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Machines
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Advances in Artificial Intelligence Applications for Smart Manufacturing and Industry...
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Advances in Artificial Intelligence Applications for Smart Manufacturing and Industry 4.0

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Material Processing Technology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3265

Special Issue Editors

Prof. Dr. Paulo Eigi Miyagi

E-Mail Website
Guest Editor
Escola Politécnica, Universidade de São Paulo, Sao Paulo 05508-030, Brazil
Interests: discrete event system; supervisory control; design of automation system
Prof. Dr. Flávio Soares Corrêa Da Silva

E-Mail Website
Guest Editor
Departamento de Ciência da Computação, Universidade de São Paulo, Sao Paulo 05508-030, Brazil
Interests: artificial intelligence; knowledge representation; philosophy of information; philosophy of technology; digital entertainment
Prof. Dr. Alexandre Da Silva Simões

E-Mail Website
Guest Editor
Campus de Sorocaba, Instituto de Ciência e Tecnologia, Universidade Estadual Paulista, Sao Paulo 01049-010, Brazil
Interests: artificial intelligence
Prof. Dr. Fabrício Junqueira

E-Mail Website
Guest Editor
Departamento de Engenharia Mecatrônica e de Sistemas Mecânicos (PMR) (POLI), University of São Paulo, Sao Paulo 05508-060, Brazil
Interests: discrete event systems; petri nets; industry 4.0
Dr. Marcosiris Amorim de Oliveira Pessoa

E-Mail Website
Guest Editor
Department of Mechatronics and Mechanical Systems Engineering, Universidade de São Paulo, São Paulo 2231, Brazil
Interests: industry 4.0; cyber-physical systems; Internet of Things; virtual entreprises; APS systems; modeling and simulation; time windows; planning and scheduling heuristics; constraint programming
Special Issues, Collections and Topics in MDPI journals
Dr. Izabel Fernanda Machado

E-Mail Website
Guest Editor
Mechatronics and Mechanical System Engineering Department, Universidade de São Paulo, Sao Paulo, Brazil
Interests: development of advanced materials for cutting tools; hard materials processed by spark plasma sintering (SPS), including (but not limited to) modified hardmetals, novel binders for hard materials; SPS processed functional multi-graded materials (FGMs)
Special Issues, Collections and Topics in MDPI journals
Dr. Lilian Berton

E-Mail Website
Guest Editor
Institute of Science and Technology, Federal University of São Paulo, São José dos Campos 12247-014, Brazil
Interests: machine learning; complex network; graph-based methods

Special Issue Information

Dear Colleagues,

The term Industry 4.0 (I4.0) is used both to refer to the fourth industrial revolution as well as to the slogan of the initiative of European entities to define a type of standard for the structuring of smart production systems based on cyber-physical systems. Regardless of the interpretation, I4.0 has been extensively explored by several works and interesting technical–scientific and social contributions. That is, a relatively large number of works are under development, and a discussion that is increasingly relevant refers to the application of artificial intelligence (AI) techniques. Thus, the objective here is to present the various and possible visions and contributions of AI in the improvement and use of technologies that involve I4.0 such as digital twin, real-time monitoring and control, supply chain interoperability and integration, prescriptive maintenance, autonomous systems, robotics, machine tools, and cyber security.

Prof. Dr. Paulo Eigi Miyagi
Prof. Dr. Flávio Soares Corrêa Da Silva
Prof. Dr. Alexandre Da Silva Simões
Prof. Dr. Fabrício Junqueira
Dr. Marcosiris Amorim de Oliveira Pessoa
Dr. Izabel Fernanda Machado
Dr. Lilian Berton
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. Machines is an international peer-reviewed open access monthly 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

  • Industry 4.0
  • artificial intelligence
  • digital twin
  • real-time
  • supply chain
  • interoperability
  • system integration
  • prescriptive maintenance
  • autonomous systems
  • cyber security

Published Papers (2 papers)

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Research

12 pages, 1808 KiB  
Article
Structural Design with Self-Weight and Inertial Loading Using Simulated Annealing for Non-Gradient Topology Optimization
by Hossein Rostami Najafabadi, Thiago C. Martins, Marcos S. G. Tsuzuki and Ahmad Barari
Machines 2024, 12(1), 25; https://doi.org/10.3390/machines12010025 - 30 Dec 2023
Viewed by 932
Abstract
This paper explores implementation of self-weight and inertial loading in topology optimization (TO) employing the Simulated Annealing (SA) algorithm as a non-gradient-based technique. This method can be applied to find optimum design of structures with no need for gradient information. To enhance the [...] Read more.
This paper explores implementation of self-weight and inertial loading in topology optimization (TO) employing the Simulated Annealing (SA) algorithm as a non-gradient-based technique. This method can be applied to find optimum design of structures with no need for gradient information. To enhance the convergence of the SA algorithm, a novel approach incorporating the crystallization factor is introduced. The method is applied in a benchmark problem of a cantilever beam. The study systematically examines multiple scenarios, including cases with and without self-weight effects, as well as varying point loads. Compliance values are calculated and compared to those reported in existing literature to validate the accuracy of the optimization results. The findings highlight the versatility and effectiveness of the SA-based TO methodology in addressing complex design challenges with considerable self-weight or inertial effect. This work can contribute to structural design of systems where only the objective value is available with no gradient information to use sensitivity-based algorithms. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Applications for Smart Manufacturing and Industry 4.0)
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15 pages, 4750 KiB  
Article
Development and Application of Knowledge Graphs for the Injection Molding Process
by Zhe-Wei Zhou, Yu-Hung Ting, Wen-Ren Jong, Shia-Chung Chen and Ming-Chien Chiu
Machines 2023, 11(2), 271; https://doi.org/10.3390/machines11020271 - 10 Feb 2023
Cited by 1 | Viewed by 1795
Abstract
Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge [...] Read more.
Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge stored in books and online articles remains in the form of unstructured data, while some even remains unwritten, resulting in many difficulties in the construction of knowledge bases. Therefore, how to extract knowledge from unstructured data and engineers’ statements is a common goal of many enterprises. This study introduced the concept of a Knowledge Graph, a triplet extraction model based on bidirectional encoder representations from transformers (BERT) which was used to extract injection molding knowledge entities from text data, as well as the relationships between such entities, which were then stored in the form of knowledge graphs after entity alignment and classification with sentence-bidirectional encoder representations from transformers. In a test, the triplet extraction model achieved an F1 score of 0.899, while the entity alignment model and the entity classification model achieved accuracies of 0.92 and 0.93, respectively. Finally, a web platform was built to integrate the functions to allow engineers to expand the knowledge graphs by inputting learning statements. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Applications for Smart Manufacturing and Industry 4.0)
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