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Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 39700

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Special Issue Editors

Dr. Kim Phuc Tran

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Department of Automation and Industrial Informatics, ENSAIT & GEMTEX, University of Lille, 2 allée Louise et Victor Champier, 59056 Roubaix, France
Interests: explainable anomaly detection; decision support systems; federated learning; cybersecurity

Dr. Yi Man

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Guest Editor

State Key Laboratory of Pulp & Paper Engineering, South China University of Technology, Guangzhou 510006, China
Interests: modeling and simulation; integrated optimization; energy saving and emission reduction of industrial processes

Dr. Zhenglei He

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Guest Editor

State Key Laboratory of Pulp & Paper Engineering, South China University of Technology, Guangzhou 510006, China
Interests: AI-based process modeling and optimization; energy conservation and production scheduling

Special Issue Information

Dear Colleagues,

This special issue focuses on the current situation and future prospects of artificial intelligence and machine learning in industrial automation. The advantages of AI include the ability to judge, respond, collect information, and identify more quickly, efficiently and accurately than ordinary people. This can help humans to complete operations with higher productivity, reduce human work intensity in complex industrial production, and improve the efficiency and stability of industrial production. For example, in the application process of Internet of Things technology, information such as positioning and intelligent recognition can be comprehensively used to expand corresponding functions. This is very beneficial to the development of industrial automation, but also to meet automotive assembly industry automation development needs. In addition, machine learning is applied to industrial modeling and prediction, and can also assist product-line design and final testing. Automation evidently plays a crucial role in the industry, and AI, as an engineering discipline, has an irreplaceable role in industrial automation.

This Special Issue focuses on the application of AI and machine learning in various fields of industrial automation, with special attention to the automation implementation and equipment integration solutions of factory production. Submitted articles can cover various topics, including product recognition, intelligent manufacturing, traditional industrial upgrading, equipment automation, and the Internet of Things. Our goal is to share successful cases of using artificial intelligence to solve practical industrial problems, and to create an international flow platform for the application of artificial intelligence in industrial automation, to widely apply this technology to industrial production.

Dr. Kim Phuc Tran
Dr. Yi Man
Dr. Zhenglei He
Guest Editors

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Keywords

explainable anomaly detection
decision support systems
federated learning
cybersecurity
logistics management
industrial automation, electrical automation
metal industry, food industry, industrial water, iron and steel industry, textile industry, etc.
autopilot
intelligent defect detection
fault diagnosis and prevention
visual recognition
the internet of things
intelligent equipment manufacturing
optimization of processes and procedures
deep learning
fuzzy control

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Published Papers (13 papers)

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Research

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16 pages, 2903 KiB

Open AccessArticle

Evaluation of the Machinability of Ti-6Al-4V Titanium Alloy by AWJM Using a Multipass Strategy

by Lisa Dekster, Nikolaos E. Karkalos, Panagiotis Karmiris-Obratański and Angelos P. Markopoulos

Appl. Sci. 2023, 13(6), 3774; https://doi.org/10.3390/app13063774 - 15 Mar 2023

Cited by 5 | Viewed by 1355

Abstract

Non-conventional machining processes offer various advantages, including the capability of processing hard-to-cut materials with a reasonable cost and sufficient productivity. However, depending on the application, different machining strategies need to be employed, in order to increase the flexibility of the process and produce parts with a better quality. In this study, experimental work was conducted and the use of a multipass strategy during slot milling of titanium alloy with abrasive water jet milling (AWJM) was explored, by comparing the effect of different numbers of passes under different process conditions, such as the jet pressure and traverse feed rate. The performance was evaluated by means of the kerf characteristics, and the productivity through material removal rate (MRR) values. The results indicated that the use of a multipass strategy had a considerable impact on the kerf taper angle, apart from the depth of penetration; and although it leads to reduced MRR and cutting efficiency, choosing appropriate values of process parameters, such as a higher jet pressure and moderate traverse feed, in combination with a moderate amount of passes, can be beneficial for AWJM from different points of view. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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22 pages, 6520 KiB

Open AccessArticle

Visual Simulator for Mastering Fundamental Concepts of Machine Learning

by Adrian Milakovic, Drazen Draskovic and Bosko Nikolic

Appl. Sci. 2022, 12(24), 12974; https://doi.org/10.3390/app122412974 - 17 Dec 2022

