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Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and...
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Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 63947

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Roque Calvo

E-Mail Website
Guest Editor
Department of Mechanical, Chemical and Industrial Design Engineering, Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain
Interests: quality assurance engineering; manufacturing engineering; industrial engineering
Special Issues, Collections and Topics in MDPI journals
Dr. José A. Yagüe-Fabra

E-Mail Website
Guest Editor
I3A, Universidad de Zaragoza, María de Luna 3, 50018 Zaragoza, Spain
Interests: precision engineering; dimensional metrology; manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Guido Tosello

E-Mail Website
Guest Editor
Department of Civil and Mechanical Engineering, Technical University of Denmark, Produktionstorvet Building 427A, 2800 Kgs., Lyngby, Denmark
Interests: micro- and nanoscale polymer manufacturing; micro- and nanometrology; additive manufacturing; surface replication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution from current to future factories is supported by research contributions in many fields. While lean manufacturing techniques represent a main improvement paradigm, the integration of new processes and technologies is a breakthrough for step-change improvements and system evolution. The dominant paradigm of Industry 4.0 is a common framework under development. This Special Issue invites novel and quality research paper contributions in a wide range of technologies and the design/operation of manufacturing systems contributing to this evolution, including but not limited to:   

  • Process integration through collaborative robotics and sensors in production/assembly lines;
  • Flexible manufacturing systems routing/scheduling for minimum resource use;
  • Automated quality assurance, traceability, and in-line metrology;
  • Implemented twin models or emulation in manufacturing systems of application cases.

Manufacturing systems and their integration under sustainability criteria (economic, environmental, and social) fit the scope of this Special Issue better than individual isolated processes and technologies, while pure information technology applications or conceptual frameworks in manufacturing are not our focus here. Links to factory job shops, including case studies presenting novel and relevant technical/scientific contributions, are welcome.

Prof. Dr. Roque Calvo
Prof. Dr. José A. Yaguë-Fabra
Prof. Dr. Guido Tosello
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

  • digital manufacturing
  • sustainable manufacturing
  • manufacturing automation
  • quality control
  • digital twin
  • flexible manufacturing systems
  • Industry 4.0
  • lean manufacturing
  • collaborative robots in assembly
  • agile manufacturing systems
  • mass customization
  • high-speed product development
  • product life cycle engineering
  • in-line process control
  • on-machine metrology
  • training systems for workers in digitalized factories

Published Papers (17 papers)

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Editorial

Jump to: Research, Review

3 pages, 200 KiB  
Editorial
Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems
by Roque Calvo, José A. Yagüe-Fabra and Guido Tosello
Appl. Sci. 2023, 13(9), 5570; https://doi.org/10.3390/app13095570 - 30 Apr 2023
Viewed by 996
Abstract
The evolution from current to future factories is supported by research contributions in many fields of technology [...] Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)

