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Explainable and Trustworthy AI/ML-Based Systems for Industry 4.0 and Smart...
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Explainable and Trustworthy AI/ML-Based Systems for Industry 4.0 and Smart Manufacturing

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

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 2295

Special Issue Editors

Prof. Dr. Yves Traon

E-Mail Website
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
Interests: software engineering; software testing; model driven engineering; security; adaptive systems; resilience
Dr. Sylvain Kubler

E-Mail Website
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
Interests: IoT; Industry 4.0; AI-based decision support systems; blockchain

Special Issue Information

Dear Colleagues,

The development towards the Industry 4.0 has a substantial influence on the manufacturing industry, allowing for the establishment of smart products and services, along with new and disruptive business models. Artificial Intelligance (AI) and Machine Learning (ML) are key enablers, being increasingly used by industries to build models for failure prediction (e.g., remaining useful life models), fault classification, or still for optimizing production and maintenance scheduling using Reinforcement Learning-like techniques. Although AI/ML techniques are promising, there are still research challenges to be addressed to make AI/ML-based systems (i) Explainable (XAI): need for creating suites of ML techniques that produce more explainable and interpretable models; and (ii) Trustworthy: need to design new approaches to robustify ML models against possible adversarial attacks and/or concept and data drift phenomena.

Overall, this Special Issue is addressed to all research studies dealing with Explainable and Trustworthy AI/ML-enabled systems for Industry 4.0 and Smart Manufacturing.

Prof. Dr. Yves Traon
Dr. Sylvain Kubler
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. 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.

Keywords

  • Industry 4.0
  • smart manufacturing
  • artificial intelligence
  • machine learning
  • explainable AI
  • trustworthy AI
  • adversarial ML

Published Papers (2 papers)

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Research

15 pages, 4819 KiB  
Article
In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks
by Maciej Rogalka, Jakub Krzysztof Grabski and Tomasz Garbowski
Sensors 2024, 24(4), 1051; https://doi.org/10.3390/s24041051 - 06 Feb 2024
Viewed by 539
Abstract
The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of [...] Read more.
The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of layers, and environmental conditions such as humidity and temperature. This study introduces an innovative application of convolutional neural networks (CNNs) for analyzing and classifying images of corrugated boards, particularly those with deformations. For this purpose, a special device with advanced imaging capabilities, including a high-resolution camera and image sensor, was developed and used to acquire detailed cross-section images of the corrugated boards. The samples of seven types of corrugated board were studied. The proposed approach involves optimizing CNNs to enhance their classification performance. Despite challenges posed by deformed samples, the methodology demonstrates high accuracy in most cases, though a few samples posed recognition difficulties. The findings of this research are significant for the packaging industry, offering a sophisticated method for quality control and defect detection in corrugated board production. The best classification accuracy obtained achieved more than 99%. This could lead to improved product quality and reduced waste. Additionally, this study paves the way for future research on applying machine learning for material quality assessment, which could have broader implications beyond the packaging sector. Full article
(This article belongs to the Special Issue Explainable and Trustworthy AI/ML-Based Systems for Industry 4.0 and Smart Manufacturing)
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22 pages, 6413 KiB  
Article
Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building
by Bo Peng and Sheng-Jen Hsieh
Sensors 2023, 23(10), 4857; https://doi.org/10.3390/s23104857 - 18 May 2023
Cited by 1 | Viewed by 1037
Abstract
Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and [...] Read more.
Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates. Traditional comfort models also lack the ability to adapt to individual demands and sensations. This research developed a data-driven thermal comfort model to improve the overall thermal comfort of occupants in office buildings. An architecture based on cyber-physical system (CPS) is used to achieve these goals. A building simulation model is built to simulate multiple occupants’ behaviors in an open-space office building. Results suggest that a hybrid model can accurately predict occupants’ thermal comfort level with reasonable computing time. In addition, this model can improve occupants’ thermal comfort by 43.41% to 69.93%, while energy consumption remains the same or is slightly reduced (1.01% to 3.63%). This strategy can potentially be implemented in real-world building automation systems with appropriate sensor placement in modern buildings. Full article
(This article belongs to the Special Issue Explainable and Trustworthy AI/ML-Based Systems for Industry 4.0 and Smart Manufacturing)
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