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Special Issue "Design and Manufacturing: An Industry 4.0 Perspective"

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 8627

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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: computational design; CAD/CAM/CAE; digital manufacturing; product design; FEA; 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: manufacturing technology; machining processes; non-conventional machining; modeling and simulation
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 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.

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

Published Papers (5 papers)

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Research

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Open AccessArticle

Optimization of Selective Laser Sintering/Melting Operations by Using a Virus-Evolutionary Genetic Algorithm

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 3 | Viewed by 988

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 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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Open AccessArticle

A Novel Computational-Based Visual Brand Identity (CbVBI) Product Design Methodology

and

Machines 2022, 10(11), 1065; https://doi.org/10.3390/machines10111065 - 11 Nov 2022

Cited by 2 | Viewed by 1016

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 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) Solidworks^TM (VBA event-driven programming language) is used. The second case study focuses on the visual programming language of Grasshopper^TM, which is incorporated within Rhinoceros3D^TM. 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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Open AccessCommunication

Drape of Composite Structures Made of Textile and 3D Printed Geometries

and

Machines 2022, 10(7), 587; https://doi.org/10.3390/machines10070587 - 19 Jul 2022

Cited by 3 | Viewed by 1480

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 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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Open AccessArticle

Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

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

Viewed by 1750

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 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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Open AccessEditor’s ChoiceReview

Assessment of Industry 4.0 for Modern Manufacturing Ecosystem: A Systematic Survey of Surveys

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 10 | Viewed by 2219

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 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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