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Recent Advances in Smart Design and Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 5446

Special Issue Editors


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Guest Editor
School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: design science in product design and development; engineering/design informatics for managing/supporting digital design and manufacturing; human factors and management of human performance
Special Issues, Collections and Topics in MDPI journals
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: human–robot collaboration; smart product-service systems; engineering informatics; smart manufacturing systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Interests: industrial knowledge graph; knowledge engineering and knowledge management
Special Issues, Collections and Topics in MDPI journals
HP-NTU Digital Manufacturing Corporate Lab, School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 637460, Singapore
Interests: user experience; kansei engineering; human-centered design and applied ergonomics

Special Issue Information

Dear Colleagues,

The rapid development of advanced technologies has dramatically changed the manufacturing industry. From part design to 3D fabrication, production planning and scheduling, the lines between the cyber, physical, and human components are blurring in the digital factory. This brings new challenges and opportunities to different stakeholders for service innovation and sustainability.   

One notable trend in Industry 4.0 is the management of heterogeneous information (e.g., customer requirements, sensor data) that is constantly collected and exchanged throughout the design and manufacturing process. Many have attempted to leverage artificial intelligence and machine-learning techniques to make devices and operations smarter and more resilient, especially during the pandemic.

This Special Issue welcomes original research articles that apply interdisciplinary/hybrid approaches to explore recent advances in the relevant areas, including but not limited to the following topics. Papers should illustrate novel methodologies with industry-focused case studies or demonstrate noteworthy generality and scalability.

Prof. Dr. Chun-Hsien Chen
Prof. Dr. Pai Zheng
Dr. Xinyu Li
Dr. Jo-Yu Kuo
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 product-service systems
  • manufacturing servitization
  • cloud manufacturing
  • artificial intelligence
  • knowledge management
  • digital twin
  • sustainability in Industry 4.0
  • design for additive manufacturing
  • user experience
  • human factors and ergonomics

Published Papers (3 papers)

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23 pages, 3773 KiB  
Article
A Two-Phase Iterative Mathematical Programming-Based Heuristic for a Flexible Job Shop Scheduling Problem with Transportation
by Che Han Lim and Seung Ki Moon
Appl. Sci. 2023, 13(8), 5215; https://doi.org/10.3390/app13085215 - 21 Apr 2023
Cited by 2 | Viewed by 1560
Abstract
In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry [...] Read more.
In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry 4.0. In this article, a two-phase iterative mathematical programming-based heuristic is proposed to minimize makespan using a machine-operation assignment centric decomposition scheme. The first phase approximates the FJSPT through an augmented flexible job shop scheduling problem (FJSP + T) that reduces the solution space while serving as a heuristic in locating good machine-operation assignments. In the second phase, a job shop scheduling problem with transportation (JSPT) network is constructed from these assignments and solved for the makespan. Compared to prior JSPT implementations, the proposed JSPT model considers job pre-emption, which is instrumental in enabling this FJSPT implementation to outperform certain established benchmarks, confirming the importance of considering job pre-emption. Results indicate that the proposed approach is effective, robust, and competitive. Full article
(This article belongs to the Special Issue Recent Advances in Smart Design and Manufacturing)
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25 pages, 11199 KiB  
Article
A Research on Image Semantic Refinement Recognition of Product Surface Defects Based on Causal Knowledge
by Weibin Zhuang, Taihua Zhang, Liguo Yao, Yao Lu and Panliang Yuan
Appl. Sci. 2022, 12(17), 8828; https://doi.org/10.3390/app12178828 - 2 Sep 2022
Viewed by 1597
Abstract
The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis [...] Read more.
The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis for solving the problem of product defects in design and manufacturing. Therefore, in this paper, we propose an image semantic refinement recognition method based on causal knowledge for product surface defects. Firstly, an improved ResNet was designed to improve the image classification effect. Then, the causal knowledge graph of surface defects was constructed and stored in Neo4j. Finally, a visualization platform for causal knowledge analysis was developed to realize the causal visualization of the defects in the causal knowledge graph driven by the output data of the network model. In addition, the method is validated by the surface defects dataset. The experimental results show that the average accuracy, recall, and precision of the improved ResNet are improved by 11%, 8.15%, and 8.3%, respectively. Through the application of the visualization platform, the cause results obtained are correct by related analysis and comparison, which can effectively represent the cause of aluminum profile surface defects, verifying the effectiveness of the method proposed in this paper. Full article
(This article belongs to the Special Issue Recent Advances in Smart Design and Manufacturing)
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24 pages, 8223 KiB  
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The Research of Complex Product Design Process Model under the Concept of Self-Recovery
by Peng Zhang, Yunpeng Su, Hanrui Niu, Yaru Wang, Yuchen Zhang and Chuankai Zhang
Appl. Sci. 2022, 12(20), 10270; https://doi.org/10.3390/app122010270 - 12 Oct 2022
Viewed by 1175
Abstract
The working environment of contemporary mechanical products is becoming more complex, and the working conditions are becoming more extreme. This has led to a significant increase in the frequency of problems in mechanical products. In order to reduce the frequency of human repair [...] Read more.
The working environment of contemporary mechanical products is becoming more complex, and the working conditions are becoming more extreme. This has led to a significant increase in the frequency of problems in mechanical products. In order to reduce the frequency of human repair after problems, the application of the self-recovery concept has become a hot research topic in the area of smart design. However, the current application of the self-recovery concept is mostly limited to the structural and parametric levels, with less research at the functional level, which may lead to a waste of resources within products. To solve this problem, this research combines the functional-level product research method with the self-recovery concept and establishes a design process model of complex products under functional self-recovery. This model extends the application scope of the self-recovery concept and improves the efficiency of resource utilization in the product. The design process model has six steps. First, according to the user requirements and the existing product, the initial function solving is carried out, and the initial function model of the product is established. Next, the main functions of the product are determined based on the initial function model of the product. Then, according to the determined main functions of the product, combined with the parameters marked in the function structure, the self-diagnosis function is designed. After that, the LT matrix and effect library are used to design the self-regulation function corresponding to the main functions, and the parameters are used to screen the self-regulation function design scheme. Finally, according to the design scheme of the self-diagnosis function and self-regulation function, the functional period oriented to self-recovery is constructed to ensure the realization of the main functions of the product. The effectiveness of the design process model is proved through the design process of an intelligent photovoltaic power generation system at the end of the paper. Full article
(This article belongs to the Special Issue Recent Advances in Smart Design and Manufacturing)
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