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Machines
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Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration
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Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 36598

Special Issue Editors

Dr. Angelos P. Markopoulos

E-Mail Website
Guest Editor
Section of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Athens, Greece
Interests: nanotechnology; artificial intelligence and neural networks; precision and ultraprecision machining; nanomaterials; non-conventional machining; molecular dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Cheng

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, London UB8 3PH, UK
Interests: design of high precision machines; ultraprecision machining systems; micro-cutting mechanics and physics; multiscale multiphysics modelling and analysis; smart cutting tools and smart machining; physical AI for manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Emmanouil-Lazaros Papazoglou

E-Mail Website
Guest Editor
Laboratory of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
Interests: additive manufacturing; non-conventional machining processes; numerical modeling methods; FEM; optimization methods; statistical methods
Special Issues, Collections and Topics in MDPI journals
Mr. Panagiotis Karmiris-Obratański

E-Mail Website
Guest Editor
Department of Manufacturing Systems, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: metal cutting and cutting tools; non-conventional machining; surface topography; surface metrology; materials science; optimization of process parameters; friction stir processes; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of manufacturing technologies continues to be a cornerstone of innovation and industrial growth. This Special Issue, based on the topics of the IManEE 2024 Conference, is expected to bring together pioneering research and technological advancements in the interdisciplinary field of advanced manufacturing. The issue aims to cover a wide array of topics, including the latest developments in forming processes for both metallic and composite materials, as well as the progress in machining and abrasive processes, with a particular emphasis on the tribological challenges inherent in them. Moreover, sustainability also remains a key focus, as various researchers are still exploring environmentally conscious manufacturing processes alongside non-conventional and emerging techniques. Furthermore, the cutting-edge realms of welding, casting, and additive manufacturing, are other interesting scientific fields that deserve intense investigation in order to highlight their role in modern industry. Apart from individual processes, the integration of manufacturing systems, robotics, and automation should be examined, alongside critical discussions on metrology, quality assurance, and process monitoring and control. Embracing the digital transformation of Industry 4.0, this issue also welcomes contributions relevant to simulation techniques for manufacturing processes and the evolving field of remanufacturing. Together, these contributions can offer a comprehensive perspective on the state-of-the-art in manufacturing science, providing valuable insights for researchers, engineers, and industry professionals.

The Special Issue, derived from the IManEE Conference, is well-aligned with the scope of the MDPI journal Machines, which emphasizes various aspects of machine design, theory, and application. The topics within this issue, such as forming processes of metallic and composite materials, machining, and abrasive processes, are integral to advanced manufacturing, which is a key focus area of the journal. Additionally, the exploration of tribological aspects of forming and machining processes fits directly within the journal's interest in friction and tribology, which are crucial for optimizing machine performance and longevity. The inclusion of content on automation, control, and robotics in manufacturing systems is highly relevant to the journal's emphasis on automation and control, as well as mechatronics and intelligent machines. Furthermore, discussions on process monitoring, control, and metrology tie into the journal's focus on condition monitoring, diagnostics, and ensuring the reliability of machines. Finally, the attention to non-conventional manufacturing processes, sustainability, and Industry 4.0 aligns with the journal’s interest in the future of machine technology, particularly in areas such as electromechatronics and advanced manufacturing. This Special Issue, therefore, can contribute significantly to the journal’s mission by advancing knowledge and innovation in key areas of machine design and application.

Dr. Angelos P. Markopoulos
Prof. Dr. Kai Cheng
Dr. Emmanouil-Lazaros Papazoglou
Mr. Panagiotis Karmiris-Obratański
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 250 words) can be sent to the Editorial Office for assessment.