Research

Jump to: Editorial, Review

17 pages, 2019 KiB  
Article
Facility Layout Problem with Alternative Facility Variants
by Jiří Kubalík, Lukáš Kurilla and Petr Kadera
Appl. Sci. 2023, 13(8), 5032; https://doi.org/10.3390/app13085032 - 17 Apr 2023
Cited by 2 | Viewed by 1563
Abstract
The facility layout problem is one of the fundamental production system management problems. It has a significant impact on overall system efficiency. This paper introduces a new facility layout problem that allows for choosing from multiple variants of each facility. The need for [...] Read more.
The facility layout problem is one of the fundamental production system management problems. It has a significant impact on overall system efficiency. This paper introduces a new facility layout problem that allows for choosing from multiple variants of each facility. The need for choosing the most suitable selection from the facility variants while at the same time optimizing other layout quality indicators represents a new optimization challenge. We build on our previous work where single- and multi-objective evolutionary algorithms using indirect representation were proposed to solve the facility layout problem. Here, the evolutionary algorithms are adapted for the problem of facility variants, including the new solution representation and variation operators. Additionally, a cooling schedule, whose role is to control the exploration/exploitation ratio during the course of the optimization process, is proposed. It was inspired by the cooling schedule used in the simulated annealing technique. The extended evolutionary algorithms have been experimentally evaluated on two data sets, with and without the alternative variants of facilities. The obtained results demonstrate the capability of the extended evolutionary algorithms to solve the newly formulated facility layout problem efficiently. It also shows that the cooling schedule improves the convergence of the algorithms. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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21 pages, 6883 KiB  
Article
Application of Machine Learning for Prediction and Process Optimization—Case Study of Blush Defect in Plastic Injection Molding
by Alireza Mollaei Ardestani, Ghasem Azamirad, Yasin Shokrollahi, Matteo Calaon, Jesper Henri Hattel, Murat Kulahci, Roya Soltani and Guido Tosello
Appl. Sci. 2023, 13(4), 2617; https://doi.org/10.3390/app13042617 - 17 Feb 2023
Cited by 6 | Viewed by 2288
Abstract
Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters [...] Read more.
Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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29 pages, 20451 KiB  
Article
Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
by Primož Bencak, Darko Hercog and Tone Lerher
Appl. Sci. 2023, 13(4), 2599; https://doi.org/10.3390/app13042599 - 17 Feb 2023
Cited by 5 | Viewed by 2682
Abstract
Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, [...] Read more.
Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, weight, and surface require complex solutions for the objects to be reliably picked. The challenging part of robotic bin-picking is to determine object pick-points correctly. This paper presents a simulation model based on ADAMS/MATLAB cosimulation for robotic pick-point evaluation for a 2-F robotic gripper. It consists of a mechanical model constructed in ADAMS/View, MATLAB/Simulink force controller, several support functions, and the graphical user interface developed in MATLAB/App Designer. Its functionality can serve three different applications, such as: (1) determining the optimal pick-points of the object due to object complexity, (2) selecting the most appropriate robotic gripper, and (3) improving the existing configuration of the robotic gripper (finger width, depth, shape, stroke width, etc.). Additionally, based on this analysis, new variants of robotic grippers can be proposed. The simulation model has been verified on a selected object on a sample 2-F parallel robotic gripper, showing promising results, where up to 75% of pick-points were correctly determined in the initial testing phase. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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28 pages, 3022 KiB  
Article
Study and Simulation of an Under-Actuated Smart Surface for Material Flow Handling
by Edoardo Bianchi, Oliver Jonas Jorg, Gualtiero Fantoni, Francisco Javier Brosed Dueso and José A. Yagüe-Fabra
Appl. Sci. 2023, 13(3), 1937; https://doi.org/10.3390/app13031937 - 02 Feb 2023
Cited by 2 | Viewed by 1937
Abstract
Smart surfaces are becoming more and more popular in the field of intralogistics, as they combine great flexibility with easy reprogrammability. Pursuing this trend, the following article proposes a modular surface to perform handling tasks, such as sorting, stopping, or slowing down material [...] Read more.
Smart surfaces are becoming more and more popular in the field of intralogistics, as they combine great flexibility with easy reprogrammability. Pursuing this trend, the following article proposes a modular surface to perform handling tasks, such as sorting, stopping, or slowing down material flows. Differently from the current technology, the surface used is under-actuated, thus, it exploits the speed, already possessed by the object, or the gravity to perform, with a simplified hardware, for the aforementioned tasks. In practice, these handling actions are completed using an array of rotors, of which only the direction of the rotation axis is controlled. Moreover, the axis can only assume certain discrete orientations in the plane, further simplifying the design. Thus, what is created is a controllable and under-actuated friction field, which, in contrast with similar existing systems, does not require active driving forces to manipulate the material flow. In the article, the analytic model of the surface is described, and a software simulation environment is introduced to demonstrate its functioning. In addition, examples of sorting, slowing down, and stopping operations and a validation of the simulation itself are presented. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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32 pages, 2221 KiB  
