Special Issue "Smart Sustainable Manufacturing Systems"

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

Deadline for manuscript submissions: closed (30 June 2018).

Special Issue Editors

Guest Editor
Prof. Dr. Dimitrios Kyritsis

ICT for Sustainable Manufacturing, EPFL SCI STI DK, ME A1 381,Stn 9, CH-1015 Lausanne, Switzerland
Website | E-Mail
Interests: ICT for Sustainable Manufacturing; Closed Loop Lifecycle Management; Lifecycle Performance Evaluation; Engineering Asset Lifecycle Management; Predictive Management; Product-Process Modelling; Ontology Based Engineering; Context Aware Enterprise Applications; Knowledge Management
Guest Editor
Dr. Gökan May

ICT for Sustainable Manufacturing, EPFL SCI STI DK, ME A1 381,Stn 9, CH-1015 Lausanne, Switzerland
Website | E-Mail
Interests: Operations Management; Energy Management in Manufacturing; ICT for Sustainable Manufacturing; Zero-defect Manufacturing; Predictive Maintenance; Eco-Factories of the Future

Special Issue Information

Dear Colleagues,

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable and accessible as well as fostering their economic and social impact. This Special Issue of the journal Applied Sciences, devoted to the broad field of “Smart Sustainable Manufacturing Systems”, aims to explore recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing in order to advance and promote the development of modern and intelligent manufacturing systems.

Topics of interest include, but are not limited to, the following:

  • Design of sustainable production systems and factories

  • Industrial big data analytics and cyber physical systems for engineering asset management

  • Intelligent maintenance approaches and technologies for increased operating life of production systems

  • Zero-defect manufacturing strategies, tools and methods towards on-line production management

  • Connected smart factories

  • Adaptation of work environments with changing levels of automation in evolving production systems

  • Flexible and customised manufacturing

  • Resource and energy efficient manufacturing

  • Green scheduling

  • Manufacturing waste management, recycling and reuse

  • Eco-design in smart factories of industry 4.0

Prof. Dimitrios Kyritsis
Dr. Gökan May
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 papers will be 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 1500 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

  • Digital manufacturing

  • Smart factory

  • Industrial data analytics

  • Cyber physical systems

  • Industry 4.0

  • Predictive maintenance

  • Energy efficiency

  • Zero-defect manufacturing

  • Machine learning for industrial applications

  • Green Scheduling

Published Papers (10 papers)

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Editorial

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Open AccessEditorial
Special Issue on Smart Sustainable Manufacturing Systems
Appl. Sci. 2019, 9(11), 2264; https://doi.org/10.3390/app9112264
Received: 24 May 2019 / Accepted: 27 May 2019 / Published: 31 May 2019
PDF Full-text (169 KB) | HTML Full-text | XML Full-text
Abstract
With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities [...] Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)