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

  • forming processes of metallic and composite materials
  • machining and abrasive processes
  • tribological aspects of forming and machining processes
  • sustainability aspects of manufacturing processes
  • non-conventional manufacturing processes
  • advances in welding and casting processes
  • additive manufacturing processes
  • manufacturing systems, robotics and automation
  • process monitoring and control, remanufacturing
  • simulation of manufacturing processes
  • digital/e-manufacturing and digital twin applications
  • ultraprecision and micro/nano manufacturing: machines, processes, tooling and their systematic integration

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

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Research

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23 pages, 3963 KB  
Article
Non-Circular Section Machining of Glass by Lathe-Type Electrochemical Discharge Machine with Force-Controlled Tool Electrode Holder
by Katsushi Furutani and Toshiki Irie
Machines 2026, 14(3), 308; https://doi.org/10.3390/machines14030308 - 9 Mar 2026
Viewed by 1137
Abstract
Electrochemical discharge machining (ECDM) with low machining reaction forces is useful for machining hard and brittle materials, which are required in precision equipment. Lathe-type ECD machines have been proposed to machine axisymmetric shapes while reducing cracks caused by thermal expansion, and they are [...] Read more.
Electrochemical discharge machining (ECDM) with low machining reaction forces is useful for machining hard and brittle materials, which are required in precision equipment. Lathe-type ECD machines have been proposed to machine axisymmetric shapes while reducing cracks caused by thermal expansion, and they are suitable for thin workpiece machining due to the small reaction force. This paper demonstrates the micromachining of non-circular cross-sections using a lathe-type ECD machine equipped with an improved force-controlled tool electrode holder. The tool electrode holder combining a voice coil motor (VCM) with leaf springs arranged in parallel was built. This holder achieves both flexibility in the longitudinal direction of the tool electrode and high rigidity in the lateral direction. The relationship between the VCM current, tool electrode shift within the tool electrode holder, and thrust force was approximated using a polynomial. Consequently, this device allows for the stable, small contact force required in micromachining. An on-machine shape measurement method was also carried out by combining the tool electrode shift with the motion of an XZ stage. As a demonstration for non-circular cross-section machining, a square cross-section was grooved from a cylindrical glass rod. The removal and measurement processes were alternately repeated to achieve precision. During ECDM, the on/off of the DC power supply for ECDM was synchronized with the rotation of the workpiece. The measurement results indicated some dimensional errors, including bulging at the middle of sides and excessive removal at corners. The bulging was mainly caused by drift due to thermal expansion of the stage, as well as tool electrode wear. Since the tool electrode comes into close proximity to with the machined surface, the discharge from the side surface of the tool electrode caused excessive removal at the corners. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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22 pages, 6391 KB  
Article
A Multimodal Machine Learning Framework for Optimizing Coated Cutting Tool Performance in CNC Turning Operations
by Paschalis Charalampous
Machines 2026, 14(2), 161; https://doi.org/10.3390/machines14020161 - 1 Feb 2026
Viewed by 758
Abstract
The present study introduces a comprehensive machine-learning framework for modeling, interpretation and optimization of the CNC turning procedure employing coated cutting inserts. The primary novelty of this work lies in the integrated pipeline that leverages a multimodal experimental dataset in order to simultaneously [...] Read more.
The present study introduces a comprehensive machine-learning framework for modeling, interpretation and optimization of the CNC turning procedure employing coated cutting inserts. The primary novelty of this work lies in the integrated pipeline that leverages a multimodal experimental dataset in order to simultaneously model surface roughness and residual stresses, as well as to interpret these predictions within a unified optimization scheme. Particularly, a deep learning model was developed incorporating a convolutional encoder for analyzing time-series signals and a static encoder for the investigated machining parameters. This fused representation enabled accurate multi-task predictions, capturing the thermo-mechanical interactions that govern surface integrity. Additionally, to ensure interpretability, a surrogate meta-model based on the deep model’s predictions was established and evaluated via Shapley Additive Explanations. This analysis quantified the relative influence of each cutting parameter, linking data-driven insights to contact-mechanical principles. Furthermore, a multi-objective optimization scheme was implemented to derive Pareto optimal trade-offs among the examined parameters that could enhance the machining efficiency. Overall, the integration of deep learning, interpretable modeling and optimization established a coherent framework for data-driven decision making in turning, highlighting the importance of model transparency in advancing intelligent manufacturing systems. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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19 pages, 4331 KB  