Article
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
by Tingting Chen, Vignesh Sampath, Marvin Carl May, Shuo Shan, Oliver Jonas Jorg, Juan José Aguilar Martín, Florian Stamer, Gualtiero Fantoni, Guido Tosello and Matteo Calaon
Appl. Sci. 2023, 13(3), 1903; https://doi.org/10.3390/app13031903 - 01 Feb 2023
Cited by 14 | Viewed by 7848
Abstract
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks [...] Read more.
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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19 pages, 2444 KiB  
Article
Performance Analysis of a Repairable Production Line Using a Hybrid Dependability Queueing Model Based on Monte Carlo Simulation
by Ferdinando Chiacchio, Ludovica Oliveri, Soheyl Moheb Khodayee and Diego D’Urso
Appl. Sci. 2023, 13(1), 271; https://doi.org/10.3390/app13010271 - 26 Dec 2022
Cited by 4 | Viewed by 1368
Abstract
Due to the augmented complexity of the factory on the one hand and the increased availability of information on the other hand, nowadays it is possible to design models of production lines able to consider the state of the health of the production [...] Read more.
Due to the augmented complexity of the factory on the one hand and the increased availability of information on the other hand, nowadays it is possible to design models of production lines able to consider the state of the health of the production system. Such models must combine both the deterministic and the stochastic behaviours of a system, with the former accounting for the mechanics and physics of the industrial process and the latter for randomness, including reliability of the production systems and the unpredictability of the maintenance and of the manufacturing lines. This study proposes the application of a Hybrid Dependability Modelling based on Monte Carlo simulation to estimate the performances of a repairable production line modelled with a queueing G/G/1 system. The model proposed is characterized by random interarrival and service times and by the wearing and dynamic aging phenomena of the machine tools that depend on the working and operating conditions. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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14 pages, 4151 KiB  
Article
Value Chain Comparison of Additively and Conventionally Manufactured Multi-Cavity Tool Steel Inserts: An Injection Molding Industrial Case Study for High-Volume Production
by Mandaná Moshiri, Mohsin Raza, Mohamed Sahlab, Ali Ahmad Malik, Arne Bilberg and Guido Tosello
Appl. Sci. 2022, 12(20), 10410; https://doi.org/10.3390/app122010410 - 15 Oct 2022
Cited by 3 | Viewed by 2245
Abstract
The development of injection molding tools is an expensive, time-consuming, and resource-intensive process offering little to no flexibility to adapt to variations in product design. Metal additive manufacturing can be used to produce these tools in a cost-effective way. Nevertheless, in an industrial [...] Read more.
The development of injection molding tools is an expensive, time-consuming, and resource-intensive process offering little to no flexibility to adapt to variations in product design. Metal additive manufacturing can be used to produce these tools in a cost-effective way. Nevertheless, in an industrial context, effective methods are missing for the selection of the most suitable technology for the given tooling project. This paper presents a method to compare process chains based on additive and conventional subtractive technologies for the manufacturing of metal tooling for injection molding. The comparison is based on a technology focused-performance analysis (TFPA) through computer simulation performed using Tecnomatix Plant Simulation developed by Siemens Digital Industries Software combined with a customized cost–benefit economic analysis tool. The analysis of the technology comparison highlights potential bottlenecks for production, such as the printing phase and the heat treatment. It also gives a deeper understanding of the technology maturity level of conventional milling machines against laser powder bed fusion machines. The result is that the total costs for an insert made by AM and CM are indeed rather similar (the cost difference between the two tooling process chains is lower than 5%). The cost analysis reveals major costs drivers in the production of high-performance molding tools, such as the cutting tools employed for the milling steps and their changeover frequency. The industrial case of a 32-cavity mold insert for plastic injection molding is used to perform the study, develop the analysis, and validate the results. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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22 pages, 15237 KiB  
Article
Low-Cost Digitalization Solution through Scalable IIoT Prototypes
by Marko Vuković, Oliver Jorg, Mohammadamin Hosseinifard and Gualtiero Fantoni
Appl. Sci. 2022, 12(17), 8571; https://doi.org/10.3390/app12178571 - 27 Aug 2022
Cited by 10 | Viewed by 2095
Abstract
Industry 4.0 is fast becoming a mainstream goal, and many companies are lining up to join the Fourth Industrial Revolution. Small and medium-sized enterprises, especially in the manufacturing industry, are the most heavily challenged in adopting new technology. One of the reasons why [...] Read more.
