Special Issue "Selected Papers from the 9th International Conference “Production Engineering and Management” (PEM 2019)"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editors

Prof. Dr. Antonella Meneghetti
Website
Guest Editor
Department of Polytechnic Engineering and Architecture, University of Udine, 33100 Udine, Italy
Interests: sustainable warehousing and distribution; sustainable production planning; energy efficiency; energy systems optimization
Prof. Dr. Elio Padoano
Website SciProfiles
Guest Editor
Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy
Interests: operations management and cleaner production; sustainable logistics and transports; evaluation methodologies for project planning; sustainability and effectiveness in services
Prof. Dr. Franz-Josef Villmer
Website
Guest Editor
OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
Interests: product development; innovation management; additive manufacturing; direct digital manufacturing

Special Issue Information

Dear Colleagues,

The International Conference “Production Engineering and Management” was held for the first time in June 2011 and, during the years, it has been an opportunity for the presentation and discussion of advanced topics concerning manufacturing practices, processes, and technologies. Since its third edition, the topic of direct digital manufacturing has been added alongside product life-cycle assessment, lean manufacturing, supply chain management, and wood processing technologies.

All these topics are strictly related to the concepts of innovation and sustainability, which are enabled by new and renewable materials, energy efficiency, smart processes and technologies, and are supported by digital infrastructures and data management technology for effective decisions.

The 9th edition of the Conference, which is scheduled for the 3–4 October 2019 in Trieste (Italy) is particularly focused on but not limited to the discussion of management, process or technological solutions that can bring benefits in the current information-rich environment, where a key role in sustainable industrial systems can be played by digital transformation.

This Special Issue is intended to collect a selection of the most significant contributions presented at the Conference. Research papers on conceptual frameworks, technology deployment and assessment, optimization models, as well as applications and case studies, are welcome. Submitted papers should be an extended and improved version of the article presented at PEM 2019.

Key dates: Paper submission for PEM 2019: 21 June 2019 (see www.pem.units.it)                  
Paper selection for the Special Issue: 30 October 2019                  
Extended paper submission: 20 January 2020

Prof. Dr. Antonella Meneghetti
Prof. Dr. Elio Padoano
Prof. Dr. Franz-Josef Villmer
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. 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 1800 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

  • Cleaner production
  • Energy efficient manufacturing
  • Sustainable logistics
  • Sustainable supply chain management
  • Sustainable product life-cycle management
  • Waste reduction
  • Smart processes
  • Digital transformation

Published Papers (5 papers)