Viewed by 2064

Abstract

Machine learning (ML) has become an increasingly popular choice of scientific research for many students due to its application in various fields. However, students often have difficulty starting with machine learning concepts due to too much focus on programming. Therefore, they are deprived of a more profound knowledge of machine learning concepts. The purpose of this research study was the analysis of introductory courses in machine learning at some of the best-ranked universities in the world and existing software tools used in those courses and designed to assist in learning machine learning concepts. Most university courses are based on the Python programming language and tools realized in this language. Other tools with less focus on programming are quite difficult to master. The research further led to the proposal of a new practical tool that users can use to learn without needing to know any programming language or programming skills. The simulator includes three methods: linear regression, decision trees, and k-nearest neighbors. In the research, several case studies are presented with applications of all realized ML methods based on real problems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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20 pages, 5797 KiB

Open AccessArticle

Deep Learning and Bayesian Hyperparameter Optimization: A Data-Driven Approach for Diamond Grit Segmentation toward Grinding Wheel Characterization

by Damien Sicard, Pascal Briois, Alain Billard, Jérôme Thevenot, Eric Boichut, Julien Chapellier and Frédéric Bernard

Appl. Sci. 2022, 12(24), 12606; https://doi.org/10.3390/app122412606 - 8 Dec 2022

Cited by 2 | Viewed by 2867

Abstract

Diamond grinding wheels (DGWs) have a central role in cutting-edge industries such as aeronautics or defense and spatial applications. Characterizations of DGWs are essential to optimize the design and machining performance of such cutting tools. Thus, the critical issue of DGW characterization lies in the detection of diamond grits. However, the traditional diamond detection methods rely on manual operations on DGW images. These methods are time-consuming, error-prone and inaccurate. In addition, the manual detection of diamond grits remains challenging even for a subject expert. To overcome these shortcomings, we introduce a deep learning approach for automatic diamond grit segmentation. Due to our small dataset of 153 images, the proposed approach leverages transfer learning techniques with pre-trained ResNet34 as an encoder of U-Net CNN architecture. Moreover, with more than 8600 hyperparameter combinations in our model, manually finding the best configuration is impossible. That is why we use a Bayesian optimization algorithm using Hyperband early stopping mechanisms to automatically explore the search space and find the best hyperparameter values. Moreover, considering our small dataset, we obtain overall satisfactory performance with over 53% IoU and 69% F1-score. Finally, this work provides a first step toward diamond grinding wheel characterization by using a data-driven approach for automatic semantic segmentation of diamond grits. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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16 pages, 23372 KiB

Open AccessArticle

Improved Combined Metric for Automatic Quality Assessment of Stitched Images

by Krzysztof Okarma and Mateusz Kopytek

Appl. Sci. 2022, 12(20), 10284; https://doi.org/10.3390/app122010284 - 12 Oct 2022

Cited by 1 | Viewed by 1531

Abstract

An automatic quality assessment of stitched images is an essential task in image analysis and is particularly useful not only in the creation of general-purpose panoramic images but also in terrain exploration and mapping made by mobile robots and drones. In Visual Simultaneous Localization and Mapping (VSLAM) solutions, the environment maps acquired by cameras mounted on the mobile robots may be captured in dynamically changing lighting conditions and subject to some other distortions influencing the final quality of the panoramic images representing the robot’s surroundings. Such images may also be used for motion planning and visual navigation for other robots, e.g., in follow-the-leader scenarios. Another relevant application area of panoramic imaging is Virtual Reality (VR), particularly head-mounted displays, where perceived image quality is even more important. Hence, automatic quality evaluations of stitched images should be made using algorithms that are both sensitive to various types of distortions and strongly consistent with subjective quality impression. The approach presented in this paper extends the state-of-the-art metric known as the Stitched Image Quality Evaluator (SIQE) by embedding it with some additional features using the proposed new combination scheme. The developed combined metric based on a nonlinear combination of the SIQE and additional features led to a substantially higher correlation with the subjective quality scores. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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14 pages, 5329 KiB

Open AccessArticle

Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety

by Ahatsham Hayat and Fernando Morgado-Dias

Appl. Sci. 2022, 12(16), 8268; https://doi.org/10.3390/app12168268 - 18 Aug 2022

Cited by 45 | Viewed by 7352

Abstract

Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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26 pages, 790 KiB

Open AccessArticle

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

by Ningkui Wang and Daijun Wei

Appl. Sci. 2022, 12(15), 7633; https://doi.org/10.3390/app12157633 - 28 Jul 2022

Cited by 1 | Viewed by 1998

Abstract

Multiagent systems (MASs) have a wide range of industrial applications due to agents’ advantages. However, because of the agents’ dynamic behaviors, it is a challenge to ensure the quality of service they present. In this paper, to address this problem, we propose an adaptive agent trust estimation model where agents may decide to go from genuine to malicious or the other way around. In the proposed trust model, both direct trust and indirect reputation are used. However, the indirect reputation derived from the direct experience of third-party agents must have reasonable confidence to be useful. The proposed model introduces a near-perfect measure that utilizes consistency, credibility, and certainty to capture confidence. Moreover, agents are incentivized to contribute correct information (to be honest) through a credit mechanism in the proposed model. Simulation experiments are conducted to evaluate the proposed model’s performance against some of the previous trust models reported in the literature. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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23 pages, 10179 KiB

