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Big-Data-Driven Sustainable Manufacturing
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Big-Data-Driven Sustainable Manufacturing

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 17911

Special Issue Editors

Dr. Ying Zuo

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: service-oriented smart manufacturing; green manufacturing; product energy-efficiency evaluation and optimization; manufacturing carbon neutralization
Dr. Meng Zhang

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: digital twin driven smart manufacturing
Dr. Feng Xiang

E-Mail Website
Guest Editor
Department of Industrial Engineering, School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: service oriented manufacturing; digital twin and sustainable manufacturing

Special Issue Information

Dear Colleagues,

As the most important pillar industry of the national economy, manufacturing is also an industry with high energy consumption and high emission. At present, the growing energy crisis has attracted increasing attention, as the primary conventional energy is faced with exhaustion. Therefore, it is a general trend to realize sustainable manufacturing worldwide for energy conservation, waste elimination, product quality assurance and manufacturing process improvement. In recent years, with the development of advanced sensor technology and Internet of Things, more and more manufacturing data have become available, which shows great potential to endow manufacturing with more intelligence. However, how to efficiently perceive the data, reveal the knowledge hidden behind the data, and take data-driven wise actions to achieve sustainable manufacturing have been plaguing practitioners in industry and researchers in academia.

In this context, this Special Issue intends to provide a forum for researchers around the world to present, discuss, and exchange their latest work on big-data-driven sustainable manufacturing, and to envision developments in the future. We invite researchers to submit contributions in terms of comprehensive reviews, case studies, or research articles with the development of data-based solutions for sustainable manufacturing.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Big-data-driven energy management in smart manufacturing;
  • Big-data-driven carbon neutralization in smart manufacturing;
  • Big-data-driven product lifecycle management;
  • Big-data-driven product quality assurance;
  • Big-data-driven green service management;
  • Big-data in engineering optimization;
  • Industrial applications of big data;
  • New information technology in sustainable manufacturing;
  • Big data and digital twins in sustainable manufacturing;
  • Use of sustainable manufacturing technology in education;
  • Engaging undergraduate students in sustainability research.

We look forward to receiving your contributions.

Dr. Ying Zuo
Dr. Meng Zhang
Dr. Feng Xiang
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. Sustainability 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 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

  • sustainable manufacturing
  • smart manufacturing
  • big data
  • new information technology

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Published Papers (7 papers)

