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Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021)
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Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021)

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 10057

Special Issue Editors

Dr. Francisco Javier Trujillo Vilches

E-Mail Website
Guest Editor
Department of Manufacturing Engineering, University of Malaga, C/ Dr. Ortiz Ramos s/n, 29071 Málaga, Spain
Interests: machining; lightweight materials; aeronautical alloys; aerospace structures; sustainable manufacturing; surface integrity; industrial metrology; additive manufacturing; simulation in manufacturing processes; digital image correlation; industrial heritage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Álvaro Rodríguez-Prieto

E-Mail Website
Guest Editor
Department of Manufacturing Engineering, Universidad Nacional de Educación a Distancia, Juan del Rosal 12, E28040 Madrid, Spain
Interests: materials processing technologies; metal forming; additive manufacturing; materials technology; data-driven decision methodologies; materials selection in manufacturing; equipment reliability; failure prognosis; nuclear power applications; renewable energy applications; oil & gas applications; aerospace applications;industrial heritage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the success of the three previous editions and encouraged by the Manufacturing Engineering Society (MES), a new edition called “Special Issue of the Manufacturing Engineering Society 2021 (SIMES-2021)” has been launched as a joint issue of the journals Materials and Applied Sciences.

The first edition collected 48 contributions on emerging methods and technologies, such as those related to additive manufacturing and 3D printing, advances and innovations in manufacturing processes in different areas (machining, forming, molding, welding, and nontraditional manufacturing processes), manufacturing systems (machines, equipment and tooling), metrology and quality in manufacturing, product lifecycle management (PLM) technologies, and risks in manufacturing engineering and society.

The second edition, defined as a Joint Special Issue with the aim of covering the wide range of research lines developed by the members and collaborators of the MES and other researchers within the field of Manufacturing Engineering, collected 39 contributions—29 in Materials and 10 in Applied Sciences.

In the third edition, finally, the Joint Special Issue successfully gathered a total of 31 papers (17 in Materials and 14 in Applied Sciences).

The main objective of the “Special Issue of the Manufacturing Engineering Society 2021 (SIMES 2021)” is to publish outstanding papers presenting cutting-edge advances in the field of Manufacturing Engineering, focusing on materials processing, as well as on experimental and theoretical results within applied sciences.

The Special Issue aims to explore the evolution of traditional manufacturing models toward the new requirements of the Manufacturing Industry 4.0 and how manufacturing professionals should face the resulting competitive challenges in the context of an ever-increasing use of digital information systems and communication technologies.

Contributions on emerging methods and technologies such as those related to additive manufacturing will have special relevance within this Special Issue, as well as those where sustainability and environmental issues play a fundamental role in manufacturing.

The main topics covered by this Special Issue are scientific contributions on the following manufacturing research topics:

  • Additive manufacturing and 3D printing;
  • Advances and innovations in manufacturing processes;
  • Sustainable and green manufacturing;
  • Micro and nanomanufacturing;
  • Manufacturing of new materials;
  • Manufacturing systems: machines, equipment, and tooling;
  • Robotics, mechatronics, and manufacturing automation;
  • Metrology and quality in manufacturing;
  • Industry 4.0;
  • Product lifecycle management (PLM) technologies;
  • Design, modeling, and simulation in manufacturing engineering;
  • Production planning;
  • Manufacturing engineering and society.

The above list is not exhaustive, and papers on other topics associated with advances in manufacturing engineering are also welcome.

Especially welcome are all works with a clear application to the manufacturing field related to processing of materials, including ceramics, glasses, polymers (plastics), semiconductors, magnetic materials, medical implant materials and biological materials, silica and carbon materials, metals and metallic alloys, composites, coatings and films, pigments, application of techniques such as electron microscopy, X-ray diffraction, calorimetry and others, the analysis of manufacturing processes and systems, mechanics of materials, and tribology (friction, lubrication and wear).

It is our pleasure to invite professionals from industry, academic institutions, and research centers from around the world to submit their contributions to this Special Issue.