Article
Machining Process Optimization Using a Model Based on Criterial Functional Dependence
by Peter Pavol Monka, Katarina Monkova, Ondrej Bilek and Martin Reznicek
Machines 2025, 13(6), 478; https://doi.org/10.3390/machines13060478 - 1 Jun 2025
Viewed by 1918
Abstract
This research deals with the optimization of the machining process using a model based on criterial functional dependence hypothesis. The basis of this hypothesis is the assertion that for each production process of a given product with many input parameters, at given known [...] Read more.
This research deals with the optimization of the machining process using a model based on criterial functional dependence hypothesis. The basis of this hypothesis is the assertion that for each production process of a given product with many input parameters, at given known requirements and conditions, it is possible to determine the minimum/maximum local extremum, that is, to find the most suitable conditions under which the criterion is achieved. To verify the optimization model, three different cutting tools (cutting inserts) were compared within the criteria functions set for cutting force Fc, process power P, and surface roughness characteristics Rz, all with two independent variables—cutting speed vc and feed f. The technology of turning with longitudinal external machining of the cylindrical surface was selected as the operation for the experiment. Taking into account the importance of individual criteria for real practice and the minimum extreme values achieved (a surface roughness Rz = 2.2 μm and cutting power p = 14,700 W at vc = 145 m/min and f = 0.8 mm), the tool with a linear cutting edge (LCE) designed at the authors’ workplace appeared as the most suitable tool for machining operation under the given conditions when compared with commercially produced cutting tools TCMT 16T308-PR 4035 and CNMG 120408-WM 4025. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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22 pages, 6123 KB  
Article
Increasing 3D Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations
by Moustapha Jadayel and Farbod Khameneifar
Machines 2025, 13(5), 382; https://doi.org/10.3390/machines13050382 - 1 May 2025
Cited by 2 | Viewed by 2210
Abstract
As Additive Manufacturing (AM) evolves from prototyping to full-scale production, improving geometric accuracy becomes increasingly critical, especially for applications requiring high dimensional fidelity. This study proposes a machine learning-based approach to enhance the geometric accuracy of 3D printed parts produced by Fused Filament [...] Read more.
As Additive Manufacturing (AM) evolves from prototyping to full-scale production, improving geometric accuracy becomes increasingly critical, especially for applications requiring high dimensional fidelity. This study proposes a machine learning-based approach to enhance the geometric accuracy of 3D printed parts produced by Fused Filament Fabrication (FFF), a widely used material extrusion process in which thermoplastic filament is heated and deposited layer by layer to form a part. Our method relies on a Convolutional Neural Network (CNN) trained to predict a systematic deviation field based on 3D scan data of a sacrificial print. These scans are acquired using a structured light 3D scanner, which provides detailed surface information on geometric deviations that arise during the printing process. The predicted deviation field is then inverted and applied to the digital model to generate a compensated geometry, which, when printed, offsets the errors observed in the original part. Experimental validation using a complex reference geometry shows that the proposed compensation method achieves an 88.5% reduction in mean absolute geometric deviation compared to the uncompensated print. This significant improvement underscores the CNN’s ability to generalize across geometric features and capture systematic deformation patterns inherent to FFF. The results demonstrate the potential of combining 3D scanning and deep learning to enable adaptive, data-driven compensation strategies in AM. The method proposed in this paper contributes to reducing trial-and-error iterations, improving part quality, and facilitating the broader adoption of FFF for precision-demanding industrial applications. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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17 pages, 2587 KB  
Article
A Cyber Manufacturing IoT System for Adaptive Machine Learning Model Deployment by Interactive Causality-Enabled Self-Labeling
by Yutian Ren, Yuqi He, Xuyin Zhang, Aaron Yen and Guann-Pyng Li
Machines 2025, 13(4), 304; https://doi.org/10.3390/machines13040304 - 8 Apr 2025
Cited by 1 | Viewed by 1350
Abstract
Machine learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an [...] Read more.