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Research

20 pages, 7279 KiB  
Article
A Novel Training Path to Promote the Ability of Mechanical Engineering Graduates to Practice and Innovate Using New Information Technologies
by Feng Xiang, Junjie Cao, Ying Zuo, Xianyin Duan, Liangxi Xie and Min Zhou
Sustainability 2024, 16(1), 364; https://doi.org/10.3390/su16010364 - 30 Dec 2023
Cited by 1 | Viewed by 1936
Abstract
In the context of the emerging era of smart manufacturing, the concept of sustainability is gaining substantial and widespread attention. Information technology has emerged as a potent tool for manufacturing companies, facilitating their transition toward greener practices and boosting operational efficiency. Additionally, the [...] Read more.
In the context of the emerging era of smart manufacturing, the concept of sustainability is gaining substantial and widespread attention. Information technology has emerged as a potent tool for manufacturing companies, facilitating their transition toward greener practices and boosting operational efficiency. Additionally, the sustainable development of the industrial and information technology sectors not only presents promising prospects for future progress but is also intricately tied to the achievement of “dual carbon” objectives. Therefore, strategically integrating cutting-edge information technology into graduate education not only enhances the proficiency of postgraduates in the fields of information technology and manufacturing but also facilitates the achievement of green and sustainable development goals. To this end, this paper proposes a novel “Three-Level Advancement” talent development model aimed at cultivating a greater number of highly qualified talents oriented toward green and sustainable development. Built upon a one-semester graduate education framework, the model assesses the effectiveness of the “Three-Level Advancement” training approach. Subsequently, the efficacy of the new talent development model is validated through a class-based comparative analysis. Finally, based on the interview responses of the participants, both teachers and students unanimously affirmed the significant superiority of learning outcomes achieved through this pedagogical reform over traditional teaching methods. The results indicate that this new talent development model not only markedly enhances the quality of practical education but also contributes to the cultivation of sustainable talents in the field of information technology. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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28 pages, 6289 KiB  
Article
Research on Talent Cultivating Pattern of Industrial Engineering Considering Smart Manufacturing
by Xugang Zhang, Cui Li and Zhigang Jiang
Sustainability 2023, 15(14), 11213; https://doi.org/10.3390/su151411213 - 18 Jul 2023
Cited by 3 | Viewed by 2711
Abstract
In-depth exploration of the theory and technological applications of smart manufacturing (SM) is lacking in the current talent training model for industrial engineering (IE) majors, and there is a lack of practical education for SM environments. This makes it difficult for students of [...] Read more.
In-depth exploration of the theory and technological applications of smart manufacturing (SM) is lacking in the current talent training model for industrial engineering (IE) majors, and there is a lack of practical education for SM environments. This makes it difficult for students of traditional IE majors to adapt to the modern trend of industrial intelligence and meet the needs of market demand and enterprise development. Therefore, how to cultivate IE talents for SM has become an urgent problem for IE majors to solve. To this end, this paper proposes a new “SM+IE” talent training model, aiming to cultivate more high-quality composite application talents. This model is based on the Lean Manufacturing course and analyzes the effect of the training mode of SM. Secondly, we used the topic of “Sorting Efficiency Improvement” to verify the effectiveness of the new talent training model. The materials were divided into three types: large, medium, and small, and the materials were sorted using traditional IE practices and smart manufacturing-oriented practices. Finally, interviews were conducted with the participants, and both teachers and students indicated that the learning effect of this teaching reform practice was significantly better than that of the traditional IE teaching mode. The results show that the new talent training model improved not only the application and practical skills of the IE students, but also their teamwork and leadership skills. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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18 pages, 4817 KiB  
Article
Calibration of Turbulent Model Constants Based on Experimental Data Assimilation: Numerical Prediction of Subsonic Jet Flow Characteristics
by Xin He, Changjiang Yuan, Haoran Gao, Yaqing Chen and Rui Zhao
Sustainability 2023, 15(13), 10219; https://doi.org/10.3390/su151310219 - 27 Jun 2023
Cited by 4 | Viewed by 1842
Abstract
Experimental measurements and numerical simulations are two primary methods for studying turbulence. However, these methods often struggle to balance the accuracy and breadth of results. In order to accurately predict the flow characteristics of subsonic jet exhaust and provide a research foundation for [...] Read more.
Experimental measurements and numerical simulations are two primary methods for studying turbulence. However, these methods often struggle to balance the accuracy and breadth of results. In order to accurately predict the flow characteristics of subsonic jet exhaust and provide a research foundation for the runway crossing operation after the takeoff point, this study utilizes the ensemble Kalman filter algorithm to recalibrate the SA turbulence model constants by integrating NASA’s experimental particle image velocimetry (PIV) data with a sample library generated using Latin hypercube sampling to obtain corresponding flow field calculations. The modified model constants effectively improve the prediction of jet flow characteristics, reducing the spatially averaged relative error along the horizontal axis behind the nozzle from 13.04% to 4.6%. This study focuses on enhancing the accuracy of numerical predictions for subsonic jet flows via the adjustment of turbulence model constants. The recalibrated model constants are then validated to improve the prediction of jet flows under various conditions. The findings have important implications for acquiring high-fidelity data on rear engine jet flows after takeoff, enabling precise determination of safety separation distances, and enhancing the operational efficiency of airports. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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19 pages, 1781 KiB  
Article
A Rental Platform Service Supply Chain Network Equilibrium Model Considering Digital Detection Technology Investment and Big Data Marketing
by Yongtao Peng and Hang Li
Sustainability 2023, 15(13), 9955; https://doi.org/10.3390/su15139955 - 22 Jun 2023
Cited by 3 | Viewed by 2041
Abstract
Digital transformation is reshaping the decision making management of the rental service mode in the manufacturing industry, and improving digital detection technology and big data marketing have become effective ways to create value. Based on the three-level rental platform service supply chain network [...] Read more.
