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Sustainable Manufacturing Systems Using Big Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 17589

Special Issue Editors


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Guest Editor
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Interests: modern mechanical design theory and method; product digital design and manufacture; big data and cloud technology in design and manufacture
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau, China
Interests: petri net theory and application; supervisory control of discrete event systems; workflow modeling and analysis; system reconfiguration; game theory; data and process mining

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Guest Editor
Computer Science Department, Conservatoire National des Arts et Métiers, 75141 Paris, France
Interests: formal methods for specification; verification, control and performance evaluation of concurrent and discrete-event systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, there is a great deal of concern and interest in environmental sustainability with regard to carbon emissions, global warming and toxic hazes. In addition to environmental pollution, the social sustainability agenda of improving the quality of human life is particularly important. Simultaneous consideration of environmental and social factors, in addition to the financial costs, aims to meet the standards of global sustainable development. A sustainable design and manufacturing process for the manufacturing industry not only reduces financial costs, but also minimizes massive amounts of carbon emissions and waste energy in addition to maximizing the social factors. The problem of large amounts of carbon emissions and energy waste caused by the design and manufacturing industry is a wide concern across the world and deciding how to find a sustainable design to address the financial, environmental and social factors is one of the primary issues in modern society.

Currently, many new technologies, such as energy-efficient cloud computing, energy internet, big data and knowledge management, have been integrated and widely applied to facilitate many national advanced design and manufacturing strategies. One of their common aims is to achieve smart design and manufacture, which is of great significance for sustainable development. However, without data support, and the support of data science and technology, “smart” cannot be achieved. However, the type of design and manufacturing that big data will generate in the entire lifecycle of a product is still unclear. Furthermore, deciding how to collect the useful data, as well as the extraction and utilization of useful information from such huge and dynamic databases for “big data”, is frightening. This has motivated researchers and practitioners to explore new methods and technologies for industrial applications of big data in sustainable design and manufacture.

This Special Issue of Applied Science solicits high-quality contributions that focus on the design and development of novel algorithms, technologies, and tools to address sustainable design and manufacture using big data. Topics of interest include but are not limited to:

  • Sustainable design methodologies and manufacturing technologies using big data;
  • Data collection and knowledge representation for sustainable design and manufacture using big data;
  • Analyzing, capturing and evaluating consumer requirements and concepts for sustainable design and manufacture through big data;
  • Impact of uncertainty on generation and evaluation of sustainable design and manufacture using big data;
  • Modeling analysis and control of a product’s sustainable design and manufacture;
  • Using big data for correlating consumer satisfaction, engineering characteristics and design attributes for sustainable design and manufacture;
  • Using big data to develop smart systems for sustainable design and manufacture;
  • Development of machine learning/artificial intelligence techniques for sustainable design and manufacture based on big data;
  • Incorporating decision-making within the development process of sustainable design and manufacture using big data;
  • Sustainable logistics and service quality management using big data;
  • For energy internet use: data mining and knowledge discovery, intelligent algorithms and optimization, and machine learning and deep learning;
  • Energy efficient hardware, devices and designs for cloud-computing platforms;
  • Modeling and control of manufacturing systems using big data;
  • Security analysis and design of manufacturing system.

Prof. Dr. Yixiong Feng
Prof. Dr. Guangdong Tian
Prof. Dr. Amir M. Fathollahi-Fard
Prof. Dr. Zhiwu Li
Prof. Dr. Kamel Barkaoui
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. Applied Sciences 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 design
  • manufacturing systems
  • big data analytics
  • system modeling and simulation
  • optimization
  • sustainable development

Published Papers (6 papers)

