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Machines
Special Issues
Design and Manufacturing: An Industry 4.0 Perspective
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Design and Manufacturing: An Industry 4.0 Perspective

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 21046

Special Issue Editors

Prof. Dr. Panagiotis Kyratsis

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Guest Editor
Department of Product and Systems Design Engineering, University of Western Macedonia, 50100 Kila Kozani, Greece
Interests: additive manufacturing; digital manufacturing; product design; FEA; computational design; Industry 4.0; prototyping; reverse engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Angelos P. Markopoulos

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Guest Editor
Section of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, 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. Henrique de Amorim Almeida

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Guest Editor
Escola Superior de Tecnologia e Gestão de Leiria, Leiria, Portugal
Interests: CAD/CAM/CAE; additive manufacturing; direct digital fabrication; manufacturing and machining; product design; reverse engineering
Dr. Tatjana Spahiu

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Guest Editor
Faculty of Mechanical Engineering, Textile and Fashion Department, Polytechnic University of Tirana, Tirana, Albania
Interests: CAD-based manufacturing; CAD/CAM/CAE; manufacturing and machining in the fashion industry; reverse engineering and prototyping of garments and footwear
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial investments include the use of advanced technologies, which refine, accelerate, improve the quality of, and raise the profitability of the stakeholders. As part of Industry 4.0, 3D printing technology, the Internet of Things (IoT), virtual and augmented reality, artificial intelligence, computer-based simulations, etc., offer considerable opportunities for transforming the traditional approach of product design and manufacturing towards a computer-based innovative way of work. Not only have design and manufacturing changed, but also researchers, engineers and the academic works towards incorporating high-end applications to all stages of a product’s life cycle, each of them shaping the future of the industry by creating both new opportunities within specific sectors and new challenging demands.

This Special Issue aims to assemble recent advances in design and manufacturing from an Industry 4.0 point of view, topics of great interest including frameworks and applications offering advantages towards achieving the goals of Industry 4.0. 

Prof. Dr. Panagiotis Kyratsis
Dr. Angelos P. Markopoulos
Prof. Dr. Henrique de Amorim Almeida
Dr. Tatjana Spahiu
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

  • Industry 4.0 applications
  • 3D printing and additive manufacturing
  • Internet of Things (IoT)
  • 3D virtual and augmented reality
  • 3D prototyping
  • artificial intelligence and machine learning
  • CAD/CAM/CAE systems
  • simulations and reverse engineering
  • modern machining and manufacturing
  • applications and simulations in robotics
  • sustainability and design based on circular economy principles
  • product lifecycle management systems (PLM)
  • sustainable product design and manufacturing
  • computational design, parametric design
  • design for X
  • interoperability, modularity and decentralization
  • remote monitoring and control
  • real-time supply chain optimization
  • digital quality management

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Published Papers (6 papers)