Open AccessArticle

BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters

by Bogdan Kovalenko, Vladimir Lukin, Sergii Kryvenko, Victoriya Naumenko and Benoit Vozel

Appl. Sci. 2022, 12(15), 7555; https://doi.org/10.3390/app12157555 - 27 Jul 2022

Cited by 5 | Viewed by 1472

Abstract

With a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression of such images has several peculiarities dealing with specific noise filtering effects and evaluation of the compression technique’s performance. In particular, an optimal operation point (OOP) may exist where quality of a compressed image is closer to the corresponding noise-free (true) image than the uncompressed (original, noisy) image quality, according to certain criterion (metrics). In such a case, it is reasonable to automatically compress an image under interest in the OOP neighborhood, but without having the true image at disposal in practice, it is impossible to accurately determine if the OOP does exist. Here we show that, by a simple and fast preliminary analysis and pre-training, it is possible to predict the OOPs existence and the metric values in it with appropriate accuracy. The study is carried out for a better portable graphics (BPG) coder for additive white Gaussian noise, focusing mainly on one-component (grayscale) images. The results allow for concluding that prediction is possible for an improvement (reduction) in the quality metrics of PSNR and PSNR-HVS-M. In turn, this allows for decision-making about the existence or absence of an OOP. If an OOP is absent, a more “careful” compression is recommended. Having such rules, it then becomes possible to carry out the compression automatically. Additionally, possible modifications for the cases of signal-dependent noise and the joint compression of three-component images are considered and the possible existence of an OOP for these cases is demonstrated. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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9 pages, 2787 KiB

Open AccessArticle

A Graph-Based Representation Method for Fashion Color

by Yuyilan Chen, Yuqian Dai, Li Li, Chenqu Ma and Xiaogang Liu

Appl. Sci. 2022, 12(13), 6742; https://doi.org/10.3390/app12136742 - 3 Jul 2022

Viewed by 2585

Abstract

Fashion color research takes the color information of fashion apparel as the major focus for further studies, such as style categorization or trend prediction. However, the colors in apparel are treated as isolated elements from each other, disregarding the fact that not only the attributes of each color itself but also the collocation relationship of the colors in apparel are important color factors. To provide a more comprehensive abstraction of the information from the fashion colors as well as emulating the human cognition of fashion colors, in this paper, we are the first to propose a knowledge graph-based representation method that captures not only the individual colors but also abstracts the spatial relation of all the colors that appear in a single piece of fashion apparel. This method provides the fundamental definition of the abstraction of the relation of colors, a detailed method to construct the color graph, as well as the practical matrix-based management and the visualization of the constructed graphs. The case studies for color data extraction and extended usage demonstrate the effectiveness of our method with comprehensive color data representation and effective information extraction. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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13 pages, 1028 KiB

Open AccessArticle

Using Maxwell Distribution to Handle Selector’s Indecisiveness in Choice Data: A New Latent Bayesian Choice Model

by Muhammad Arshad, Tanveer Kifayat, Juan L. G. Guirao, Juan M. Sánchez and Adrián Valverde

Appl. Sci. 2022, 12(13), 6337; https://doi.org/10.3390/app12136337 - 22 Jun 2022

Cited by 1 | Viewed by 1445

Abstract

This research primarily aims at the development of new pathways to facilitate the resolving of the long debated issue of handling ties or the degree of indecisiveness precipitated in comparative information. The decision chaos is accommodated by the elegant application of the choice axiom ensuring intact utility when imperfect choices are observed. The objectives are facilitated by inducing an additional parameter in the probabilistic set up of Maxwell to retain the extent of indecisiveness prevalent in the choice data. The operational soundness of the proposed model is elucidated through the rigorous employment of Gibbs sampling—a popular approach of the Markov chain Monte Carlo methods. The outcomes of this research clearly substantiate the applicability of the proposed scheme in retaining the advantages of discrete comparative data when the freedom of no indecisiveness is permitted. The legitimacy of the devised mechanism is enumerated on multi-fronts such as the estimation of preference probabilities and assessment of worth parameters, and through the quantification of the significance of choice hierarchy. The outcomes of the research highlight the effects of sample size and the extent of indecisiveness exhibited in the choice data. The estimation efficiency is estimated to be improved with the increase in sample size. For the largest considered sample of size 100, we estimated an average confidence width of 0.0097, which is notably more compact than the contemporary samples of size 25 and 50. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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16 pages, 4154 KiB