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Research

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15 pages, 11682 KiB  
Article
Experimental Research on Braking Feedback and Taxiing Feedback System of New Energy Vehicles
by Ning Li, Yingshuai Liu and Siyuan Tan
Appl. Sci. 2021, 11(23), 11093; https://doi.org/10.3390/app112311093 - 23 Nov 2021
Cited by 3 | Viewed by 1724
Abstract
This paper took a new energy vehicle feedback system as the research object, aiming to study the energy recovery law of the new energy vehicle under braking feedback and taxiing feedback conditions. Firstly, the braking energy feedback control strategy and different forms of [...] Read more.
This paper took a new energy vehicle feedback system as the research object, aiming to study the energy recovery law of the new energy vehicle under braking feedback and taxiing feedback conditions. Firstly, the braking energy feedback control strategy and different forms of taxiing energy feedback were studied. Then the integration and application of braking energy recovery system were carried out on a pure electric bus and a hybrid electric bus, with each vehicle model corresponding to different integration and test schemes, which provided a guarantee for the relevant test of real vehicle environments. Finally, relevant vehicle experiments were carried out to test the impact of superposition and coordination strategies on the contribution rate of braking energy recovery under a typical Chinese city bus circle and compared the difference in vehicle energy consumption with and without taxi feedback strategy. The test results showed that the coordinated braking energy recovery control strategy can make more effective use of the maximum torque that can be fed back by the motor, and the fuel consumption of the taxiing feedback mode was lower than that of the no taxiing feedback mode under different driving conditions. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data)
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11 pages, 1747 KiB  
Article
Multi-Objective Optimization Model of Industrial Lubricants Based on Integer Nonlinear Programming
by Min Yuan, Yu Li, Wenqiang Xu and Wei Cui
Appl. Sci. 2021, 11(18), 8375; https://doi.org/10.3390/app11188375 - 9 Sep 2021
Viewed by 1583
Abstract
Based on actual lubricating oil production data and the base oil performance indexes of an enterprise, two nonlinear blending schemes corresponding to viscosity and freezing point and four linear blending schemes corresponding to acid value, flash point, oxidation stability, and carbon residue are [...] Read more.
Based on actual lubricating oil production data and the base oil performance indexes of an enterprise, two nonlinear blending schemes corresponding to viscosity and freezing point and four linear blending schemes corresponding to acid value, flash point, oxidation stability, and carbon residue are given in this paper. On the premise that the error of each index is less than 5%, a linear weighted multi-objective optimization model based on integer nonlinear programming considering cost and performance is established, and the lubricating oil blending scheme is obtained. The results show that the blending formula is simple in form and convenient in calculation, and that the overall consistency between the calculated value and the measured value is good. At the same time, the relative error of each performance index, except residual carbon, of the scheme with weight value of (0.5, 0.5) is far less than 5%. Although the performance index is slightly inferior to that of the scheme with a weight value of (0, 1), it is far higher than that of the scheme with a weight value of (1, 0). The linear weighted multi-objective optimization model based on integer nonlinear programming proposed in this paper can well-optimize the blending scheme of industrial lubricating oil, and can re-select different weight combinations according to the actual situation, providing good prospects for application. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data)
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25 pages, 2854 KiB  
Article
Reverse Logistics Location Based on Energy Consumption: Modeling and Multi-Objective Optimization Method
by Lijun Chang, Honghao Zhang, Guoquan Xie, Zhenzhong Yu, Menghao Zhang, Tao Li, Guangdong Tian and Dexin Yu
Appl. Sci. 2021, 11(14), 6466; https://doi.org/10.3390/app11146466 - 13 Jul 2021
Cited by 10 | Viewed by 2159
Abstract
The low-carbon economy, as a major trend of global economic development, has been a widespread concern, which is a rare opportunity to realize the transformation of the economic way in China. The realization of a low-carbon economy requires improved resource utilization efficiency and [...] Read more.
The low-carbon economy, as a major trend of global economic development, has been a widespread concern, which is a rare opportunity to realize the transformation of the economic way in China. The realization of a low-carbon economy requires improved resource utilization efficiency and reduced carbon emissions. The reasonable location of logistics nodes is of great significance in the optimization of a logistics network. This study formulates a double objective function optimization model of reverse logistics facility location considering the balance between the functional objectives of the carbon emissions and the benefits. A hybrid multi-objective optimization algorithm that combines a gravitation algorithm and a particle swarm optimization algorithm is proposed to solve this reverse logistics facility location model. The mobile phone recycling logistics network in Jilin Province is applied as the case study to verify the feasibility of the proposed reverse logistics facility location model and solution method. Analysis and discussion are conducted to monitor the robustness of the results. The results prove that this approach provides an effective tool to solve the multi-objective optimization problem of reverse logistics location. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data)
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16 pages, 2135 KiB  
Article
Integrated Design of Process-Tolerance for Remanufacturing Based on Failure Feature
by Yanxiang Chen, Zhigang Jiang, Hua Zhang and Wei Yan
Appl. Sci. 2021, 11(14), 6377; https://doi.org/10.3390/app11146377 - 9 Jul 2021
Cited by 2 | Viewed by 1703
Abstract
The uncertainty failure of the used part leads the complexity selection of remanufacturing processes. The different remanufacturing process combinations among used parts of used products also make the formulation of tolerance schemes more difficult. It is hard to guarantee the optimality of process-tolerance [...] Read more.
The uncertainty failure of the used part leads the complexity selection of remanufacturing processes. The different remanufacturing process combinations among used parts of used products also make the formulation of tolerance schemes more difficult. It is hard to guarantee the optimality of process-tolerance schemes by traditional serial production modes in general, in which tolerance design is followed by process formulation. In order to generate the optimal remanufacturing scheme of process and tolerance for used products, an optimization method to integrate designs of process-tolerance (IDP-T) based on fault features was presented. In this work, the failure description set of used parts was constructed by combining the attribute characteristics and failure characteristics. Case-based reasoning (CBR) was first utilized to generate the feasible remanufacturing process plans of used parts. Then, based on the feasible process plans, the factors of cost, quality loss, closed-loop accuracy and machining ability of remanufacturing were comprehensively considered to construct the optimization model of IDP-T. The Beetle Antennae Search algorithm (BAS) was used for the optimal alternative selection. Finally, a used gearbox was taken as an example to illustrate the validity and practicality of the proposed method. The results showed that the proposed method was effective in the optimization of IDP-T for remanufacturing. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data)
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29 pages, 5294 KiB  
Article
Research on Testing Method of Oil Characteristic Based on Quartz Tuning Fork Sensor
by Hao Sun, Yingshuai Liu and Jianwei Tan
Appl. Sci. 2021, 11(12), 5642; https://doi.org/10.3390/app11125642 - 18 Jun 2021
Cited by 17 | Viewed by 2892
Abstract
There is increasing demand for the on-board diagnosis of lubricating oils. In this research, we consider various sensor principles for on-board diagnosis of the thermal aging of engine oils. One of the parameters investigated is the viscosity of the lubricating oil, which can [...] Read more.
There is increasing demand for the on-board diagnosis of lubricating oils. In this research, we consider various sensor principles for on-board diagnosis of the thermal aging of engine oils. One of the parameters investigated is the viscosity of the lubricating oil, which can be efficiently measured using a microacoustic sensor. Compared with conventional viscometers, these sensors probe a different rheological domain, which needs to be considered in the interpretation of measurement results. This specific behavior is examined by systematically investigating engine oils, with and without additive packages, that were subjected to a defined artificial aging process. This paper presents design strategies for the algorithm developed and applied for direct on-board diagnosis of engine oil conditions with a fluid property sensor; this enables prediction of remaining oil life and optimization of oil change intervals, thereby minimizing the likelihood of dramatic engine failure and reducing maintenance costs. After a general description of the principles of sensor measurement, different engine oil contaminants, aging phenomena, and associated sensor detection and measurement capabilities are discussed. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data)
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Review