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Research

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25 pages, 5050 KiB  
Article
Development of a Human-Centric Autonomous Heating, Ventilation, and Air Conditioning Control System Enhanced for Industry 5.0 Chemical Fiber Manufacturing
by Madankumar Balasubramani, Jerry Chen, Rick Chang and Jiann-Shing Shieh
Machines 2025, 13(5), 421; https://doi.org/10.3390/machines13050421 - 17 May 2025
Viewed by 219
Abstract
This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor [...] Read more.
This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R2 up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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21 pages, 1538 KiB  
Article
Optimization of Selective Laser Sintering/Melting Operations by Using a Virus-Evolutionary Genetic Algorithm
by Nikolaos A. Fountas, John D. Kechagias and Nikolaos M. Vaxevanidis
Machines 2023, 11(1), 95; https://doi.org/10.3390/machines11010095 - 11 Jan 2023
Cited by 23 | Viewed by 2689
Abstract
This work presents the multi-objective optimization results of three experimental cases involving the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem aimed at maximizing the [...] Read more.
This work presents the multi-objective optimization results of three experimental cases involving the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem aimed at maximizing the density of Ti6Al4V specimens, with layer thickness, linear energy density, hatching space and scanning strategy as the independent process parameters. The second one refers to the formulation of a two-objective optimization problem aimed at maximizing both the hardness and tensile strength of Ti6Al4V samples, with laser power, scanning speed, hatch spacing, scan pattern angle and heat treatment temperature as the independent process parameters. Finally, the third case deals with the formulation of a three-objective optimization problem aimed at minimizing mean surface roughness, while maximizing the density and hardness of laser-melted L316 stainless steel powder. The results obtained by the proposed algorithm are statistically compared to those obtained by the Greywolf (GWO), Multi-verse (MVO), Antlion (ALO), and dragonfly (DA) algorithms. Algorithm-specific parameters for all the algorithms including those of the virus-evolutionary genetic algorithm were examined by performing systematic response surface experiments to find the beneficial settings and perform comparisons under equal terms. The results have shown that the virus-evolutionary genetic algorithm is superior to the heuristics that were tested, at least on the basis of evaluating regression models as fitness functions. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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15 pages, 5442 KiB  
Article
A Novel Computational-Based Visual Brand Identity (CbVBI) Product Design Methodology
by Athanasios Manavis, Anastasios Tzotzis, Apostolos Tsagaris and Panagiotis Kyratsis
Machines 2022, 10(11), 1065; https://doi.org/10.3390/machines10111065 - 11 Nov 2022
Cited by 9 | Viewed by 2398
Abstract
Product design is a promising field for the application of new technologies and methodologies emerging from the digital evolution of Industry 4.0. A great number of tools have been developed in order to accentuate the use of modern Computer-Aided Design (CAD) systems and [...] Read more.
Product design is a promising field for the application of new technologies and methodologies emerging from the digital evolution of Industry 4.0. A great number of tools have been developed in order to accentuate the use of modern Computer-Aided Design (CAD) systems and computational design techniques for design customization in product applications. The present paper deals with the development of two different applications for designing furniture based on the Computational-based Visual Brand Identity (CbVBI) design methodology. For the first case study, the Application Programming Interface (API) SolidworksTM (VBA event-driven programming language) is used. The second case study focuses on the visual programming language of GrasshopperTM, which is incorporated within Rhinoceros3DTM. The proposed case studies offer a great deal of flexibility in both design and manufacturing, while many design alternatives could become available in a very short period. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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17 pages, 4820 KiB  
Communication
Drape of Composite Structures Made of Textile and 3D Printed Geometries
by Tatjana Spahiu, Zlatin Zlatev, Elita Ibrahimaj, Julieta Ilieva and Ermira Shehi
Machines 2022, 10(7), 587; https://doi.org/10.3390/machines10070587 - 19 Jul 2022
Cited by 6 | Viewed by 3012
Abstract
Applications of 3D printing in the fashion industry have continued to attract interest from academia and industry in order to improve and add functionalities to products. Among these applications, an interesting one is 3D printing on textile fabric. Composite structures created by 3D [...] Read more.
Applications of 3D printing in the fashion industry have continued to attract interest from academia and industry in order to improve and add functionalities to products. Among these applications, an interesting one is 3D printing on textile fabric. Composite structures created by 3D printing and textile fabric change a drape by improving or worsening its appearance. The scope of this work is to evaluate the effect of various 3D printed geometries on textile fabric regarding fabric drapes. The drape coefficient of the created composite structure is evaluated using a drape tester built according to EN ISO 9073-9. The results taken are compared with an algorithm developed for determining drape parameters and 3D form representation using color digital images and their image histograms. The measured values of the drape coefficient are close, with a minimal difference, up to 4%. The 3D printed patterns show a significant effect on the drape coefficient of textile fabrics by depicting another way to modify fabric drapes and create complex shapes by using less material. This can be seen as an advantage in the fashion industry where complex geometries can be added to textile fabrics, while changing fabric drape and product personalization and adding functionalities for garments and technical textiles. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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27 pages, 2438 KiB  
Article
Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study
by Abdessalem Jerbi, Wafik Hachicha, Awad M. Aljuaid, Neila Khabou Masmoudi and Faouzi Masmoudi
Machines 2022, 10(4), 247; https://doi.org/10.3390/machines10040247 - 30 Mar 2022
Cited by 5 | Viewed by 3689
Abstract
One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic [...] Read more.
One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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Review

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26 pages, 2644 KiB  
Review
Assessment of Industry 4.0 for Modern Manufacturing Ecosystem: A Systematic Survey of Surveys
by Fotios K. Konstantinidis, Nikolaos Myrillas, Spyridon G. Mouroutsos, Dimitrios Koulouriotis and Antonios Gasteratos
Machines 2022, 10(9), 746; https://doi.org/10.3390/machines10090746 - 29 Aug 2022
Cited by 55 | Viewed by 5617
Abstract
The rise of the fourth industrial revolution aspires to digitize any traditional manufacturing process, paving the way for new organisation schemes and management principles that affect business models, the environment, and services across the entire value chain. During the last two decades, the [...] Read more.
The rise of the fourth industrial revolution aspires to digitize any traditional manufacturing process, paving the way for new organisation schemes and management principles that affect business models, the environment, and services across the entire value chain. During the last two decades, the generated advancements have been analysed and discussed from a bunch of technological and business perspectives gleaned from a variety of academic journals. With the aim to identify the digital footprint of Industry 4.0 in the current manufacturing ecosystem, a systematic literature survey of surveys is conducted here, based on survey academic articles that cover the current state-of-the-art. The 59 selected high-impact survey manuscripts are analysed using PRISMA principles and categorized according to their technologies under analysis and impact, providing valuable insights for the research and business community. Specifically, the influence Industry 4.0 exerts on traditional business models, small and medium-sized enterprises, decision-making processes, human–machine interaction, and circularity affairs are investigated and brought out, while research gaps, business opportunities, and their relevance to Industry 5.0 principles are pointed out. Full article
(This article belongs to the Special Issue Design and Manufacturing: An Industry 4.0 Perspective)
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