Open AccessArticle

An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition

by Feiyu Jia, Ali Jebelli, Yongsheng Ma and Rafiq Ahmad

Appl. Sci. 2022, 12(11), 5697; https://doi.org/10.3390/app12115697 - 3 Jun 2022

Cited by 4 | Viewed by 2508

Abstract

Smart manufacturing uses robots and artificial intelligence techniques to minimize human interventions in manufacturing activities. Inspection of the machine’ working status is critical in manufacturing processes, ensuring that machines work correctly without any collisions and interruptions, e.g., in lights-out manufacturing. However, the current method heavily relies on workers onsite or remotely through the Internet. The existing approaches also include a hard-wired robot working with a computer numerical control (CNC) machine, and the instructions are followed through a pre-program path. Currently, there is no autonomous machine tending application that can detect and act upon the operational status of a CNC machine. This study proposes a deep learning-based method for the CNC machine detection and working status recognition through an independent robot system without human intervention. It is noted that there is often more than one machine working in a representative industrial environment. Therefore, the SiameseRPN method is developed to recognize and locate a specific machine from a group of machines. A deep learning-based text recognition method is designed to identify the working status from the human–machine interface (HMI) display. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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25 pages, 5261 KiB

Open AccessArticle

An Interactive Personalized Garment Design Recommendation System Using Intelligent Techniques

by Zhujun Wang, Xuyuan Tao, Xianyi Zeng, Yingmei Xing, Yanni Xu, Zhenzhen Xu, Pascal Bruniaux and Jianping Wang

Appl. Sci. 2022, 12(9), 4654; https://doi.org/10.3390/app12094654 - 6 May 2022

Cited by 8 | Viewed by 2831

Abstract

This paper presents a garment design recommendation system based on two mathematical models that permit the prediction and control of garment styles and structural parameters from a consumer’s personalized requirements in terms of fitting and aesthetics. Based on a formalized professional garment knowledge base, enabling the quantitative characterization of the relations between consumer profiles and garment profiles (colors, fabrics, styles, and garment fit), these two models aim at recommending the most relevant garment profile from a specific consumer profile, using reasoning with fuzzy rules and self-adjusting the garment patterns according to the feedback of the 3D virtual fitting effects corresponding to the recommended garment profile, using a genetic algorithm (GA) and support vector regression. Based on these knowledge-based models, the proposed interactive recommendation system enables the progressive optimization of the design solution through a series of human–machine interactions, i.e., the repeated execution of the cycle “design generation—virtual garment demonstration—user’s evaluation—adjustment” until the satisfaction of the end user (consumer or designer). The effectiveness of this interactive recommendation system was validated by a real case of pants customization. In a manner different from the existing approaches, the proposed system will enable designers to rapidly, accurately, intelligently, and automatically generate the optimal design solution, which is relevant in dealing with mass customization and e-shopping for fashion companies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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Review

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19 pages, 1290 KiB

Open AccessReview

Blockchain-Based Internet of Medical Things

by Hamed Taherdoost

Appl. Sci. 2023, 13(3), 1287; https://doi.org/10.3390/app13031287 - 18 Jan 2023

Cited by 20 | Viewed by 2626

Abstract

IoMT sensor nodes, Internet of Things (IoT) wearable medical equipment, healthcare facilities, patients, and insurance firms are all increasingly being included in IoMT systems. Therefore, it is difficult to create a blockchain design for such systems, since scalability is among the most important aspects of blockchain technology. This realization prompted us to comprehensively analyze blockchain-based IoMT solutions developed in English between 2017 and 2022. This review incorporates the theoretical underpinnings of a large body of work published in highly regarded academic journals over the past decade, to standardize evaluation methods and fully capture the rapidly developing blockchain space. This study categorizes blockchain-enabled applications across various industries such as information management, privacy, healthcare, business, and supply chains according to a structured, systematic evaluation, and thematic content analysis of the literature that is already identified. The gaps in the literature on the topic have also been highlighted, with a special focus on the restrictions posed by blockchain technology and the knock-on effects that such restrictions have in other fields. Based on these results, several open research questions and potential avenues for further investigation that are likely to be useful to academics and professionals alike are pinpointed. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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34 pages, 5898 KiB

Open AccessReview

A Survey of CNN-Based Network Intrusion Detection

by Leila Mohammadpour, Teck Chaw Ling, Chee Sun Liew and Alihossein Aryanfar

Appl. Sci. 2022, 12(16), 8162; https://doi.org/10.3390/app12168162 - 15 Aug 2022

Cited by 47 | Viewed by 6920

Abstract

Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Industrial Automation: Methods and Applications)

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