Digital transformation is reshaping the decision making management of the rental service mode in the manufacturing industry, and improving digital detection technology and big data marketing have become effective ways to create value. Based on the three-level rental platform service supply chain network structure composed of manufacturers, rental platform operators and the demand market, a supply chain network equilibrium model considering the digital detection technology input and big data marketing is constructed by using variational inequality and the Nash equilibrium theory, and the optimal decision making conditions of the manufacturers and rental platform operators are derived. Combined with the Euler algorithm design procedure and numerical examples, the influences of the digital detection technology level, big data marketing cost coefficient and cost sharing ratio on the equilibrium state are analyzed. The results show that the input of digital detection technology leads to the increase in profits of each participant in the rental platform service supply chain network and promotes a more coordinated development of the supply chain. When the rental platforms implement big data marketing, the manufacturers share the cost, which can continuously improve the profits of both partners and make the cooperation more stable and efficient. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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18 pages, 4173 KiB  
Article
Study of Turbulent Kinetic Energy and Dissipation Based on Fractal Impeller
by Hongjun Li, Xingzhang Li, Jin Zhan, Wei Chen and Wangyuan Zong
Sustainability 2023, 15(10), 7772; https://doi.org/10.3390/su15107772 - 9 May 2023
Cited by 9 | Viewed by 2673
Abstract
Turbulent kinetic energy and turbulent dissipation are important aspects of the flow field characteristics, which can affect the wear and energy loss in mixing equipment. In order to increase equipment wear and energy loss in the mixing process, a series of fractal impellers [...] Read more.
Turbulent kinetic energy and turbulent dissipation are important aspects of the flow field characteristics, which can affect the wear and energy loss in mixing equipment. In order to increase equipment wear and energy loss in the mixing process, a series of fractal impellers were designed based on the fractal iteration method, and the effects of the fractal dimension and the number of iterations on the flow field characteristics were investigated. Firstly, the distribution characteristics of turbulent kinetic energy and its uniformity were studied. Then, the distribution characteristics of the turbulent dissipation rate were studied and interpreted using vortex analysis, and the mixing power of the device was further investigated. The results showed that: for the turbulent kinetic energy of the flow field, an increase in the fractal dimension and the number of iterations makes the turbulent kinetic energy intensity of the flow field decrease and the distribution more uniform, compared to the non-iterative impeller, specifically the rectangular secondary iterative impeller caused a 30% reduction in the turbulent kinetic energy intensity and a 50% increase in the uniformity; for the turbulent dissipation of the flow field, in general an increase in the fractal dimension reduces the turbulent dissipation in the flow field, and an increase in the number of iterations increases it slightly, this influence law is due to a change in the trailing vortex caused by the blade structure; and a change in the law of turbulent dissipation also causes a corresponding change in the stirring power, from the non-iterative impeller to a rectangular one iteration impeller, the power decreases by 20% while the average speed decreases by only 5%. In conclusion, the special boundary of the fractal iterative impeller can reduce the turbulent kinetic energy and turbulent dissipation of the flow field to a large extent, and its two characteristics, the fractal dimension and the number of iterations, affect the reduction effect. The results of the study can be used as a reference for the design of mixing equipment to reduce turbulent kinetic energy and turbulent dissipation. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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16 pages, 25102 KiB  
Article
Visualization Monitoring of Industrial Detonator Automatic Assembly Line Based on Digital Twin
by Hongjun Li, Yu Yang, Chi Zhang, Chengjun Zhang and Wei Chen
Sustainability 2023, 15(9), 7690; https://doi.org/10.3390/su15097690 - 8 May 2023
Cited by 5 | Viewed by 2581
Abstract
The continuous development of information technology has increased the level of automation and informatization in the manufacturing industry, which makes it necessary for companies to effectively monitor their assembly lines. Aiming to visualize the monitoring challenges of the assembly line production process, taking [...] Read more.
The continuous development of information technology has increased the level of automation and informatization in the manufacturing industry, which makes it necessary for companies to effectively monitor their assembly lines. Aiming to visualize the monitoring challenges of the assembly line production process, taking the industrial detonator automatic assembly line as the research object and referring to the digital twin five-dimensional model, a visualization monitoring method that utilizes an assembly line based on a digital twin is proposed. First, the architecture of the assembly line visualization monitoring system based on digital twin is constructed, and its specific operation flow is studied. Then, three key implementation methods, including assembly line virtual entity model construction, data collection in the assembly process and complex equipment error detection, are studied. Finally, a visualization monitoring system for the industrial detonator automatic assembly line is designed and developed, which verifies that the proposed method is effective in the visualization monitoring of the assembly line. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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12 pages, 2807 KiB  
Article
Reform of the Training Program of Intelligent Manufacturing Engineering of Universities in the Steel Industry
by Xianyin Duan, Kunpeng Zhu, Xingdong Wang and Min Zhou
Sustainability 2023, 15(5), 3952; https://doi.org/10.3390/su15053952 - 22 Feb 2023
Cited by 5 | Viewed by 2842
Abstract
To meet the demand of talents in the rapid and sustainable development of the steel manufacturing industry and the needs of the local development of green and intelligent steel technology, this paper presented a reformed training program of intelligent manufacturing of universities for [...] Read more.
To meet the demand of talents in the rapid and sustainable development of the steel manufacturing industry and the needs of the local development of green and intelligent steel technology, this paper presented a reformed training program of intelligent manufacturing of universities for the steel industry. The training program explored the reform plan of talent training objectives, curriculum system, teaching mode, practical links, and operation mechanism of intelligent manufacturing engineering, and built a system with the goal of cultivating innovative ability covering green and intelligent concepts. A new mode of talent cultivation that covers the green and intelligent manufacturing concept and awareness, engineering knowledge, and innovation ability is built, to cultivate high-quality engineering talents who can adapt to the demand of green and intelligent development of steel and other industries. The innovative talent cultivation mode could reconstruct the steel intelligent manufacturing engineering education system and produce a demonstrative effect and good social benefits on the construction of intelligent manufacturing engineering specialty in the industry-featured colleges and universities. Full article
(This article belongs to the Special Issue Big-Data-Driven Sustainable Manufacturing)
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