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Intelligent Industrial Process Control Systems: 2nd Edition
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Intelligent Industrial Process Control Systems: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 2111

Special Issue Editor

Dr. Iwona Grobelna

E-Mail Website
Guest Editor
Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, 65-516 Zielona Góra, Poland
Interests: control systems; formal verification; Petri nets; model checking; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The widespread realization of Industry 4.0 forces continuous progress in all its embedded technologies. Intelligent manufacturing systems are modern systems of manufacturing that integrate the abilities of humans, machines and processes to achieve the best possible outcome. In this context, control processes directly influence the behavior of industrial systems. They are supposed to operate in a safe, reliable and precise way. In order to do that, several modern technologies are combined together in an integrated design involving artificial intelligence.

This Special Issue is dedicated to interdisciplinary research in the area of intelligent industrial process control systems. It calls for cutting-edge contributions to fundamental theoretical research, as well as application-based research. This Special Issue covers, but is not limited to, the following topics:

  • Artificial intelligence for industrial applications;
  • Computational intelligence in control;
  • Cyber-security of industrial control systems;
  • Digital manufacturing;
  • Flexible manufacturing systems;
  • Industrial control systems;
  • Industry 4.0;
  • Intelligent control;
  • Intelligent industrial processes;
  • Microcontrollers, FPGA, PLC, and modern electronic systems for Industry 4.0;
  • Specification of industrial control systems;
  • Verification of industrial control systems.

Dr. Iwona Grobelna
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. Sensors 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 2600 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.

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Published Papers (2 papers)

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Research

19 pages, 5025 KiB  
Article
Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis
by Simone Mari, Giovanni Bucci, Fabrizio Ciancetta, Edoardo Fiorucci and Andrea Fioravanti
Sensors 2025, 25(9), 2710; https://doi.org/10.3390/s25092710 - 24 Apr 2025
Viewed by 274
Abstract
This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual [...] Read more.
This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contaminants critical for quality assurance. The system acquires high-resolution monochrome images under various lighting configurations, including natural light and infrared (IR) at 850 nm and 940 nm, with different angles of incidence. Four blob detection algorithms—adaptive thresholding, Laplacian of Gaussian (LoG), Difference of Gaussians (DoG), and Determinant of Hessian (DoH)—were implemented and evaluated based on their ability to detect surface impurities. Performance was assessed by comparing the total detected blob area before and after the cleaning process, providing a proxy for both sensitivity and false positive rate. Among the tested methods, adaptive thresholding under 30° natural light produced the best results, with a statistically significant z-score of +2.05 in the pre-wash phase and reduced false detections in post-wash conditions. The LoG and DoG methods were more prone to spurious detections, while DoH demonstrated intermediate performance but struggled with reflective surfaces. The proposed approach offers a cost-effective and scalable solution for real-time quality control in industrial environments, with the potential to improve process reliability and reduce waste due to surface defects. Full article
(This article belongs to the Special Issue Intelligent Industrial Process Control Systems: 2nd Edition)
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20 pages, 20405 KiB  
Article
A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing
by Tangxiao Yuan, Kondo Hloindo Adjallah, Alexandre Sava, Huifen Wang and Linyan Liu
Sensors 2024, 24(20), 6586; https://doi.org/10.3390/s24206586 - 12 Oct 2024
Cited by 1 | Viewed by 1240
Abstract
The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue [...] Read more.
The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings. Full article
(This article belongs to the Special Issue Intelligent Industrial Process Control Systems: 2nd Edition)
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