Industry 4.0 is fast becoming a mainstream goal, and many companies are lining up to join the Fourth Industrial Revolution. Small and medium-sized enterprises, especially in the manufacturing industry, are the most heavily challenged in adopting new technology. One of the reasons why these enterprises are lagging behind is the motivation of the key personnel, the decision-makers. The factories in question often do not have a pressing need for advancing to Industry 4.0 and are wary of the risk in doing so. The authors present a rapid, low-cost prototyping solution for the manufacturing companies with legacy machinery intending to adopt the Industry 4.0 paradigm with a low-risk initial step. The legacy machines are retrofitted through the Industrial Internet of Things, making these machines both connectable and capable of providing data, thus enabling process monitoring. The machine chosen as the digitization target was not connectable, and the retrofit was extensive. The choice was made to present the benefits of digitization to the stakeholders quickly and effectively. Indeed, the solution provides immediate results within manufacturing industrial settings, with the ultimate goal being the digital transformation of the entire factory. This work presents an implementation cycle for digitizing an industrial broaching machine, supported by state-of-the-art literature analysis. The methodology utilized in this work is based on the well-known DMAIC strategy customized for the specifics of this case study. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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22 pages, 13622 KiB  
Article
Design and Implementation of OPC UA-Based VR/AR Collaboration Model Using CPS Server for VR Engineering Process
by Jeehyeong Kim and Jongpil Jeong
Appl. Sci. 2022, 12(15), 7534; https://doi.org/10.3390/app12157534 - 27 Jul 2022
Cited by 3 | Viewed by 2461
Abstract
In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is [...] Read more.
In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is highly growthable. In particular, the core technologies that enable digital transformation VR (Virtual Reality)/AR (Augmented Reality) technologies have developed rapidly in recent years, but have not yet achieved any particular results in industrial engineering. In the manufacturing industry, digital threads and collaboration systems are needed to reduce design costs that change over and over again due to the inability to respond to various problems and demands that should be considered when designing products. To this end, we propose a VR/AR collaboration model that increases efficiency of manufacturing environments such as inspection and maintenance as well as design simultaneously with participants through 3D rendering virtualization of facilities or robot 3D designs in VR/AR. We implemented converting programs and middleware CPS (Cyber Physical System) servers that convert to BOM (Bill of Material)-based 3D graphics models and CPS models to test the accuracy of data and optimization of 3D modeling and study their performance through robotic arms in real factories. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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19 pages, 5981 KiB  
Article
Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants
by Néstor Rodríguez-Padial, Marta M. Marín and Rosario Domingo
Appl. Sci. 2022, 12(14), 7144; https://doi.org/10.3390/app12147144 - 15 Jul 2022
Cited by 4 | Viewed by 1425
Abstract
Current production systems that respond to market demands with high rates of production change and customization use complex systems. These systems are machines with a high capacity for communication, sensing and self-diagnosis, although they are susceptible to failures, breakdowns and a loss of [...] Read more.
Current production systems that respond to market demands with high rates of production change and customization use complex systems. These systems are machines with a high capacity for communication, sensing and self-diagnosis, although they are susceptible to failures, breakdowns and a loss of reliability. The amount of data they provide as a productive system and, individually, as a machine can be treated to improve customized maintenance plans. The objective of this work, with an operational scope, is to collect and exploit the knowledge acquired in the industrial plant on failures and breakdowns based on its historical data. The acquisition of the aforementioned data is channeled through the human intellectual capital of the work groups formed for this purpose. Once this knowledge is acquired and available in a worksheet format according to the Reliability-Centered Maintenance (RCM) methodology, it is implemented using Case-Based Reasoning algorithms in a Java application developed for this purpose to carry out the process of RCM, accessing a base of similar cases that can be adapted. This operational definition allows for the control of the maintenance function of an industrial plant in the short term, with a weekly horizon, to design a maintenance plan adjusted to the reality of the plant in its current operating context, which may differ greatly from the originally projected plan or from any other plan caused by new production requirements. This new plan designed as such will apply changes to the equipment, which make up the production system, as a consequence of the adaptation to the changing market demand. As a result, a computer application has been designed, implemented and validated that allows, through the incorporation of RCM cases already successfully carried out on the productive system of the plant, for the development of a customized maintenance plan through an assistant, which, in a conductive way, guides the plant maintenance engineer through their design process, minimizing human error and design time and leveraging existing intellectual capital. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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15 pages, 1209 KiB  
Article
A Robust Scheduling Framework for Re-Manufacturing Activities of Turbine Blades
by Lei Liu and Marcello Urgo
Appl. Sci. 2022, 12(6), 3034; https://doi.org/10.3390/app12063034 - 16 Mar 2022
Cited by 4 | Viewed by 1664
Abstract
