Editorial Board Members’ Collection Series: Intelligent Manufacturing

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: applied machine vision; control systems; manufacturing processes; robotic welding
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and Engineering, Shanghai Jiao Tong University (SJTU), Shanghai 200240, China
Interests: intelligentized robot welding; intelligent control of welding dynamical process; intelligent welding manufacturing and systems

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Guest Editor
Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Interests: Industry 4.0; 3D Printing; sustainable product development; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce that the Journal of Manufacturing and Materials Processing (JMMP) plans to publish a Special Issue entitled "Editorial Board Members' Collection Series: Intelligent Manufacturing". This will be a collection of feature papers contributed by our Editorial Board Members and their invitees. JMMP achieved a stage victory this year with its new CiteScore of 4.8 and the confirmation of receiving first Impact Factor in June 2023.

All feature papers in this Collection will receive full waivers while enjoying benefits stemming from open access and JMMP promotions and highlights. The Special Issue will be opened by an Editorial to highlight and introduce the corresponding authors and their research achievements for each of the feature papers included.

Feature papers being sought for this Collection can be comprehensive review/tutorial articles that identify challenges, provide historical views, analyze past and ongoing efforts, and suggest future directions. They can also be research papers characterized by novel concepts and innovative developments. Papers from all related areas are within the scope of this Collection, including but not limited to:

  • Monitoring, sensing, dynamic modeling, and control of manufacturing processes;
  • Robotic manufacturing systems/processes;
  • Human–robotic collaborative manufacturing systems/processes;
  • Use of machine vision, machine learning, data fusion, optimization, and other advanced techniques/algorithms to improve the operation, quality, and productivity of manufacturing processes;
  • Sensing, monitoring, and control of additive manufacturing, including wire arc additive manufacturing processes.

If you have not been invited and believe that you are among the world leaders in intelligent manufacturing, please contact/email us for invitations.

Prof. Dr. YuMing Zhang
Prof. Dr. Shanben Chen
Prof. Dr. Sharifu Ura
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. 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 1800 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.

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Published Papers (1 paper)

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Research

30 pages, 7977 KiB  
Article
Towards Developing Big Data Analytics for Machining Decision-Making
by Angkush Kumar Ghosh, Saman Fattahi and Sharifu Ura
J. Manuf. Mater. Process. 2023, 7(5), 159; https://doi.org/10.3390/jmmp7050159 - 2 Sep 2023
Cited by 2 | Viewed by 1910
Abstract
This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, [...] Read more.
This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consist of five integrated system components: (1) Data Preparation System, (2) Data Exploration System, (3) Data Visualization System, (4) Data Analysis System, and (5) Knowledge Extraction System. The functional requirements of the integrated system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry Electrical Discharge Machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In summary, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out. Full article
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