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Processes
Special Issues
Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control
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Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 731

Special Issue Editors

Prof. Dr. Li Li

E-Mail Website
Guest Editor
College of Engineering and Technology, Southwest University, Chongqing 400715, China
Interests: intelligent manufacturing; machine learning; deep learning; data-driven modeling
Dr. Wei Cai

E-Mail Website
Guest Editor
College of Engineering and Technology, Southwest University, Chongqing 400715, China
Interests: intelligent manufacturing; green manufacturing; manufacturing system engineering; 3D printing and additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Lingling Li

E-Mail Website
Guest Editor
College of Engineering and Technology, Southwest University, Chongqing 400715, China
Interests: intelligent manufacturing; collaborative optimization; manufacturing systems; deep learning; reinforcement learning

Special Issue Information

Dear Colleagues,

Given the focus on green and efficiency-enhancing data modeling coupled with analysis technology derived from big data and artificial intelligence, there exists a pressing need for intensive research in the domain of data-driven digital workshop operations and intelligent decision support technology. Said research is crucial for achieving enhanced green practices and heightened efficiency within discrete manufacturing enterprises' production processes. For this Special Issue on "Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control", we invite the submission of high-quality works focusing on the latest novel advances in the optimization of manufacturing processes.

Topics include, but are not limited to:

  • New optimization control techniques to investigate the multi-axis machining processes of complex parts.
  • Investigations of energy efficiency involving electricity, heat, gas, waste, and mass transfer in multi-axis machining systems, considering multi-source heterogeneous data.
  • New model approaches to describing multi-axis machining energy efficiency, including both local phenomena (such as the energy and other information flow of each axis) and the total calculation of multi-axis integrated energy consumption.
  • Application of advanced computer science techniques, such as machine learning and deep learning, to explore the energy efficiency optimization behavior of multi-axis processing.

Prof. Dr. Li Li
Dr. Wei Cai
Dr. Lingling Li
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

  • intelligent manufacturing
  • machine learning
  • deep learning
  • data-driven modeling
  • sustainable machining

Published Papers (2 papers)

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Research

18 pages, 1200 KiB  
Article
Construction Method and Practical Application of Oil and Gas Field Surface Engineering Case Database Based on Knowledge Graph
by Taiwu Xia, Zhixiang Dai, Yihua Zhang, Feng Wang, Wei Zhang, Li Xu, Dan Zhou and Jun Zhou
Processes 2024, 12(6), 1088; https://doi.org/10.3390/pr12061088 - 25 May 2024
Viewed by 221
Abstract
To address the challenge of quickly and efficiently accessing relevant management experience for a wide range of ground engineering construction projects, supporting project management with information technology is crucial. This includes the establishment of a case database and an application platform for intelligent [...] Read more.
To address the challenge of quickly and efficiently accessing relevant management experience for a wide range of ground engineering construction projects, supporting project management with information technology is crucial. This includes the establishment of a case database and an application platform for intelligent search and recommendations. The article leverages Optical Character Recognition (OCR) technology, knowledge graph technology, and Natural Language Processing (NLP) technology. It explores the mechanisms for classifying construction cases, methods for constructing a case database, structuring case data, intelligently retrieving and matching cases, and intelligent recommendation methods. This research forms a complete, feasible, and scalable method for deconstructing, storing, intelligently retrieving, and recommending construction cases, providing a theoretical basis for the establishment of a construction case database. It aims to meet the needs of digital project management and intelligent decision-making support in the oil and gas sector, thereby enhancing the efficiency and accuracy of project construction. This work offers a theoretical foundation for the development of an intelligent management platform for ground engineering projects in the oil and gas industry, supporting the sector’s digital transformation and intelligent development. Full article
(This article belongs to the Special Issue Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control)
24 pages, 5522 KiB  
Article
Characterization and Performance Evaluation of Digital Light Processing 3D Printed Functional Anion Exchange Membranes in Electrodialysis
by Xue Yu, Hongyi Yang, Xinran Lv, Xin Zhang, Veeriah Jegatheesan, Xiaobin Zhou and Yang Zhang
Processes 2024, 12(6), 1043; https://doi.org/10.3390/pr12061043 - 21 May 2024
Viewed by 312
Abstract
With the rapid development of 3D printing technologies, more attention has been focused on using 3D printing for the fabrication of membranes. This study investigated the application of digital light processing (DLP) 3D printing combined with quaternization processes to develop dense anion exchange [...] Read more.
With the rapid development of 3D printing technologies, more attention has been focused on using 3D printing for the fabrication of membranes. This study investigated the application of digital light processing (DLP) 3D printing combined with quaternization processes to develop dense anion exchange membranes (AEMs) for electrodialysis (ED) separation of Cl− and SO42− ions. It was discovered that at optimal curing times of 40 min, the membrane pore density was significantly enhanced and the surface roughness was reduced, and this resulted in an elevation of desalination rates (97.5–98.7%) and concentration rates (165.8–174.1%) of the ED process. Furthermore, increasing the number of printed layers improved the membranes’ overall polymerization and performance, with double-layer printing showing superior ion flux. This study also highlights the impact of the polyethylene glycol diacrylate (PEGDA) molecular weight on membrane efficacy, where PEGDA-700 outperformed PEGDA-400 in ion transport capabilities and desalination efficiency. Additionally, higher 4-vinylbenzyl chloride (VBC) content improved the quaternary ammonium group concentration and membrane conductivity, and hence elevated the ED performance. Under optimized conditions, DLP 3D printed membranes demonstrated exceptional selectivity of 24.0 for Cl−/SO42− and a selective purity of 81.4%. With a current density of 400 A/m2, the current efficiency and energy consumption were in the range of 82.4% to 99.7%, and 17.2 to 25.4 kW‧h‧kg−1, respectively, showcasing the potential of advanced manufacturing techniques in creating efficient and functional ion exchange membranes. Full article
(This article belongs to the Special Issue Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control)
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