Advances in Digital Manufacturing

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5892

Special Issue Editors

Core Department, National and Kapodistrian University of Athens, 34400 Psachna Eyvoias, Greece
Interests: CNC; additive and subtractive manufacturing; optimization algorithms
Department of Machine Manufacturing Technology, Gheorghe Asachi Technical University of Iasi, 700059 Iasi, Romania
Interests: processing and characterization of polymeric and biodegradable materials; management of industrial manufacturing projects
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Special Issue Information

Dear Colleagues,

The digital transformation of the manufacturing sector has pushed the envelope and opened new gateways into the way that the overall manufacturing processes and manufacturing systems are perceived and utilized in the global economy. With the constant development of new and advanced digital techniques in the design of parts and assemblies, in additive, subtractive and hybrid process modeling and optimization in terms of productivity, product quality and efficiency, manufacturing is considered, now more than ever, a key factor in the advancement of science and technology that supports human activity in a circular and sustainable manner. However, with the increased demand for automating all processes involved in production, while at the same time keeping final product costs low, the applicability, adoption and efficiency of these digital technologies is proven to be a challenge.

The aim of this Special Issue is the “Advances in Digital Manufacturing”. Its purpose is to present state-of-the-art and novel concepts in design and manufacturing digitalization and to re-address well-known concepts and applications in novel ways, as well as to introduce innovative approaches regarding the processes and machinery that can solve real-life problems and problems that are expected to arise in the near future. Researchers and academics from around the world are invited to submit their work, substantiated theoretical, simulation-based, experimental and/or practical research, concerning engineering and its applications, addressing, but not limited to, the following topics:

  • Manufacturing process modeling, digital twins and optimization;
  • Smart manufacturing: modeling, devices, applications, operations, infrastructure and connectivity;
  • Additive manufacturing and hybrid manufacturing processes and machines;
  • Design for additive, subtractive and hybrid manufacturing;
  • Modern techniques of design optimization and applications;
  • Smart and reconfigurable machine tools;
  • Micro-manufacturing and micro-fabrication.

Prof. Dr. Agathoklis A. Krimpenis
Prof. Dr. Angelos P. Markopoulos
Prof. Dr. Dumitru Nedelcu
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

  • CNC
  • 3D printing
  • smart manufacturing
  • optimization
  • hybrid machine tools
  • manufacturing systems

Published Papers (3 papers)

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Research

16 pages, 2329 KiB  
Article
Overview of Selective Laser Melting for Industry 5.0: Toward Customizable, Sustainable, and Human-Centric Technologies
by Ramin Rahmani, Javad Karimi, Pedro R. Resende, João C. C. Abrantes and Sérgio I. Lopes
Machines 2023, 11(5), 522; https://doi.org/10.3390/machines11050522 - 01 May 2023
Cited by 5 | Viewed by 2267
Abstract
Industry 5.0 combines automation/digitalization with human capabilities to create a more intuitive, interactive, and sustainable working environment. Additive manufacturing, widely known as 3D printing, is a key technology used to increase customization and efficiency and reduce waste in manufacturing. Industry 5.0 enables manufacturers [...] Read more.
Industry 5.0 combines automation/digitalization with human capabilities to create a more intuitive, interactive, and sustainable working environment. Additive manufacturing, widely known as 3D printing, is a key technology used to increase customization and efficiency and reduce waste in manufacturing. Industry 5.0 enables manufacturers to create environmentally sustainable and consumer-centric products. However, there is a lack of studies on the introduction of AM technologies to Industry 5.0. The present study investigates the use of additive manufacturing for the fabrication of metallic parts/assemblies and the correlation between human-centric technologies, additive manufacturing, and environmental sustainability. Effective communication between these components is the key to achieving the goals of Industry 5.0, and the important parameters are shown in this article. The present work is focused on an overview and the impact of the futuristic subdivision of additive manufacturing applied to the fabrication of metallic parts/assemblies, more specifically, the 3D printing of challenging alloys or composites (such as copper alloys and/or composites with hard particles). Full article
(This article belongs to the Special Issue Advances in Digital Manufacturing)
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14 pages, 1732 KiB  
Article
Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment
by Constantine A. Kyriakopoulos, Ilias Gialampoukidis, Stefanos Vrochidis and Ioannis Kompatsiaris
Machines 2023, 11(4), 499; https://doi.org/10.3390/machines11040499 - 21 Apr 2023
Cited by 1 | Viewed by 1395
Abstract
Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of [...] Read more.
Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product’s physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline’s output. An innovative framework for the early detection of a product’s physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice. Full article
(This article belongs to the Special Issue Advances in Digital Manufacturing)
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12 pages, 4969 KiB  
Article
Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System
by Zhaolong Zhu, Dong Jin, Zhanwen Wu, Wei Xu, Yingyue Yu, Xiaolei Guo and Xiaodong (Alice) Wang
Machines 2022, 10(7), 567; https://doi.org/10.3390/machines10070567 - 14 Jul 2022
Cited by 18 | Viewed by 1311
Abstract
This work focused on changes in surface roughness under different cutting conditions for improving the cutting quality of beech wood during milling. A response surface methodology and an adaptive network-based fuzzy inference system were adopted to model and establish the relationship between milling [...] Read more.
This work focused on changes in surface roughness under different cutting conditions for improving the cutting quality of beech wood during milling. A response surface methodology and an adaptive network-based fuzzy inference system were adopted to model and establish the relationship between milling conditions and surface roughness. Moreover, the significant impact of each factor and two-factor interactions on surface roughness were explored by analysis of variance. The specific objective of this work was to find milling parameters for minimum surface roughness, and the optimal milling condition was determined to be a rake angle of 15°, a spindle speed of 3357 r/min and a depth of cut of 0.62 mm. These parameters are suggested to be used in actual machining of beech wood with respect of smoothness surface. Full article
(This article belongs to the Special Issue Advances in Digital Manufacturing)
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