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Machines
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Sustainable Manufacturing and Green Processing Methods, 2nd Edition
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Sustainable Manufacturing and Green Processing Methods, 2nd Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Material Processing Technology".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1353

Special Issue Editors

Prof. Dr. Ali Khalfallah

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Guest Editor
CEMMPRE, Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal
Interests: multidisciplinary modeling of the mechanical behavior of materials; identification of thin-film properties; combination of computational physics; artificial intelligence; multi-scale simulations and materials characterization; recent exploration into tribology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carlos Leitao

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Guest Editor
Department of Mechanical Engineering, University of Coimbra, 3030-788 Coimbra, Portugal
Interests: friction stir welding; modelling; aluminum; mechanical characterization; digital image correlation; plasticity and microstructural characterization
Special Issues, Collections and Topics in MDPI journals
Dr. Elango Natarajan

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Department of Mechanical and Mechatronic Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia
Interests: composites; machining; optimization

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue, titled “Sustainable Manufacturing and Green Processing Methods” (https://www.mdpi.com/journal/machines/special_issues/5XNSF3BW3F), we are pleased to announce the next in the series, entitled “Sustainable Manufacturing and Green Processing Methods, 2nd Edition”.

The field of material processing technology plays a pivotal role in shaping the future of numerous industries, ranging from electronics and aerospace to healthcare and energy. As advancements continue to drive innovation, there is an increasing need to address the environmental impact associated with processing methods. The urgent demand for sustainable manufacturing practices and the adoption of green processing technologies has propelled research endeavors aimed at minimizing the environmental footprint while maintaining or enhancing the performance of manufactured components.

This Special Issue aims to explore the latest developments and cutting-edge research in sustainable manufacturing and green processing methods within the domain of material processing technology. By focusing on mitigating the environmental impact of material processing, this Issue seeks to highlight the interdisciplinary efforts in designing eco-friendly approaches, novel energy-efficient processing methods, and innovative technologies to achieve sustainable and green manufacturing processes. Additionally, this Special Issue intends to foster an exchange of knowledge, ideas, and advancements among researchers, engineers, and practitioners working in the field of manufacturing. By featuring a diverse collection of high-quality contributions, we aspire to facilitate an in-depth understanding of sustainable manufacturing practices, green processing techniques, and their implications across various production systems and industries.

Potential topics include, but are not limited to, the following:

eco-friendly manufacturing processing; energy-efficient manufacturing processes and technologies; waste reduction strategies and recycling methods; green solvents and chemicals in material processing; and life cycle assessment of manufacturing technologies.

Prof. Dr. Ali Khalfallah
Prof. Dr. Carlos Leitao
Dr. Elango Natarajan
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. Machines is an international peer-reviewed open access monthly 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
  • green processing methods
  • environmental impact
  • eco-friendly processing technologies
  • energy-efficient processes
  • waste reduction
  • recycling methods
  • environmental footprint
  • renewable resources
  • carbon footprint
  • resource efficiency
  • process optimization for waste reduction
  • environmental sustainability
  • circular economy
  • clean production

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 2415 KiB  
Article
Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining
by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang and Ali Khalfallah
Machines 2025, 13(7), 570; https://doi.org/10.3390/machines13070570 - 1 Jul 2025
Abstract
This paper proposes and demonstrates a domain-adapted ensemble machine learning approach for enhanced prediction of surface roughness (Ra) during the machining of polymeric materials. The proposed model methodology employs a two-stage pipelined architecture, where classified data are fed into the model for regressive [...] Read more.
This paper proposes and demonstrates a domain-adapted ensemble machine learning approach for enhanced prediction of surface roughness (Ra) during the machining of polymeric materials. The proposed model methodology employs a two-stage pipelined architecture, where classified data are fed into the model for regressive analysis. First, a classifier (Logistic Regression or XGBoost, selected based on performance) categorizes machining data into distinct regimes based on cutting Speed (Vc), feed rate (f), and depth of cut (ap) as inputs. This classification leverages output discretization to mitigate data imbalance and capture regime-specific patterns. Second, a regressor (Support Vector Regressor or XGBoost, selected based on performance) predicts Ra within each regime, utilizing the classifier’s output as an additional feature. This structured hybrid approach enables more robust prediction in small, noisy datasets characteristic of machining studies. To validate the methodology, experiments were conducted on Polyoxymethylene (POM), Polytetrafluoroethylene (PTFE), Polyether ether ketone (PEEK), and PEEK/MWCNT composite, using a L27 Design of Experiments (DoEs) matrix. Model performance was optimized using k-fold cross-validation and hyperparameter tuning via grid search, with R-squared and RMSE as evaluation metrics. The resulting meta-model demonstrated high accuracy (R2 > 90% for XGBoost regressor across all materials), significantly improving Ra prediction compared to single-model approaches. This enhanced predictive capability offers potential for optimizing machining processes and reducing material waste in polymer manufacturing. Full article
(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods, 2nd Edition)
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24 pages, 3600 KiB  
Article
Lean Tools Implementation Model in Shipbuilding Processes Under Conditions of Predominantly Custom Production
by Zoran Kunkera, Biserka Runje, Nataša Tošanović and Neven Hadžić
Machines 2025, 13(4), 260; https://doi.org/10.3390/machines13040260 - 22 Mar 2025
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Abstract
The European shipbuilding industry is primarily active in the niches of building vessels with high added value characterized by individual demand or eventual orders in smaller series—the authors approach this research motivated by the desire to contribute to maintaining its competitiveness on the [...] Read more.
The European shipbuilding industry is primarily active in the niches of building vessels with high added value characterized by individual demand or eventual orders in smaller series—the authors approach this research motivated by the desire to contribute to maintaining its competitiveness on the world market. To enhance business processes, shipyards have at their disposal, among others, digital technologies and Lean tools. However, the production of highly complex products in a business environment with complex inter-process relations among a large number of stakeholders also implies a highly demanding project of Lean methodology implementation. And according to the literature gap and available archival data, the outcome is very uncertain. Therefore, the authors conduct this research for the purpose of overcoming the risk of failure in completing the Lean implementation process with the aim of contributing to the transformation of the shipbuilding system into a smart and sustainable, or climate-neutral, one. As experts in the field of research and based on interviews with representatives of one of the European shipyards, the authors develop a Lean process management implementation model adapted not only to custom production in shipbuilding but also to other industries with similar characteristics. The model theoretically results not only in the successful closure of the Lean transformation process in an optimal time and at low costs but also in the simultaneous continuous improvement of shipbuilding processes during the implementation period. Moreover, the neutral influence of the business system’s organizational structure on the presented model adds originality to this study. Full article
(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods, 2nd Edition)
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