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Applied Sciences
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Artificial Intelligence and Optimization in Industry 4.0
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Artificial Intelligence and Optimization in Industry 4.0

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 November 2022) | Viewed by 14459

Special Issue Editor

Prof. Dr. Chun-Cheng Lin

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan
Interests: metaheuristic algorithms; machine learning; Internet of Things; wireless networks; information visualization; computational management science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has brought about a change in the pattern of the operation of industry, driven by a new form of interaction between man and machine. Development of AI techniques for Industry 4.0 can help manufacturers improve product quality, reduce cost, and optimize manufacturing processes. The key for driving Industry 4.0 is to enable smarter machines through adopting AI techniques to leverage the ability of novel technologies, including Internet of things (IoT), 3D printing, robots, machine learning, 5G, cloud/fog/edge computing, digital twins, blockchains, among others. However, the complexity of using AI in Industry 4.0 or smart manufacturing requires manufacturers to collaborate with specialists to achieve appropriate and customised solutions. In addition, building the necessary technology for AI in Industry 4.0 is much costy and requires in-depth knowledge both internally and technically. It is also challenging to take into account zero carbon in all operations in Industry 4.0. Therefore, we kindly invite researchers and practitioners to contribute their high-quality original research or review articles on these topics to this special issue.

Prof. Dr. Chun-Cheng Lin
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

  • artificial intelligence and optimization in Industry 4.0
  • AI and optimization for deployment of smart factory, facility planning, scheduling, and logistics optimization in Industry 4.0
  • AI and cloud/fog/edge computing in Industry 4.0
  • AI and Private 5G network in Industry 4.0
  • AI and digital twin in Industry 4.0
  • AI and Blockchain in Industry 4.0
  • AI and the Internt of things in Industry 4.0
  • AI applications for smart manufacturing
  • AI Applications of unmanned aerial vehicles (UAV) and automated guided vehicles (AGV) in Industry 4.0
  • sustainability and zero carbon in Industry 4.0
  • digital manufacturing

Published Papers (4 papers)

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Research

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19 pages, 4179 KiB  
Article
SSA-CAE-Based Abnormal Data Classification Method in Edge Intelligence Device of CNC Machine
by Donghyun Kim, Seokju Oh, Jeahyeong Lee and Jongpil Jeong
Appl. Sci. 2022, 12(12), 5864; https://0-doi-org.brum.beds.ac.uk/10.3390/app12125864 - 09 Jun 2022
Viewed by 1618
Abstract
Smart factories and big data are important factors in the Fourth Industrial Revolution. Smart factories aim for automation and integration; however, the most important part is the application of data. Despite extensive research on the maintenance and quality management of big data-based production [...] Read more.
Smart factories and big data are important factors in the Fourth Industrial Revolution. Smart factories aim for automation and integration; however, the most important part is the application of data. Despite extensive research on the maintenance and quality management of big data-based production equipment, industrial data gathered for analysis contain more normal data than abnormal data. In addition, a significant amount of energy is expended in the data pre-processing process to analyze the acquired data. Therefore, to maintain production equipment and quality management, data classification technology that allows easy data analysis by classifying abnormal data into normal data is required. In this paper, we propose an abnormal data classification architecture for cycle data sets gathered from production facilities through SSA-CAE along with data storage methods for each product unit. SSA-CAE is a hybrid technique that combines singular spectrum analysis (SSA) techniques that are effective in reducing noise in time series data with convolutional auto encoder (CAE) that have performed well in time series. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Industry 4.0)
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17 pages, 2631 KiB  
Article
Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
by Abdul Wahid, John G. Breslin and Muhammad Ali Intizar
Appl. Sci. 2022, 12(9), 4221; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094221 - 22 Apr 2022
Cited by 18 | Viewed by 5766
Abstract
The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given [...] Read more.
The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses in revenue that can be reduced by making accurate failure predictions using the sensor data. Internet of Things (IoT) technologies have made it possible to collect sensor data in real time. We found that hybrid modelling can result in efficient predictions as they are capable of capturing the abstract features which facilitate better predictions. In addition, developing effective optimization strategy is difficult because of the complex nature of different sensor data in real time scenarios. This work proposes a method for multivariate time-series forecasting for predictive maintenance (PdM) based on a combination of convolutional neural networks and long short term memory with skip connection (CNN-LSTM). We experiment with CNN, LSTM, and CNN-LSTM forecasting models one by one for the prediction of machine failures. The data used in this experiment are from Microsoft’s case study. The dataset provides information about the failure history, maintenance history, error conditions, and machine features and telemetry, which consists of information such as voltage, pressure, vibration, and rotation sensor values recorded between 2015 and 2016. The proposed hybrid CNN-LSTM framework is a two-stage end-to-end model in which the LSTM is leveraged to analyze the relationships among different time-series data variables through its memory function, and 1-D CNNs are responsible for effective extraction of high-level features from the data. Our method learns the long-term patterns of the time series by extracting the short-term dependency patterns of different time-series variables. In our evaluation, CNN-LSTM provided the most reliable and highest prediction accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Industry 4.0)
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14 pages, 860 KiB  
Article
Hybrid Salp Swarm Algorithm for Solving the Green Scheduling Problem in a Double-Flexible Job Shop
by Changping Liu, Yuanyuan Yao and Hongbo Zhu
Appl. Sci. 2022, 12(1), 205; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010205 - 25 Dec 2021
Cited by 10 | Viewed by 2636
Abstract
Green scheduling is not only an effective way to achieve green manufacturing but also an effective way for modern manufacturing enterprises to achieve energy conservation and emission reduction. The double-flexible job shop scheduling problem (DFJSP) considers both machine flexibility and worker flexibility, so [...] Read more.
Green scheduling is not only an effective way to achieve green manufacturing but also an effective way for modern manufacturing enterprises to achieve energy conservation and emission reduction. The double-flexible job shop scheduling problem (DFJSP) considers both machine flexibility and worker flexibility, so it is more suitable for practical production. First, a multi-objective mixed-integer programming model for the DFJSP with the objectives of optimizing the makespan, total worker costs, and total influence of the green production indicators is formulated. Considering the characteristics of the problem, three-layer salp individual encoding and decoding methods are designed for the multi-objective hybrid salp swarm algorithm (MHSSA), which is hybridized with the Lévy flight, the random probability crossover operator, and the mutation operator. In addition, the influence of the parameter setting on the MHSSA in solving the DFJSP is investigated by means of the Taguchi method of design of experiments. The simulation results for benchmark instances show that the MHSSA can effectively solve the proposed problem and is significantly better than the MSSA and the MOPSO algorithm in the diversity, convergence, and dominance of the Pareto frontier. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Industry 4.0)
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Review

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24 pages, 2513 KiB  
Review
Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview
by Sharmin Sultana Sheuly, Mobyen Uddin Ahmed and Shahina Begum
Appl. Sci. 2022, 12(13), 6512; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136512 - 27 Jun 2022
Cited by 4 | Viewed by 3257
Abstract
The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have [...] Read more.
The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Industry 4.0)
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