We hope this fourth edition of the Special Issue is as successful as the last three editions.

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  • Members of the Manufacturing Engineering Society will benefit from a 15% discount (approx. 280 €) on the article processing charges. If you are not a member yet, please find more information on how to join the society (here). Regular individual member fee 75 €/year, student fee 35 €/year.

Dr. Francisco Javier Trujillo Vilches
Dr. Álvaro Rodríguez-Prieto
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

  • 3D printing
  • additive manufacturing
  • advanced materials processing
  • assembly processes
  • coatings and films
  • digital manufacturing
  • forming
  • friction
  • green manufacturing
  • Industry 4.0
  • joining
  • machining
  • manufacturing automation
  • manufacturing systems
  • mechatronics
  • metrology

Published Papers (5 papers)

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Editorial

Jump to: Research

4 pages, 834 KiB  
Editorial
Special Issue of the Manufacturing Engineering Society—2021 (SIMES-2021)
by Francisco Javier Trujillo and Álvaro Rodríguez-Prieto
Appl. Sci. 2022, 12(13), 6666; https://doi.org/10.3390/app12136666 - 1 Jul 2022
Cited by 1 | Viewed by 1162
Abstract
After the complete success of the first [...] Full article
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021))
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Research

Jump to: Editorial

17 pages, 3618 KiB  
Article
Methodology to Optimize Quality Costs in Manufacturing Based on Multi-Criteria Analysis and Lean Strategies
by Lorena Pérez-Fernández, Miguel A. Sebastián and Cristina González-Gaya
Appl. Sci. 2022, 12(7), 3295; https://doi.org/10.3390/app12073295 - 24 Mar 2022
Cited by 7 | Viewed by 2361
Abstract
Manufacturing quality cost optimization is a priority in any manufacturing sector due to quality issues impacting companies’ reputations and has financial consequences. Quality costs are composed of tangible and intangible costs, however, only tangible costs used to be analyzed because there is no [...] Read more.
Manufacturing quality cost optimization is a priority in any manufacturing sector due to quality issues impacting companies’ reputations and has financial consequences. Quality costs are composed of tangible and intangible costs, however, only tangible costs used to be analyzed because there is no suitable methodology for measuring intangible costs. In this context, an innovative decision support system is developed with an empirical base, applying Analytical Hierarchy Process (AHP), Analytical Network Process (ANP), and Lean methodology to reduce all quality costs in an efficient way. In quality departments, perceptions, thoughts, and judgments (intangible costs) are not measured and controlled. This study develops an innovative methodology that allows to address this issue in an effective way. Another major innovation is the application of both multi-criteria methodologies to obtain the best combined result for decision making and the optimization of this result, developing an effort–impact matrix based on Lean manufacturing methodology. This system speeds up the decision-making process and assures its efficiency for quality department applications. Moreover, this decision support system may be applicable to any manufacturing sector. Full article
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021))
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17 pages, 2645 KiB  
Article
Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques
by Patricia I. Benito, Miguel A. Sebastián and Cristina González-Gaya
Appl. Sci. 2021, 11(24), 11979; https://doi.org/10.3390/app112411979 - 16 Dec 2021
Cited by 5 | Viewed by 1349
Abstract
This paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of [...] Read more.
This paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of thermal comfort for different pairs of indoor temperature and humidity values according to activity, time, and season, verified in the workplace, were obtained. The results obtained were compared to other statistical models of linear regression used for thermal comfort, thus observing that comfort temperature values are within a same range, yet the network offered more information since a range of options for interior design parameters (temperature/relative humidity) was offered for different work, time, and season conditions. Additionally, if compared with static models of heat exchange, the contribution of Bayesian networks is noted when considering a context of actual operability and adaptability conditions to the environment, which is promising for developing thermal comfort intelligent systems, especially for the development of sustainable settings within the Industry 4.0 paradigm. Full article
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021))
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15 pages, 412 KiB  
Article
A Comparative Study on Teaching Methodologies Applied in Engineering and Manufacturing Process Subjects during the COVID-19 Pandemic in 2020 and 2021
