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JMMP
Special Issues
Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing
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Special Issue "Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing"

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 1 February 2024 | Viewed by 3079

Special Issue Editor

Dr. Antonio Padovano

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy
Interests: Industry 4.0; simulation modeling; smart operators; sustainable production and logistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 and the advancements in smart manufacturing and digital technologies are revolutionizing the manufacturing industry. With the integration of these technologies, manufacturing processes can be improved in terms of productivity, quality, sustainability, and cost-effectiveness. This Special Issue aims to showcase the latest research and developments in Industry 4.0 and smart materials processing for enhanced manufacturing. This Special Issue seeks to bring together contributions that address the challenges and opportunities of using Industry 4.0 and smart materials processing to support manufacturing processes. The purpose of this Special Issue is to provide a comprehensive overview of the latest research and practical applications of Industry 4.0 and smart materials processing in the manufacturing sector. The aim is to promote interdisciplinary collaboration and knowledge exchange among researchers, practitioners, and policymakers in the field of manufacturing and materials processing.

Topics of interest include but are not limited to:

  • Additive manufacturing and 3D printing technologies for smart materials processing;
  • Cyber–physical systems and the Industrial Internet of Things (IIoT) in Industry 4.0;
  • Robotics and automation for smart materials processing;
  • Smart sensors and data analytics for process monitoring and optimization;
  • Digital twin and virtual reality for manufacturing simulations and design;
  • Sustainable manufacturing and green technologies for Industry 4.0;
  • Materials innovation and advances in smart materials processing.

Dr. Antonio Padovano
Guest Editor

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. Journal of Manufacturing and Materials Processing 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 1600 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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14 pages, 2479 KiB  
Article
Innovative Smart Drilling with Critical Event Detection and Material Classification
by
Kantawatchr Chaiprabha
and
Ratchatin Chanchareon
J. Manuf. Mater. Process. 2023, 7(5), 155; https://doi.org/10.3390/jmmp7050155 - 23 Aug 2023
Viewed by 973
Abstract
This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the [...] Read more.
This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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Review

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48 pages, 8831 KiB  
Review
Tool Wear Monitoring with Artificial Intelligence Methods: A Review
by
Roberto Munaro
,
Aldo Attanasio
and
Antonio Del Prete
J. Manuf. Mater. Process. 2023, 7(4), 129; https://doi.org/10.3390/jmmp7040129 - 11 Jul 2023
Viewed by 1813
Abstract
Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge [...] Read more.
Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge makes it unable to guarantee the quality required during machining. Knowing and measuring the wear of tools is possible through artificial intelligence (AI), a branch of information technology that, by interpreting the behaviour of the tool, predicts its wear through intelligent systems. AI systems include techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process. The aim of this research work is to provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods. The last few years mainly refer to the last ten years, but with a few exceptions, for a better explanation of the topics covered. Therefore, the review identifies, in addition to the methods, the industrial sector to which the scientific article refers, the type of processing, the material processed, the tool used and the type of wear calculated. Publications are described in accordance with PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols). Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Materials Processing for Enhanced Manufacturing)
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