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24 pages, 3409 KiB  
Review
A Review on the Lifecycle Strategies Enhancing Remanufacturing
by Raoul Fonkoua Fofou, Zhigang Jiang and Yan Wang
Appl. Sci. 2021, 11(13), 5937; https://doi.org/10.3390/app11135937 - 25 Jun 2021
Cited by 30 | Viewed by 5576
Abstract
Remanufacturing is a domain that has increasingly been exploited during recent years due to its numerous advantages and the increasing need for society to promote a circular economy leading to sustainability. Remanufacturing is one of the main end-of-life (EoL) options that can lead [...] Read more.
Remanufacturing is a domain that has increasingly been exploited during recent years due to its numerous advantages and the increasing need for society to promote a circular economy leading to sustainability. Remanufacturing is one of the main end-of-life (EoL) options that can lead to a circular economy. There is therefore a strong need to prioritize this option over other available options at the end-of-life stage of a product because it is the only recovery option that maintains the same quality as that of a new product. This review focuses on the different lifecycle strategies that can help improve remanufacturing; in other words, the various strategies prior to, during or after the end-of-life of a product that can increase the chances of that product being remanufactured rather than being recycled or disposed of after its end-of-use. The emergence of the fourth industrial revolution, also known as industry 4.0 (I4.0), will help enhance data acquisition and sharing between different stages in the supply chain, as well boost smart remanufacturing techniques. This review examines how strategies like design for remanufacturing (DfRem), remaining useful life (RUL), product service system (PSS), closed-loop supply chain (CLSC), smart remanufacturing, EoL product collection and reverse logistics (RL) can enhance remanufacturing. We should bear in mind that not all products can be remanufactured, so other options are also considered. This review mainly focuses on products that can be remanufactured. For this review, we used 181 research papers from three databases; Science Direct, Web of Science and Scopus. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data)
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