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Research

Open AccessArticle
Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry
Sustainability 2020, 12(11), 4776; https://doi.org/10.3390/su12114776 - 11 Jun 2020
Abstract
The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend [...] Read more.
The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms—the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)—are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures. Full article
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Open AccessArticle
Waste Reduction in Production Processes through Simulation and VSM
Sustainability 2020, 12(8), 3291; https://doi.org/10.3390/su12083291 - 17 Apr 2020
Cited by 1
Abstract
Corporate managers often face the need to choose the optimal configurations of production processes to reduce waste. Research has shown that simulation is an effective tool among those conceived to support the manager’s decisions. Nevertheless, the use of simulation at the company level [...] Read more.
Corporate managers often face the need to choose the optimal configurations of production processes to reduce waste. Research has shown that simulation is an effective tool among those conceived to support the manager’s decisions. Nevertheless, the use of simulation at the company level remains limited due to the complexity in the design phase. In this context, the Value Stream Map (VSM)—a tool of the Lean philosophy–is here exploited as a link between the strategic needs of the management and the operational aspect of the simulation process in order to approach sustainability issues. The presented approach is divided into two main parts: a set of criteria for expanding the VSM are identified in order to increase the level of details of the represented processes; then, data categories required for the inputs and outputs of each sub-process modeling are defined, including environmental indicators. Specifically, an extended version of the classical VSM (X-VSM), conceived to support process simulation, is here proposed: the X-VSM is used to guide the design of the simulation so that the management decisions, in terms of waste reduction, can be easily evaluated. The proposal was validated on a production process of a large multinational manufacturing company. Full article
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Open AccessArticle
Waste Reduction for Green Service Supply Chain—the Case Study of a Payment Service Provider in Iran
Sustainability 2020, 12(5), 1833; https://doi.org/10.3390/su12051833 - 29 Feb 2020
Cited by 1
Abstract
Analyzing and designing how service is provided to the customer is crucial for sustainable supply chains in services. In this respect, there can be barriers to applying sustainable improvements due to regulations, practices and customer culture. This study is focused on finding the [...] Read more.
Analyzing and designing how service is provided to the customer is crucial for sustainable supply chains in services. In this respect, there can be barriers to applying sustainable improvements due to regulations, practices and customer culture. This study is focused on finding the waste produced by the service of one of the biggest payment service provider (PSP) companies in Iran and how to meet the essential needs of the sustainable supply chain. It has been observed that using thermal papers as a biohazardous material causes environmental problems and even it is hazardous to mix them with normal paper waste in the recycling process. Moreover, preventive maintenance of the thermal printers itself causes a huge number of unnecessary shuttles between the customers and service suppliers, which represents a source of CO2 emission, traffic—especially in the capital—and high maintenance costs for the company. Three main alternatives to the thermal paper receipt were analyzed and ranked by means of a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, which employed the sustainability pillars and the technical point of view as evaluation criteria. The priorities against the set of criteria were obtained by means of surveys, which targeted a sample of customers and a pool of experts. The results highlighted that customers’ habits and legislation are the most important barriers to the transition to a more sustainable service. Full article
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Open AccessArticle
Transferability of Process Parameters in Laser Powder Bed Fusion Processes for an Energy and Cost Efficient Manufacturing
Sustainability 2020, 12(4), 1565; https://doi.org/10.3390/su12041565 - 19 Feb 2020
Abstract
In the past decade, the sales of metal additive manufacturing systems have increased intensely. In particular, PBF-LB/M systems (powder bed fusion of metals using a laser-based system) represent a technology of great industrial interest, in which metallic powders are molten and solidified layer [...] Read more.
In the past decade, the sales of metal additive manufacturing systems have increased intensely. In particular, PBF-LB/M systems (powder bed fusion of metals using a laser-based system) represent a technology of great industrial interest, in which metallic powders are molten and solidified layer upon layer by a focused laser beam. This leads to a simultaneous increase in demand for metallic powder materials. Due to adjusted process parameters of PBF-LB/M systems, the powder is usually procured by the system’s manufacturer. The requirement and freedom to process different feedstocks in a reproducible quality and the economic and ecological factors involved are reasons to have a closer look at the differences between the quality of the provided metallic powders. Besides, different feedstock materials require different energy inputs, allowing a sustainable process control to be established. In this work, powder quality of stainless steel 1.4404 and the effects during the processing of metallic powders that are nominally the same were analyzed and the influence on the build process followed by the final part quality was investigated. Thus, a correlation between morphology, particle size distribution, absorptivity, flowability, and densification depending on process parameters was demonstrated. Optimized exposure parameters to ensure a more sustainable and energy and cost-efficient manufacturing process were determined. Full article
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Open AccessArticle
Automated Reconditioning of Thin Wall Structures Using Robot-Based Laser Powder Coating
Sustainability 2020, 12(4), 1477; https://doi.org/10.3390/su12041477 - 17 Feb 2020
Abstract
Implementing digitalization in the field of production represents a major hurdle for some small- and medium-sized enterprises (SMEs) due to the ensuing demands on employees and, in some cases, the significant financial investment required. The RobReLas research project has developed a system whose [...] Read more.
Implementing digitalization in the field of production represents a major hurdle for some small- and medium-sized enterprises (SMEs) due to the ensuing demands on employees and, in some cases, the significant financial investment required. The RobReLas research project has developed a system whose purpose is to enable an economical solution to this dilemma for SMEs in the field of automated, robot-based reconditioning of components. A laser scanner was integrated in the robot’s control. The data generated by the scanner are used to mathematically describe the virtual area of the surface to be laser-treated. The scanner recognizes the relevant area within the robot’s predefined work space by defining the maximum length and width of the relevant component. The system then automatically applies predefined and qualified repair strategies in the virtual area. Tests on nickel-based blades demonstrated the system’s economic potential, showing a reduction in reconditioning time of about 70% compared to the conventional reconditioning method. The main advantage of the system is the fact that a basic knowledge of operating robots is sufficient for the attainment of repeatable results. Further, no additional CAD/CAM workstations are required for implementation. Full article
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