Machine learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality-enabled self-labeling method has been proposed to advance adaptive ML applications in cyber–physical systems, especially manufacturing, by automatically adapting and personalizing ML models after deployment to counter data distribution shifts. The unique features of the self-labeling method require a novel software system to support dynamism at various levels. This paper proposes the AdaptIoT system, comprising an end-to-end data streaming pipeline, ML service integration, and an automated self-labeling service. The self-labeling service consists of causal knowledge bases and automated full-cycle self-labeling workflows to adapt multiple ML models simultaneously. AdaptIoT employs a containerized microservice architecture to deliver a scalable and portable solution for small and medium-sized manufacturers. A field demonstration of a self-labeling adaptive ML application is conducted with a makerspace and shows reliable performance with comparable accuracy at 98.3%. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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20 pages, 8714 KB  
Article
Optimization of Toolpath Planning and CNC Machine Performance in Time-Efficient Machining
by Arbnor Pajaziti, Orlat Tafilaj, Afrim Gjelaj and Besart Berisha
Machines 2025, 13(1), 65; https://doi.org/10.3390/machines13010065 - 17 Jan 2025
Cited by 10 | Viewed by 10965
Abstract
This study explores the optimization of the machining time in CNC milling machines by varying the machine parameters and toolpath strategies. Using the ICAM3D simulation software version 3.1.0, this approach focuses on minimizing the machining time while adhering to operational constraints. In addition, [...] Read more.
This study explores the optimization of the machining time in CNC milling machines by varying the machine parameters and toolpath strategies. Using the ICAM3D simulation software version 3.1.0, this approach focuses on minimizing the machining time while adhering to operational constraints. In addition, a novel approach to the optimization of the G-code in time machining, focusing on reducing the machining time while maintaining the required precision and quality of the finished product, is presented. We propose a method that integrates advanced algorithms to identify and eliminate redundant movements, optimize the toolpaths, and improve the machining strategies. The experimental results demonstrate a significant reduction in the machining time without compromising the machining accuracy, offering substantial cost savings and efficiency improvements for industrial applications. The importance of this work lies in the correct choice of the toolpath strategy. In the P3 project, the optimization process reduced the machining time from 15 min and 23 s to 13 min and 33 s by utilizing the optimized G-code. The initial machining time of 20 min and 2 s corresponds to the completion of the P3 project when the CNC machine was operated at 75% speed. To further enhance efficiency, additional software tools such as ARTCAM and ASPIRE have been utilized to implement a new toolpath strategy. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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16 pages, 3706 KB  
Article
Development of a Web-Based e-Portal for Freeform Surfaced Lens Design and Manufacturing and Its Implementation Perspectives
by Shangkuan Liu, Kai Cheng and Negin Dianat
Machines 2025, 13(1), 59; https://doi.org/10.3390/machines13010059 - 16 Jan 2025
Cited by 1 | Viewed by 1580
Abstract
In modern freeform surfaced optics manufacturing, ultraprecision machining through single-point diamond turning (SPDT) plays a crucial role due to its ability to meet the high accuracy demands of optical design and stringent surface quality requirements of the final optic. The process involves meticulous [...] Read more.
In modern freeform surfaced optics manufacturing, ultraprecision machining through single-point diamond turning (SPDT) plays a crucial role due to its ability to meet the high accuracy demands of optical design and stringent surface quality requirements of the final optic. The process involves meticulous steps, including optic surface modeling and analysis, optic design, machining toolpath generation, and manufacturing. This paper presents an integrated approach to customized precision design and the manufacturing of freeform surfaced varifocal lenses through a web-based e-portal. The approach implements an e-portal-driven manufacturing system that seamlessly integrates lens design, modeling and analysis, toolpath generation for ultraprecision machining, mass personalized customization, and service delivery. The e-portal is specifically designed to meet the stringent demands of personalized mass customization, and to offer a highly interactive and transparent experience for the lens users. By using Shiny and R-script programming for platform development and combining COMSOL Multiphysics for the ray tracing simulation, the e-portal leverages open-source technologies to provide manufacturing service agility, responsiveness, and accessibility. Furthermore, the integration of R-script and Shiny programming allows for advanced interactive information processing, which also enables the e-portal-driven manufacturing system to be well suited for personalized complex products such as freeform surfaced lenses. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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Review

