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Digital Technology in Sustainable Manufacturing Systems
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Digital Technology in Sustainable Manufacturing Systems

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3737

Special Issue Editors

Prof. Dr. Hao Li

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhoug University of Light Industry, Zhengzhou, China
Interests: digital twin; intelligent operation and maintenance; optimization design of complex equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Shuai Zhang

E-Mail Website
Guest Editor
School of Business, Operations and Strategy, University of Greenwich, London, UK
Interests: industrial sustainability; manufacturing systems; knowledge management; digital twin
Dr. Haoqi Wang

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
Interests: digital twin; model based systems engineering; product design methodology

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to “Digital Technology in Sustainable Manufacturing Systems” in Sustainability. This Special Issue investigates how digital technologies impact economic, environmental, and social aspects of sustainable manufacturing, including process optimization, energy, material efficiency, waste reduction and reuse, policy frameworks, and regulatory tools. Digital technology plays a crucial role in sustainable manufacturing practices. It involves the use of technologies such as robotics, artificial intelligence, big data, and the Internet of Things (IoT), and the development and integration of new digital technologies such as digital twins, advanced sensors, and predictive analytics to achieve specific goals in the circular economy, industrial symbiosis, human-centered manufacturing, and life cycle assessment for sustainability. 

There are several challenges related to digital technologies for sustainability in manufacturing systems. The problems involved in digital technologies are extensive and complex, as the corresponding working conditions vary from manufacturing systems. In the meantime, sustainable manufacturing focuses on reducing the environmental impact by minimizing and reusing wastes, reducing energy consumption, and utilizing renewable energy sources. The integration of digital technologies into sustainable manufacturing processes further opens up research directions on evaluating, monitoring, and optimizing waste reduction, energy efficiency, and materials utilization.

We have organized this Special Issue to call for discussions on the latest research progress in digital technologies for sustainable manufacturing, including various problems faced in the improvement of sustainable manufacturing systems and the impacts of digital technologies on sustainability

Prof. Dr. Hao Li
Dr. Shuai Zhang
Dr. Haoqi 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. 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
  • wastes reduction
  • energy consumption and efficiency
  • process optimization
  • artificial intelligence
  • digital twin
  • internet of thing (IoT)
  • industrial symbiosis
  • circular economy
  • life cycle assessment

Published Papers (3 papers)

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Research

24 pages, 2270 KiB  
Article
A Bottom-Up Methodology for Identifying Key Performance Indicators for Sustainability Monitoring of Unit Manufacturing Processes
by Marija Glišić, Badrinath Veluri and Devarajan Ramanujan
Sustainability 2024, 16(2), 806; https://doi.org/10.3390/su16020806 - 17 Jan 2024
Viewed by 792
Abstract
With growing environmental concerns and regulatory requirements, manufacturers are increasingly required to monitor and reduce the environmental impacts of their production processes. Despite increasing digitalization and data-collection capabilities, manufacturers are challenged in collecting the right data and framing process improvement targets. To address [...] Read more.
With growing environmental concerns and regulatory requirements, manufacturers are increasingly required to monitor and reduce the environmental impacts of their production processes. Despite increasing digitalization and data-collection capabilities, manufacturers are challenged in collecting the right data and framing process improvement targets. To address this challenge, this paper presents a bottom-up methodology based on the life cycle assessment for identifying performance indicators with the goal of monitoring and reducing the overall environmental impacts of a manufacturing process. More specifically, process performance indicators are defined as a set of controllable process parameters, and their suitability for sustainability monitoring is evaluated based on their sensitivity, measurability, actionability, reliability, timeliness, and human-centricity with respect to a chosen environmental impact category. The bottom-up formulation of process performance indicators is demonstrated through a real-world case study on an infeed centerless grinding process in a large manufacturing company. Results from the case study show that the process performance indicators with regards to climate change impacts included (i) reduction in grinding time, (ii) reduction in total grinding power, (iii) reduction in sparkout time, and (iv) increase in batch size. Full article
(This article belongs to the Special Issue Digital Technology in Sustainable Manufacturing Systems)
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29 pages, 5699 KiB  
Article
Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing
by Zhongfei Zhang, Ting Qu, Kuo Zhao, Kai Zhang, Yongheng Zhang, Lei Liu, Jun Wang and George Q. Huang
Sustainability 2023, 15(23), 16539; https://doi.org/10.3390/su152316539 - 04 Dec 2023
Viewed by 1085
Abstract
In the quest for sustainable production, manufacturers are increasingly adopting mixed-flow production modes to meet diverse product demands, enabling small-batch production and ensuring swift delivery. A key aspect in this shift is optimizing material distribution scheduling to maintain smooth operations. However, traditional methods [...] Read more.
In the quest for sustainable production, manufacturers are increasingly adopting mixed-flow production modes to meet diverse product demands, enabling small-batch production and ensuring swift delivery. A key aspect in this shift is optimizing material distribution scheduling to maintain smooth operations. However, traditional methods frequently encounter challenges due to outdated information tools, irrational task allocation, and suboptimal route planning. Such limitations often result in distribution disarray, unnecessary resource wastage, and general inefficiency, thereby hindering the economic and environmental sustainability of the manufacturing sector. Addressing these challenges, this study introduces a novel dynamic material distribution scheduling optimization model and strategy, leveraging digital twin (DT) technology. This proposed strategy aims to bolster cost-effectiveness while simultaneously supporting environmental sustainability. Our methodology includes developing a route optimization model that minimizes distribution costs, maximizes workstation satisfaction, and reduces carbon emissions. Additionally, we present a cloud–edge computing-based decision framework and explain the DT-based material distribution system’s components and operation. Furthermore, we designed a DT-based dynamic scheduling optimization mechanism, incorporating an improved ant colony optimization algorithm. Numerical experiments based on real data from a partner company revealed that the proposed material distribution scheduling model, strategy, and algorithm can reduce the manufacturer’s distribution operation costs, improve resource utilization, and reduce carbon emissions, thereby enhancing the manufacturer’s economic and environmental sustainability. This research offers innovative insights and perspectives that are crucial for advancing sustainable logistics management and intelligent algorithm design in analogous manufacturing scenarios. Full article
(This article belongs to the Special Issue Digital Technology in Sustainable Manufacturing Systems)
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18 pages, 8809 KiB  
Article
A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment
by Hao Li, Xiaocong Wang, Yan Liu, Gen Liu, Zhongshang Zhai, Xinyu Yan, Haoqi Wang and Yuyan Zhang
Sustainability 2023, 15(14), 10783; https://doi.org/10.3390/su151410783 - 10 Jul 2023
Cited by 2 | Viewed by 1181
Abstract
Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to [...] Read more.
Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits. Full article
(This article belongs to the Special Issue Digital Technology in Sustainable Manufacturing Systems)
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