Research

Jump to: Editorial

Open AccessArticle
Hybrid Laminate for Haptic Input Device with Integrated Signal Processing
Appl. Sci. 2018, 8(8), 1261; https://doi.org/10.3390/app8081261
Received: 29 June 2018 / Revised: 23 July 2018 / Accepted: 26 July 2018 / Published: 31 July 2018
Cited by 1 | PDF Full-text (2529 KB) | HTML Full-text | XML Full-text
Abstract
Achieving lightweight construction through only material substitution does not realize the full potential of producing a lightweight material, hence, it is no longer sufficient. Weight-saving goals are best achieved through additional function integration. In order to implement this premise for mass production, a [...] Read more.
Achieving lightweight construction through only material substitution does not realize the full potential of producing a lightweight material, hence, it is no longer sufficient. Weight-saving goals are best achieved through additional function integration. In order to implement this premise for mass production, a manufacturing process for joining and forming hybrid laminates using a new tool concept is presented. All materials used are widely producible and processable. The manufactured cover of an automotive center console serves to demonstrate a human interface device with impact detection and action execution. This is only possible through a machine learning system, which is implemented on a small—and thus space- and energy-saving—embedded system. The measurement results confirm the objective and show that localization was sufficiently accurate. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
Opportunities for Industry 4.0 to Support Remanufacturing
Appl. Sci. 2018, 8(7), 1177; https://doi.org/10.3390/app8071177
Received: 28 June 2018 / Revised: 13 July 2018 / Accepted: 13 July 2018 / Published: 19 July 2018
Cited by 5 | PDF Full-text (1351 KB) | HTML Full-text | XML Full-text
Abstract
Remanufacturing is the process of bringing end-of-life products back to good-as-new. It plays a critical role in decoupling economic growth from growth in resource use, and in accelerating the circular economy. However, the uptake of remanufacturing activities faces obstacles. This paper reviews the [...] Read more.
Remanufacturing is the process of bringing end-of-life products back to good-as-new. It plays a critical role in decoupling economic growth from growth in resource use, and in accelerating the circular economy. However, the uptake of remanufacturing activities faces obstacles. This paper reviews the challenges encountered by the remanufacturing sector and discusses how the Industry 4.0 revolution could help to effectively address these issues and unlock the potential of remanufacturing. Two case studies are included in this paper to exemplify how technology enablers from Industry 4.0 can increase efficiency, reliability, and digitization of the remanufacturing process. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits
Appl. Sci. 2018, 8(7), 1153; https://doi.org/10.3390/app8071153
Received: 28 June 2018 / Revised: 7 July 2018 / Accepted: 10 July 2018 / Published: 16 July 2018
Cited by 3 | PDF Full-text (648 KB) | HTML Full-text | XML Full-text
Abstract
The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered [...] Read more.
The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
Dynamic Supply Chain Design and Operations Plan for Connected Smart Factories with Additive Manufacturing
Appl. Sci. 2018, 8(4), 583; https://doi.org/10.3390/app8040583
Received: 4 March 2018 / Revised: 26 March 2018 / Accepted: 5 April 2018 / Published: 8 April 2018
Cited by 2 | PDF Full-text (2802 KB) | HTML Full-text | XML Full-text
Abstract
Interest in smart factories and smart supply chains has been increasing, and researchers have emphasized the importance and the effects of advanced technologies such as 3D printers, the Internet of Things, and cloud services. This paper considers an innovation in dynamic supply-chain design [...] Read more.
Interest in smart factories and smart supply chains has been increasing, and researchers have emphasized the importance and the effects of advanced technologies such as 3D printers, the Internet of Things, and cloud services. This paper considers an innovation in dynamic supply-chain design and operations: connected smart factories that share interchangeable processes through a cloud-based system for personalized production. In the system, customers are able to upload a product design file, an optimal supply chain design and operations plan are then determined based on the available resources in the network of smart factories. The concept of smart supply chains is discussed and six types of flexibilities are identified, namely: design flexibility, product flexibility, process flexibility, supply chain flexibility, collaboration flexibility, and strategic flexibility. Focusing on supply chain flexibility, a general planning framework and various optimization models for dynamic supply chain design and operations plan are proposed. Further, numerical experiments are conducted to analyze fixed, production, and transportation costs for various scenarios. The results demonstrate the extent of the dynamic supply chain design and operations problem, and the large variation in transportation cost. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
A Modified Method for Evaluating Sustainable Transport Solutions Based on AHP and Dempster–Shafer Evidence Theory
Appl. Sci. 2018, 8(4), 563; https://doi.org/10.3390/app8040563
Received: 25 January 2018 / Revised: 20 March 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
Cited by 30 | PDF Full-text (2751 KB) | HTML Full-text | XML Full-text
Abstract
With the challenge of transportation environment, a large amount of attention is paid to sustainable mobility worldwide, thus bringing the problem of the evaluation of sustainable transport solutions. In this paper, a modified method based on analytical hierarchy process (AHP) and Dempster–Shafer evidence [...] Read more.
With the challenge of transportation environment, a large amount of attention is paid to sustainable mobility worldwide, thus bringing the problem of the evaluation of sustainable transport solutions. In this paper, a modified method based on analytical hierarchy process (AHP) and Dempster–Shafer evidence theory (D-S theory) is proposed for evaluating the impact of transport measures on city sustainability. AHP is adapted to determine the weight of sustainability criteria while D-S theory is used for data fusion of the sustainability assessment. A Transport Sustainability Index (TSI) is presented as a primary measure to determine whether transport solutions have a positive impact on city sustainability. A case study of car-sharing is illustrated to show the efficiency of our proposed method. Our modified method has two desirable properties. One is that the BPA is generated with a new modification framework of evaluation levels, which can flexibly manage uncertain information. The other is that the modified method has excellent performance in sensitivity analysis. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
An Integrated Open Approach to Capturing Systematic Knowledge for Manufacturing Process Innovation Based on Collective Intelligence
Appl. Sci. 2018, 8(3), 340; https://doi.org/10.3390/app8030340
Received: 3 February 2018 / Revised: 17 February 2018 / Accepted: 22 February 2018 / Published: 27 February 2018
Cited by 1 | PDF Full-text (4584 KB) | HTML Full-text | XML Full-text
Abstract