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26 pages, 3309 KB  
Review
Quality Control in Seamless Copper Tube Manufacturing: A Narrative Review & Future Perspective
by Kyriakos Sabatakakis, Apostolos Kaimenopoulos, Dimitrios Karatasios and Panagiotis Stavropoulos
Machines 2026, 14(4), 428; https://doi.org/10.3390/machines14040428 - 11 Apr 2026
Viewed by 293
Abstract
Seamless Copper Tube Manufacturing (SCTM) is a multi-stage manufacturing chain, typically comprising billet casting, hot extrusion, cold drawing or pilgering, intermediate annealing, and finishing operations. Despite the fact that quality control (QC) practices are implemented at individual stages, many product deviations originated from [...] Read more.
Seamless Copper Tube Manufacturing (SCTM) is a multi-stage manufacturing chain, typically comprising billet casting, hot extrusion, cold drawing or pilgering, intermediate annealing, and finishing operations. Despite the fact that quality control (QC) practices are implemented at individual stages, many product deviations originated from cumulative thermomechanical and metallurgical interactions across multiple processes. Thus, although the current stage-wise QC schema ensures compliance with quality standards, it’s questionable whether it can identify root causes or implement proactive QC at the production level. This study presents a narrative review of QC approaches in SCTM, examining the production chain across key quality domains, including billet integrity, extrusion tooling condition, dimensional control, surface and internal defect detection, annealing atmosphere monitoring, and inner-surface cleanliness. The industrial practices are critically compared with research approaches in numerical modelling, advanced sensing technologies, and data-driven monitoring methods. Results confirmed that dimensional instability, defect formation, surface contamination, and microstructural variation in the tube are influenced by interactions among factors such as billet quality, thermomechanical conditions during extrusion and drawing, annealing conditions, tooling conditions, lubrication regimes, and handling between processing steps. Their analysis indicated that the main limitation of current QC frameworks is not the lack of monitoring or modelling technologies but the limited integration of process data across the manufacturing chain. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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29 pages, 4987 KB  
Review
A Review of Recent Advancements in Heat Pump Systems and Developments in Microchannel Heat Exchangers
by Roopesh Chowdary Sureddi, Liang Li, Hongwei Wu, Niccolo Giannetti, Kiyoshi Saito and David Rees
Machines 2025, 13(4), 333; https://doi.org/10.3390/machines13040333 - 18 Apr 2025
Cited by 4 | Viewed by 6765
Abstract
Heating and cooling are the main concerns across a wide range of sectors, including residential buildings, industrial facilities, transportation and commercial enterprises. This being the case, a continuous rise in the cost of energy demands more effective ways to conserve energy. Heat pump [...] Read more.
Heating and cooling are the main concerns across a wide range of sectors, including residential buildings, industrial facilities, transportation and commercial enterprises. This being the case, a continuous rise in the cost of energy demands more effective ways to conserve energy. Heat pump (HP) systems provide the one of the best possible solutions to this problem as they offer an economical and energy-efficient system. In this review, HP systems are overviewed as energy-efficient and cost-effective solutions, focusing on their characteristic properties but also on enhancements, novel techniques and the use of heat exchangers (HXs), and microchannel heat exchangers (MCHEs) in these systems, as well as their development in recent years and their limitations. The main factors contributing to variations in the performance of HP systems are temperature and humidity in the ambient atmosphere. The present study is expected to support numerical and experimental performance analysis, and design miniaturisation via MCHEs. Unique designs or manufacturing techniques in MCHEs; various configurations in HP systems, depending on their load and environmental conditions; various nanofluids; and a comparison of nanofluids with different base metals are presented and discussed. Comparisons between various MCHEs and their respective limitations provide evidence-based guidelines for technology selection and designs for optimised operation at given environmental and load conditions. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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Other

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27 pages, 4974 KB  
Systematic Review
Engineering Innovations for Polyvinyl Chloride (PVC) Recycling: A Systematic Review of Advances, Challenges, and Future Directions in Circular Economy Integration
by Alexander Chidara, Kai Cheng and David Gallear
Machines 2025, 13(5), 362; https://doi.org/10.3390/machines13050362 - 28 Apr 2025
Cited by 10 | Viewed by 7540
Abstract
Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed [...] Read more.
Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
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