Refurbished products are gaining importance in many industrial sectors, specifically high-value products whose residual value is relevant and guarantee the economic viability of the re-manufacturing at an industrial level, e.g., turbine blades for power generation. In this paper, we address the robust scheduling [...] Read more.
Refurbished products are gaining importance in many industrial sectors, specifically high-value products whose residual value is relevant and guarantee the economic viability of the re-manufacturing at an industrial level, e.g., turbine blades for power generation. In this paper, we address the robust scheduling scheme of re-manufacturing activities for turbine blades. Parts entering the process may have very different wear states or presence of defects. Thus, the repair process is affected by a significant degree of uncertainty. The paper investigates the uncertainties and discusses how they affect the scheduling performance of the re-manufacturing system. We then present a robust scheduling framework for the re-manufacturing scheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches in the re-manufacturing scheduling area, which will be a guideline for the planning and scheduling of re-manufacturing activities of turbine blades. A case study approach was adopted to examine how re-manufacturers design their scheduling strategies. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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22 pages, 3117 KiB  
Article
Ontology-Based Production Simulation with OntologySim
by Marvin Carl May, Lars Kiefer, Andreas Kuhnle and Gisela Lanza
Appl. Sci. 2022, 12(3), 1608; https://doi.org/10.3390/app12031608 - 03 Feb 2022
Cited by 12 | Viewed by 4255
Abstract
Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully [...] Read more.
Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve current challenges in modeling and simulation in production science. Due to the individualization and customization of products and the resulting increase in complexity of production, a need for flexibly adaptable simulations arises. This need is exemplified in the trend towards Digital Twins and Digital Shadows. Their application to production systems, against the background of an ever increasing speed of change in such systems, is arduous. Moreover, missing understandability and human interpretability of current approaches hinders successful, goal oriented applications. The OntologySim can help solving this challenge by providing the ability to generate truly cyber physical systems, both interlocked with reality and providing a simulation framework. In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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15 pages, 5303 KiB  
Article
RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analogic Indicators
by Mohammadamin Hosseinifard, Salam Alzubaidi, Andrea Michel and Gualtiero Fantoni
Appl. Sci. 2022, 12(3), 1451; https://doi.org/10.3390/app12031451 - 29 Jan 2022
Cited by 2 | Viewed by 2668
Abstract
Simple analog devices like manometers, manual valves, etc., have been ignored in the digitization process that has characterized the transition towards Industry 4.0. The reason behind this is that their substitution with the equivalent digital versions is high cost and needs re-wiring. This [...] Read more.
Simple analog devices like manometers, manual valves, etc., have been ignored in the digitization process that has characterized the transition towards Industry 4.0. The reason behind this is that their substitution with the equivalent digital versions is high cost and needs re-wiring. This study introduces a low-cost wireless and passive model aligned with the Industry 4.0 paradigm to digitize analog indicators. The concept is based on electromagnetic (EM) shielding of the manometer’s embedded radio frequency identification (RFID) tag. We designed and tuned a new tiny RFID tag to be embedded into analog devices. Finally, a digitized manometer by RFID electromagnetic shielding concept is simulated in the Ansys HFSS modeling environment. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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29 pages, 2210 KiB  
Article
PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
by Paulo Peças, João Encarnação, Manuel Gambôa, Manuel Sampayo and Diogo Jorge
Appl. Sci. 2021, 11(16), 7671; https://doi.org/10.3390/app11167671 - 20 Aug 2021
Cited by 16 | Viewed by 6956
Abstract
Continuous improvement (CI) is a key component of lean manufacturing (LM), which is fundamental for organizations to remain competitive in an ever more challenging market. At present, the new industrial revolution, Industry 4.0 (I4.0), is taking place in the manufacturing and service markets, [...] Read more.
Continuous improvement (CI) is a key component of lean manufacturing (LM), which is fundamental for organizations to remain competitive in an ever more challenging market. At present, the new industrial revolution, Industry 4.0 (I4.0), is taking place in the manufacturing and service markets, allowing more intelligent and automated processes to become a reality through innovative technologies. Not much research was found regarding a holistic application of I4.0′s technological concepts towards CI, which clarifies the potential for improving its effectiveness. This clearly indicates that research is needed regarding this subject. The present publication intends to close this research gap by studying the main I4.0 technological concepts and their possible application towards a typical CI process, establishing the requirements for such an approach. Based on that study, a conceptual approach is proposed (PDCA 4.0), depicting how I4.0 technological concepts should be used for CI enhancement, while aiming to satisfy the identified requirements. By outlining the PDCA 4.0 approach, this paper contributes to increasing the knowledge available regarding the CI realm on how to support the CI shift towards a I4.0 industrial paradigm. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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Review

