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Processes
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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: closed (31 March 2025) | Viewed by 6559

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

15 pages, 10121 KiB  
Article
A Study on Microstructure-Property Relationships and Notch-Sensitive Fracture Behavior of X80 Steel Welds
by Yangfan Zou, Lifeng Li, Shuxin Zhang, Xiangzhen Yan and Shuyi Xie
Processes 2025, 13(3), 763; https://doi.org/10.3390/pr13030763 - 6 Mar 2025
Viewed by 462
Abstract
X80 steel pipelines are widely used in oil and gas transportation, and the quality and fracture behavior of the girth weld have an important influence on the safety and performance of the pipeline. This study presents a comprehensive investigation into the microstructure, mechanical [...] Read more.
X80 steel pipelines are widely used in oil and gas transportation, and the quality and fracture behavior of the girth weld have an important influence on the safety and performance of the pipeline. This study presents a comprehensive investigation into the microstructure, mechanical properties, and fracture characteristics of X80 steel welded joints. Through microstructure analysis and mechanical testing, the hardness, impact, and tensile properties of the base metal, heat-affected zone, and weld zone are evaluated. Digital Image Correlation (DIC) technology is employed to scrutinize the strain behavior under quasi-static tensile tests for both smooth and notched round bar specimens, providing a detailed strain distribution analysis. The findings indicate that, while X80 welded joints are well-formed without significant defects, the hardness and impact properties vary across different zones, with the base metal exhibiting the highest impact toughness and the weld zone the lowest. Notched tensile tests reveal that the presence and geometry of notches significantly alter the stress state and deformation characteristics, influencing the fracture mode. The DIC analysis further elucidates the strain concentration and localization behavior in the weld zone, highlighting the importance of notch size in determining the load-bearing capacity and ductility of the welded joints. This study contributes to a deeper understanding of the fracture mechanics in X80 pipeline girth welds and offers valuable insights for the optimization of welding practices and the assessment of pipeline integrity. Full article
(This article belongs to the Special Issue Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control)
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13 pages, 8297 KiB  
Article
The Impact of Weld Repairs on the Microstructure and Mechanical Integrity of X80 Pipelines in Oil and Gas Transmission
by Lifeng Li, Lixia Zhu, Xiangzhen Yan, Haidong Jia, Shuyi Xie and Yangfan Zou
Processes 2025, 13(2), 512; https://doi.org/10.3390/pr13020512 - 12 Feb 2025
Viewed by 455
Abstract
The integrity of oil and gas pipelines is critical to energy transportation and has significant implications for national energy security. This study employs finite-element numerical simulation to investigate the impact of multiple repairs on the microstructure and mechanical integrity of X80 pipeline girth [...] Read more.
The integrity of oil and gas pipelines is critical to energy transportation and has significant implications for national energy security. This study employs finite-element numerical simulation to investigate the impact of multiple repairs on the microstructure and mechanical integrity of X80 pipeline girth welds. The effects of varying repair iterations on the microstructure, toughness, and loading capacity of high-strength steel pipes were analyzed. The results revealed that the microstructure of the welded joint remained unchanged across different repair instances, but the toughness, optimized by welding heat input, diminished after three repairs due to grain growth from repeated thermal cycles. Specifically, the impact toughness of the welding line, fusion line, and adjacent areas decreased significantly after three repairs, with the toughness of the welding line dropping to 25 J and the fusion line dropping to 30 J. The hardness of the welded joint decreased with repairs, and the dispersion of hardness increased. The average hardness at the welding line decreased from 25 HV to 20 HV after three repairs. Residual stress in the repaired girth weld was highest in the filling welding layer, increasing with the number of repairs. The loading capacity of the girth weld significantly decreased after the first repair (by 12.1%) and continued to decrease with additional repairs (15.3% after the second repair and 16.7% after the third repair). It is concluded that X80 pipeline girth welds should be repaired no more than twice to maintain optimal structural integrity. Full article
(This article belongs to the Special Issue Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control)
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27 pages, 6978 KiB  
Article
Tool Wear State Monitoring in Titanium Alloy Milling Based on Wavelet Packet and TTAO-CNN-BiLSTM-AM
by Zongshuo Yang, Li Li, Yunfeng Zhang, Zhengquan Jiang and Xuegang Liu
Processes 2025, 13(1), 13; https://doi.org/10.3390/pr13010013 - 24 Dec 2024
Viewed by 873
Abstract
To effectively monitor the nonlinear wear variation of tools during the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network [...] Read more.
To effectively monitor the nonlinear wear variation of tools during the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Firstly, vibration signals from the machine tool spindle are acquired and subjected to the wavelet packet transform (WPT) to extract multi-frequency band energy features as model inputs. Then, the CNN and BiLSTM modules capture the features and temporal relationships of the input signals. Finally, introduction of the AM, combined with the TTAO algorithm, automatically extracts deep features, overcoming issues such as local optima and slow convergence in traditional neural networks, thereby enhancing the accuracy and efficiency of tool wear state recognition. The experimental results demonstrate that the proposed model achieves an average accuracy rate of 98.649% in predicting tool wear states, outperforming traditional backpropagation (BP) networks and standard CNN models. Full article
(This article belongs to the Special Issue Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control)
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16 pages, 3092 KiB  
Article
Monitoring, Control and Optimization of Laser Micro-Perforation Process for Automotive Synthetic Leather Parts
by Alexandru-Nicolae Rusu, Dorin-Ion Dumitrascu and Adela-Eliza Dumitrascu
Processes 2024, 12(6), 1275; https://doi.org/10.3390/pr12061275 - 20 Jun 2024
Cited by 1 | Viewed by 1465
Abstract
This paper presents a comparative analysis of the laser operating power (P1 and P2) and synthetic leather thickness to achieve the optimal quality of components in the airbag area, produced through micro-perforation laser processing. Within the study, various laser power settings and material [...] Read more.
This paper presents a comparative analysis of the laser operating power (P1 and P2) and synthetic leather thickness to achieve the optimal quality of components in the airbag area, produced through micro-perforation laser processing. Within the study, various laser power settings and material thicknesses were investigated to determine the combinations that ensure the best component performance. The experimental results indicate that setting the laser to 25% of its total power (P1, P2) of two kilowatts (kW) represents the optimal parameter setup to achieve parts of superior quality. This configuration is not significantly influenced by the material thickness, suggesting important versatility in practical applications. The overall results indicate the significant influence of the laser power level on micro-perforation processing. The normal analysis of means (ANOM) and factorial design (DOE) provide significant evidence for an interaction, highlighting that the effects of one laser power factor depend on the level of the other laser power factor. These findings are essential in improving production processes, as they allow for the manufacture of airbag components with high precision and consistency, minimizing the risks of material deformation or damage. Thus, not only is compliance with safety standards ensured, but the economic efficiency of the production process is also enhanced. Full article
(This article belongs to the Special Issue Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control)
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18 pages, 4288 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
Cited by 3 | Viewed by 1330
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)
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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 1305
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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