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Applied Sciences
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Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing
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Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing

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 June 2022) | Viewed by 3906

Special Issue Editors

Dr. Haizea González-Barrio

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Alameda de Urquijo s/n, 48013 Bilbao, Spain
Interests: super abrasive machining; milling; manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Amaia Calleja-Ochoa

E-Mail Website1 Website2
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Interests: mechanical engineering; coatings; machining; manufacturing of aeroengine components
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 is now underway, changing traditional manufacturing processes into smart manufacturing. Smart manufacturing is one of the main industries to make full use of artificial intelligence and machine-learning technologies. Artificial intelligence is making machines smarter than before in the manufacturing industry by addressing how to build computers that improve automatically with experience. This Special Issue is open to new findings and approaches related to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. We encourage researchers to contribute to this Special Issue, including, but not being limited to, the following subject areas:

  • Real-time monitoring with machine learning;
  • Artificial intelligence for predictive maintenance;
  • Production scheduling with reinforcement learning;
  • Artificial intelligence and robotics in smart manufacturing;
  • IoT-enabled smart manufacturing;
  • Digital twin-driven smart manufacturing.

Dr. Haizea González-Barrio
Dr. Amaia Calleja-Ochoa
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

  • smart manufacturing
  • machine learning
  • digital twins
  • monitoring and control in manufacturing

Published Papers (2 papers)

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Research

12 pages, 1584 KiB  
Article
Research on Normal Behavior Models for Status Monitoring and Fault Early Warning of Pitch Motors
by Liang Yuan, Lirong Qiu and Chunxia Zhang
Appl. Sci. 2022, 12(15), 7747; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157747 - 01 Aug 2022
Cited by 3 | Viewed by 996
Abstract
Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based [...] Read more.
Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based on the normal behavior modeling technique, this paper studies the status monitoring of the pitch motors. Based on the fact that the state of the motor varies with time, we propose to train an echo state network with the SCADA data to predict the temperature of the pitch motor. Subsequently, the EWMA (exponentially weighted moving average) technique is used to set the alarm limit lines of each parameter. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and early warn the faults of the pitch motor. Full article
(This article belongs to the Special Issue Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing)
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19 pages, 2454 KiB  
Article
A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin
by Minyeol Yang, Junhyung Moon, Jongpil Jeong, Seokho Sin and Jimin Kim
Appl. Sci. 2022, 12(2), 553; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020553 - 06 Jan 2022
Cited by 7 | Viewed by 2172
Abstract
Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method [...] Read more.
Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins. Full article
(This article belongs to the Special Issue Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing)
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