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51 pages, 6605 KiB  
Review
Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review
by Phuong Thao Ho, José Antonio Albajez, Jorge Santolaria and José A. Yagüe-Fabra
Appl. Sci. 2022, 12(4), 1961; https://doi.org/10.3390/app12041961 - 13 Feb 2022
Cited by 27 | Viewed by 9791
Abstract
Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require [...] Read more.
Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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Review
From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
by Francisco Gil-Vilda, José A. Yagüe-Fabra and Albert Sunyer
Appl. Sci. 2021, 11(21), 10318; https://doi.org/10.3390/app112110318 - 03 Nov 2021
Cited by 16 | Viewed by 7965
Abstract
Over recent decades, the increasing competitiveness of markets has propagated the term “lean” to describe the management concept for improving productivity, quality, and lead time in industrial as well as services operations. Its overuse and linkage to different specifiers (surnames) have created confusion [...] Read more.
Over recent decades, the increasing competitiveness of markets has propagated the term “lean” to describe the management concept for improving productivity, quality, and lead time in industrial as well as services operations. Its overuse and linkage to different specifiers (surnames) have created confusion and misunderstanding as the term approximates pragmatic ambiguity. Through a systematic literature review, this study takes a historical perspective to analyze 4962 papers and 20 seminal books in order to clarify the origin, evolution, and diversification of the lean concept. Our main contribution lies in identifying 17 specifiers for the term “lean” and proposing four mechanisms to explain this diversification. Our research results are useful to both academics and practitioners to return to the Lean origins in order to create new research areas and conduct organizational transformations based on solid concepts. We conclude that the use of “lean” as a systemic thinking is likely to be further extended to new research fields. Full article
(This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems)
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