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Processes
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Artificial Intelligence Technologies and Methods for Green Manufacturing
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Artificial Intelligence Technologies and Methods for Green Manufacturing

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 25 April 2026 | Viewed by 840

Special Issue Editors

Prof. Dr. Zhigang Jiang

E-Mail Website
Guest Editor
Department of Industrial Engineering, School of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: green manufacturing and remanufacturing; energy efficient manufacturing; low-carbon design and manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Wang

E-Mail Website
Guest Editor
School of Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, UK
Interests: intelligent and sustainable manufacturing; sustainable design; remanufacturing; process and operation management; computer-aided design and manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence offers new technologies and pathways for green manufacturing, demonstrating significant potential in areas such as energy consumption optimization, intelligent disassembly, and low-carbon process planning. This Special Issue explores how to leverage the latest artificial intelligence (AI) technologies and methods to enhance the potential of green manufacturing, and delves into the patterns of influence that multimodal data exerts on green manufacturing processes. The focus is on key challenges for AI in green manufacturing scenarios: knowledge graphs supporting decision-making and optimization, traceability in dynamic environments, and the development of compact models tailored to specific tasks. Researchers and industry practitioners are invited to submit original research and reviews to share the latest research findings and practical experiences regarding AI in the field of green manufacturing.

The goal of this Research Topic is to explore AI methods and technologies, using both solid theoretical development and practical importance to implement green manufacturing. The central theme of the proposed Research Topic is AI Technologies for Green Manufacturing, where intelligent analysis, control, and optimization are the focus areas, and broad aspects and issues will be carefully discussed. Topics to be covered include, but are not limited to, the following:

  • Green design methods driven by AI, including knowledge graphs, intelligent evaluation, and generative design algorithms, etc.;
  • Low-carbon manufacturing driven by large language models or optimization algorithms;
  • AI optimization of green manufacturing processes, including intelligent inspection, process optimization, and virtual manufacturing, etc.;
  • Green workshop scheduling driven by big data analysis and intelligent optimization;
  • Custom language models driving low-carbon remanufacturing;
  • Remanufacturing process planning driven by knowledge graphs and optimization algorithms;
  • Reverse supply chain driven by AI.

Prof. Dr. Zhigang Jiang
Dr. Yan Wang
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. Processes 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

  • green manufacturing
  • artificial intelligence
  • remanufacturing process
  • green logistics
  • reverse supply chain

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Published Papers (2 papers)

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Research

19 pages, 1304 KB  
Article
Low-Carbon, High-Efficiency, and High-Quality Equipment Selection for Milling Process Based on New Quality Productivity Orientation
by Wenyue Qu and Zhongjin Ni
Processes 2025, 13(9), 2935; https://doi.org/10.3390/pr13092935 - 14 Sep 2025
Viewed by 362
Abstract
Selecting appropriate milling equipment is an important means to reduce carbon emissions and improve the efficiency of part-machining processes, as the process of machining the same part on different milling machines varies greatly. Traditional milling machine selection approaches only involve a static analysis [...] Read more.
Selecting appropriate milling equipment is an important means to reduce carbon emissions and improve the efficiency of part-machining processes, as the process of machining the same part on different milling machines varies greatly. Traditional milling machine selection approaches only involve a static analysis of their advantages and disadvantages without considering the dynamic changes in the production process, making them difficult to adapt to the requirements of the new era. To solve this problem, we establish a milling machine selection model based on the new quality productivity (NQP) concept; propose a calculation method considering carbon emissions, efficiency, and quality (expressed as surface roughness) in the production process; and quantitatively analyze the process objectives of different milling machines according to the changes in the machining process. The spindle speed, feed rate, cutting width, and cutting depth are taken as the optimization variables, and the cutting parameters are optimized using the egret swarm algorithm (ESA) to obtain the Pareto frontier solutions providing low-carbon and high efficiency process parameters. The method was verified through a plane milling example. After ESA optimization, the processing time was increased by 5.6%, the surface roughness accuracy was improved by 12.9%, and the carbon emissions were reduced by 13.1%, demonstrating the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies and Methods for Green Manufacturing)
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21 pages, 1888 KB  
Article
An Intelligent Design Method for Product Remanufacturing Based on Remanufacturing Information Reuse
by Chao Ke, Yichen Deng, Shijie Liu and Hongwei Cui
Processes 2025, 13(9), 2899; https://doi.org/10.3390/pr13092899 - 10 Sep 2025
Viewed by 354
Abstract
Design for remanufacturing (DfRem) is a green design mode that ensures good remanufacturability at the end-of-life (EOL) of the product. However, the diversity of service environments and operating modes makes it difficult to generate accurate DfRem solutions for the smooth implementation of remanufacturing. [...] Read more.
Design for remanufacturing (DfRem) is a green design mode that ensures good remanufacturability at the end-of-life (EOL) of the product. However, the diversity of service environments and operating modes makes it difficult to generate accurate DfRem solutions for the smooth implementation of remanufacturing. Moreover, the historical remanufacturing process contains a great deal of information conducive to DfRem. It will greatly enhance the efficiency and accuracy of remanufacturing design by feeding effective remanufacturing information back into the product design process. Unfortunately, there is a lack of direct correlation between them, which prevents remanufacturing information from effectively guiding DfRem. To improve the accuracy of DfRem solutions and the utilization rate of remanufacturing information, an intelligent design method for product remanufacturing based on remanufacturing information reuse is proposed. Firstly, rough set theory (RST) is used to identify key remanufacturability demand, and the quality function development (QFD) is used to establish a relationship between remanufacturability demand and engineering characteristics, which can accurately obtain the design objectives. Then, the correlation between remanufacturability demand, remanufacturing information, and DfRem parameters is analyzed, and the ontology technology is applied to construct the DfRem knowledge by ingratiating remanufacturing information. In addition, case-based reasoning (CBR) is applied to search for design cases from DfRem knowledge that best match the design objectives, and gray relational analysis (GRA) is used to calculate the similarity between design knowledge. Finally, the feasibility of the method is verified by taking an ordinary lathe as an example. This method has been implemented as a DfRem interface application using Visual Studio 2022 and Microsoft SQL Server 2022, and the research results indicate that this design method can accurately generate a reasonable DfRem scheme. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies and Methods for Green Manufacturing)
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