Process innovation plays a vital role in the manufacture realization of increasingly complex new products, especially in the context of sustainable development and cleaner production. Knowledge-based innovation design can inspire designers’ creative thinking; however, the existing scattered knowledge has not yet been properly [...] Read more.
Process innovation plays a vital role in the manufacture realization of increasingly complex new products, especially in the context of sustainable development and cleaner production. Knowledge-based innovation design can inspire designers’ creative thinking; however, the existing scattered knowledge has not yet been properly captured and organized according to Computer-Aided Process Innovation (CAPI). Therefore, this paper proposes an integrated approach to tackle this non-trivial issue. By analyzing the design process of CAPI and technical features of open innovation, a novel holistic paradigm of process innovation knowledge capture based on collective intelligence (PIKC-CI) is constructed from the perspective of the knowledge life cycle. Then, a multi-source innovation knowledge fusion algorithm based on semantic elements reconfiguration is applied to form new public knowledge. To ensure the credibility and orderliness of innovation knowledge refinement, a collaborative editing strategy based on knowledge lock and knowledge–social trust degree is explored. Finally, a knowledge management system MPI-OKCS integrating the proposed techniques is implemented into the pre-built CAPI general platform, and a welding process innovation example is provided to illustrate the feasibility of the proposed approach. It is expected that our work would lay the foundation for the future knowledge-inspired CAPI and smart process planning. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessFeature PaperArticle
A Multi-Usable Cloud Service Platform: A Case Study on Improved Development Pace and Efficiency
Appl. Sci. 2018, 8(2), 316; https://doi.org/10.3390/app8020316
Received: 18 December 2017 / Revised: 13 February 2018 / Accepted: 16 February 2018 / Published: 24 February 2018
Cited by 3 | PDF Full-text (3193 KB) | HTML Full-text | XML Full-text
Abstract
The case study, spanning three contexts, concerns a multi-usable cloud service platform for big data collection and analytics and how the development pace and efficiency of it has been improved by 50–75% by using the Arrowhead framework and changing development processes/practices. Furthermore, additional [...] Read more.
The case study, spanning three contexts, concerns a multi-usable cloud service platform for big data collection and analytics and how the development pace and efficiency of it has been improved by 50–75% by using the Arrowhead framework and changing development processes/practices. Furthermore, additional results captured during the case study are related to technology, competencies and skills, organization, management, infrastructure, and service and support. A conclusion is that when offering a complex offer such as an Industrial Product-Service System, comprising sensors, hardware, communications, software, cloud service platform, etc., it is necessary that the technology, business model, business setup, and organization all go hand in hand during the development and later operation, as all ‘components’ are required for a successful result. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
Kernel-Density-Based Particle Defect Management for Semiconductor Manufacturing Facilities
Appl. Sci. 2018, 8(2), 224; https://doi.org/10.3390/app8020224
Received: 2 January 2018 / Revised: 20 January 2018 / Accepted: 28 January 2018 / Published: 1 February 2018
Cited by 2 | PDF Full-text (5488 KB) | HTML Full-text | XML Full-text
Abstract
In a semiconductor manufacturing process, defect cause analysis is a challenging task because the process includes consecutive fabrication phases involving numerous facilities. Recently, in accordance with the shrinking chip pitches, fabrication (FAB) processes require advanced facilities and designs for manufacturing microcircuits. However, the [...] Read more.
In a semiconductor manufacturing process, defect cause analysis is a challenging task because the process includes consecutive fabrication phases involving numerous facilities. Recently, in accordance with the shrinking chip pitches, fabrication (FAB) processes require advanced facilities and designs for manufacturing microcircuits. However, the sizes of the particle defects remain constant, in spite of the increasing modernization of the facilities. Consequently, this increases the particle defect ratio. Therefore, this study proposes a particle defect management method for the reduction of the defect ratio. The proposed method provides a kernel-density-based particle map that can overcome the limitations of the conventional method. The method consists of two phases. The first phase is the acquisition of cumulative coordinates of the defect locations on the wafer using the FAB database. Subsequently, this cumulative data is used to generate a particle defect map based on the estimation of kernel density; this map establishes the advanced monitoring statistics. In order to validate this method, we conduct an experiment for comparison with the previous industrial method. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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Open AccessArticle
Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
Appl. Sci. 2018, 8(2), 165; https://doi.org/10.3390/app8020165
Received: 12 December 2017 / Revised: 12 January 2018 / Accepted: 19 January 2018 / Published: 24 January 2018
Cited by 3 | PDF Full-text (3866 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper provides new insights to the area of sustainable manufacturing systems at analyzing the novel paradigm of integrated production logistics, quality, and maintenance design. For this purpose, we investigate the optimal production and repair/major maintenance switching strategy of an unreliable deteriorating manufacturing [...] Read more.
This paper provides new insights to the area of sustainable manufacturing systems at analyzing the novel paradigm of integrated production logistics, quality, and maintenance design. For this purpose, we investigate the optimal production and repair/major maintenance switching strategy of an unreliable deteriorating manufacturing system. The effects of the deterioration process are mainly observed on the failure intensity and on the quality of the parts produced, where the rate of defectives depends on the production rate. When unplanned failures occur, either a minimal repair or a major maintenance could be conducted. The integration of availability and quality deterioration led us to propose a new stochastic dynamic programming model where optimality conditions are derived through the Hamilton-Jacobi-Bellman equations. The model defined the joint production and repair/major maintenance switching strategies minimizing the total cost over an infinite planning horizon. In the results, the influence of the deterioration process were evident in both the production and maintenances control parameters. A numerical example and an extensive sensitivity analysis were conducted to illustrate the usefulness of the results. Finally, the proposed control policy was compared with alternative strategies based on common assumptions of the literature in order to illustrate its efficiency. Full article
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
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