Editorial Board Members’ Collection Series: "Smart Manufacturing"

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 3102

Special Issue Editors

Department of Product and Systems Design Engineering, University of Western Macedonia, 50100 Kila Kozani, Greece
Interests: computational design; CAD/CAM/CAE; digital manufacturing; product design; FEA; industry 4.0; prototyping; reverse engineering
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Interests: design; manufacturing; MEMS; dynamic systems; biorobotics; biosensors; bioacutators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Collection entitled Editorial Board Members’ Collection Series: Smart Manufacturing, which will collect papers invited by the Editorial Board Members.

The aim of this Collection is to provide a venue for networking and communication between Machines and scholars in the field of smart manufacturing. All papers will be fully open access upon publication after peer review.

Potential topics includes but not limited to:

  • Machine learning (ML) and artificial intelligence (AI) in manufacturing
  • Collaborative robots (robot–robot, human–robot, etc.)
  • Computational design and CAD/CAM systems
  • Virtual/Augmented/Extended/Mixed reality (VR/AR/XR/MR)
  • Smart manufacturing processes and smart factories
  • Cyberphysical systems and digital twins (DT)
  • Digital manufacturing (DM)
  • Internet of Things (IoT)
  • Smart technology and smart materials
  • Cloud computing and big data analysis
  • Reverse Engineering
  • Industry 4.0 applications
  • Additive/Hybrid manufacturing
  • 3D prototyping and simulation
  • Modern machining and manufacturing
  • Intelligent machining systems and processes
  • Digital quality management
  • Data transformation and visualization
  • Sustainable and resilient manufacturing systems
  • Human factors in manufacturing

Prof. Dr. Panagiotis Kyratsis
Prof. Dr. Wenjun (Chris) Zhang
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.

Published Papers (2 papers)

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Research

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20 pages, 4823 KiB  
Article
Supply Chain 4.0: A Machine Learning-Based Bayesian-Optimized LightGBM Model for Predicting Supply Chain Risk
Machines 2023, 11(9), 888; https://doi.org/10.3390/machines11090888 - 04 Sep 2023
Viewed by 1158
Abstract
In today’s intricate and dynamic world, Supply Chain Management (SCM) is encountering escalating difficulties in relation to aspects such as disruptions, globalisation and complexity, and demand volatility. Consequently, companies are turning to data-driven technologies such as machine learning to overcome these challenges. Traditional [...] Read more.
In today’s intricate and dynamic world, Supply Chain Management (SCM) is encountering escalating difficulties in relation to aspects such as disruptions, globalisation and complexity, and demand volatility. Consequently, companies are turning to data-driven technologies such as machine learning to overcome these challenges. Traditional approaches to SCM lack the ability to predict risks accurately due to their computational complexity. In the present research, a hybrid Bayesian-optimized Light Gradient-Boosting Machine (LightGBM) model, which accurately forecasts backorder risk within SCM, has been developed. The methodology employed encompasses the creation of a mathematical classification model and utilises diverse machine learning algorithms to predict the risks associated with backorders in a supply chain. The proposed LightGBM model outperforms other methods and offers computational efficiency, making it a valuable tool for risk prediction in supply chain management. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Smart Manufacturing")
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Review

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48 pages, 1780 KiB  
Review
A Review on Wearable Product Design and Applications
Machines 2024, 12(1), 62; https://doi.org/10.3390/machines12010062 - 16 Jan 2024
Viewed by 1303
Abstract
In recent years, the rapid advancement of technology has caused an increase in the development of wearable products. These are portable devices that can be worn by people. The main goal of these products is to improve the quality of life as they [...] Read more.
In recent years, the rapid advancement of technology has caused an increase in the development of wearable products. These are portable devices that can be worn by people. The main goal of these products is to improve the quality of life as they focus on the safety, assistance and entertainment of their users. The introduction of many new technologies has allowed these products to evolve into many different fields with multiple uses. The way in which the design of wearable products/devices is approached requires the study and recording of multiple factors so that the final device is functional and efficient for its user. The current research presents an in-depth overview of research studies dealing with the development, design and manufacturing of wearable products/devices and applications/systems in general. More specifically, in this review, a comprehensive classification of wearable products/devices in various sectors and applications was carried out, resulting in the creation of eight different categories. A total of 161 studies from the last 13 years were analyzed and commented on. The findings of this review show that the use of new technologies such as 3D scanning and 3D printing are essential tools for the development of wearable products. In addition, many studies observed the use of various sensors through which multiple signals and data could be recorded. Finally, through the eight categories that the research studies were divided into, two main conclusions emerged. The first conclusion is that 3D printing is a method that was used the most in research. The second conclusion is that most research directions concern the safety of users by using sensors and recording anthropometric dimensions. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Smart Manufacturing")
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