by Óscar López, Alfonso González, Francisco J. Álvarez and David Rodríguez
Appl. Sci. 2021, 11(23), 11519; https://doi.org/10.3390/app112311519 - 5 Dec 2021
Cited by 2 | Viewed by 2573
Abstract
Specific disciplines in engineering, such as manufacturing processes, require students in their academic stage to pay special attention, given the possible changes that may affect the acquisition of competencies. In an environment of uncertainty, such as a global pandemic, teaching must adapt without [...] Read more.
Specific disciplines in engineering, such as manufacturing processes, require students in their academic stage to pay special attention, given the possible changes that may affect the acquisition of competencies. In an environment of uncertainty, such as a global pandemic, teaching must adapt without losing the effective delivery of content to students. The health and safety measures applied during the first months of the pandemic led to a different type of teaching to that which had customarily been applied, such as synchronous and asynchronous methodologies defined by the university’s governing bodies, where face-to-face and online methodologies coexisted in the same academic year. All of this avoided interrupting the academic year. This paper studies the results achieved in this uncertain environment, extends them and compares them with the following year, where only the face-to-face methodology was applied to the students enrolled in Manufacturing Processes 2 at the Centro Universitario de Mérida within the Bachelor’s Degree in Design Engineering and New Product Development (Grado en Ingeniería en Diseño y Desarrollo de Nuevos Productos -GIDIDP-). An analysis of variance (ANOVA) was applied to the data obtained to locate the significant differences between the samples taken in the first year with online and face-to-face teaching methodologies and those taken in the second year with an exclusively face-to-face methodology. When comparing the results, maintaining face-to-face teaching proved essential, as it contributes towards achieving better marks or maintaining the level. However, online methodologies also help as an additional tool to acquire other knowledge and specific skills in these technical engineering subjects, specifically those dealing with the manufacturing processes addressed in this study. Full article
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021))
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10 pages, 10518 KiB  
Article
Conceptual Classification of Leading Indicators for the Dynamic Analysis of Emerging Risks in Integrated Management Systems
by Francisco Brocal Fernandez, Alberto Sanchez-Lite, José Luis Fuentes-Bargues, Miguel Á. Sebastian and Cristina González-Gaya
Appl. Sci. 2021, 11(22), 10921; https://doi.org/10.3390/app112210921 - 18 Nov 2021
Cited by 1 | Viewed by 1662
Abstract
Companies that implement Integrated Management Systems (IMS) require approaches that optimize resources and results. In the case of IMS of a new or emerging nature, the use of dynamics risk analysis approaches and the integration of real-time monitoring data in the risk assessment [...] Read more.
Companies that implement Integrated Management Systems (IMS) require approaches that optimize resources and results. In the case of IMS of a new or emerging nature, the use of dynamics risk analysis approaches and the integration of real-time monitoring data in the risk assessment process offers news perspectives. The objective of this work is to identify and classify leading indicators that facilitate the dynamic analyses of emerging risks in an IMS for quality, environment, and safety. For it, such indicator analysis has been based on a bibliographic analysis. Regarding results, firstly, a structure of indicators emerges configured of three categories organized in two levels. At the first level, it is established by the indicators of the IMS which can be integrated. The second level is configured of two categories of interrelated indicators, that is, process integrity indicators and occupational risks indicators. In turn, each of these three categories has two dimensions. The first dimension represents the direction of the indicator, leading or lagging indicator. The second dimension represents the risk nature, emerging or traditional risk. Secondly, a classification of the leading indicators is derived according to the categories of the indicators and the risk nature. This classification shows the direction of the leading indicators as well as qualitative graduation of the potential associated consequences. Said theoretical framework has been applied to a case study configured by a manufacturing process. Thus, a conceptual scheme has been developed that represents the first step towards a more in-depth and detailed development that allows the identification and definition of specific leading indicators within an IMS from a dynamic and emerging risk perspective. Full article
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society-2021 (SIMES-2021))
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