying on the mutual support [...] Read more.
Successful recycling of electronic waste requires accurate separation of materials such as plastics, PCBs and electronic components on PCBs (capacitors, transistors, etc.). This article therefore proposes a vision approach based on a combination of 3D and HSI data, relying on the mutual support of the datasets to compensate existing weaknesses when using single 3D- and HSI-Sensors. The combined dataset serves as a basis for the extraction of geometric and spectral features. The classification is performed and evaluated based on these extracted features which are exploited through rules. The efficiency of the proposed approach is demonstrated using real electronic waste and leads to convincing results with an overall accuracy (OA) of 98.24%. To illustrate that the addition of 3D data has added value, a comparison is also performed with an SVM classification based only on hyperspectral data. Full article
(This article belongs to the Special Issue Image Analysis for Product Quality Control)
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23 pages, 21289 KiB  
Article
Characterization of Prints Based on Microscale Image Analysis of Dot Patterns
by Indrama Das, Swati Bandyopadhyay and Alain Trémeau
Appl. Sci. 2021, 11(14), 6634; https://doi.org/10.3390/app11146634 - 20 Jul 2021
Cited by 1 | Viewed by 4304
Abstract
Identifying a print document (original) from a reprint document (copy or fake) can be a challenge. The analyse at microscopic scale of print documents shows random dot shapes which depend on the printing parameters as well as the printing device used. We can, [...] Read more.
Identifying a print document (original) from a reprint document (copy or fake) can be a challenge. The analyse at microscopic scale of print documents shows random dot shapes which depend on the printing parameters as well as the printing device used. We can, therefore, draw the assumption that the dot shapes can be used as a fingerprint to differentiate a print from a reprint. In this paper, we explore several shape indexes that were not investigated until now to analyse at microscopic scale documents printed on aluminium foils using rotogravure printing process. This paper presents a statistical analysis which is based on a pattern recognition process defined by three steps. First, a new image processing pipeline is used to segment automatically disconnected dots. Next, new dot pattern features are used to characterize automatically dot patterns. Six types of dot patterns (including four types of doughnut patterns) are introduced. Lastly, a new statistical analysis method is used to characterize a printed sample from the set of dots printed on it. The experiments done demonstrate the relevance of the analytical method proposed. Results shows the potential of this method to identify a reprint from a print. Full article
(This article belongs to the Special Issue Image Analysis for Product Quality Control)
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Review

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13 pages, 2997 KiB  
Review
Vision System in Product Quality Control Systems
by Andrzej Sioma
Appl. Sci. 2023, 13(2), 751; https://doi.org/10.3390/app13020751 - 5 Jan 2023
Cited by 8 | Viewed by 3691
Abstract
The development of technology for manufacturing products and machines carrying out technological operations is closely linked to developing systems for tracking and controlling product and production process parameters. This paper shows how the development of quality control and production management systems such as [...] Read more.
The development of technology for manufacturing products and machines carrying out technological operations is closely linked to developing systems for tracking and controlling product and production process parameters. This paper shows how the development of quality control and production management systems such as TQM and MES is related to the development of imaging and image analysis methods used in industry. The development of imaging methods is discussed in the context of developing product quality control capabilities. It is also shown as to what extent image analysis can be used to observe manufacturing parameters and process management capabilities. It was noted that the use of vision systems as an industrial measurement-quality control system would still increase. Due to the increase in imaging resolution, there was an increase in the imaging frequency, growth in the spectral range of imaging, and the dynamic development of three-dimensional and hybrid imaging methods. Based on experience from industrial applications and tasks described in scientific publications, areas where vision systems will play a key role in inspection tasks have been identified. This is the introductory article for the Special Issue “Image Analysis for Product Quality Control” on using vision systems in various industries to execute production quality control tasks. Full article
(This article belongs to the Special Issue Image Analysis for Product Quality Control)
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