Smart Production in Terms of Industry 4.0 and 5.0

Topic Information

Dear Colleagues,

This Topic is dedicated to the fourth and fifth industrial revolutions and aims to promote new intelligent technologies, artificial intelligence and machine learning methods in the fields of materials, manufacturing, enterprise/factory and even maintenance to make them smart. New mechatronic concepts of Cyber Physical Systems (CPSs) connected in the dimension of the Internet of Things (IoT) for production lines, machines, operators, robots, tools and other equipment are promoted. One of the dimensions of the fourth industrial revolution is the advanced combination of virtual and real tools and tests to improve the efficiency of modern engineering science. Standards for integration, communication and interaction between CPSs of robots, humans and machines are strongly needed to bridge the gap between theory and practice. Network connections between CPSs and physical systems are characterized by a new level of awareness of their process capabilities, including various objectives such as production lead times, production on time, customized products, higher production control, increased product quality, equipment reliability and availability, and more. Papers presenting new manufacturing paradigms, including smart connections, data-to-information conversion, cyber, cognition and configuration levels are welcome. Other aspects of Industry 4.0 are Big Data processing, cloud computing, blockchain technology, etc. Models of sensor data integration with CPS should be developed, with the need to periodically query the controlled sensor network and process the received data in real time. Moreover, analysis based on historical data must be synchronized with real-time conditions of machines, robots, devices and workers to enhance the capabilities of the physical and virtual world. For each domain of a manufacturing process, models of edge computing and knowledge acquisition are needed at a specific moment and for a censored time interval.

For this Topic, we invite you to submit original papers and reviews of the struggles of scientists and practitioners in the fields of Industry 4.0 and 5.0. We encourage you to publish studies containing the results of conceptual work and laboratory and real-object tests to present issues including the use of smart connections, data-to-information conversion, cyber, cognition and configuration levels and more.

Dr. Iwona Paprocka
Dr. Cezary Grabowik
Dr. Jozef Husar
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
IoT IoT	-	8.5	2020	27.8 Days	CHF 1200	Submit
Materials materials	3.1	5.8	2008	13.9 Days	CHF 2600	Submit
Robotics robotics	2.9	6.7	2012	21 Days	CHF 1800	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Machines machines	2.1	3.0	2013	15.5 Days	CHF 2400	Submit

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Published Papers (11 papers)

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13 pages, 3402 KiB  

Open AccessArticle

From Simulation to Validation in Ensuring Quality and Reliability in Model-Based Predictive Analysis

by Stella Hrehova, Katarzyna Antosz, Jozef Husár and Alena Vagaska

Appl. Sci. 2025, 15(6), 3107; https://doi.org/10.3390/app15063107 - 13 Mar 2025

Viewed by 276

Abstract

The increasing complexity of artificial intelligence and machine learning models has raised concerns about balancing model accuracy and interpretability. While advanced software tools facilitate model design, they also introduce challenges in selecting models that offer both high quality and manageable complexity. Validation techniques [...] Read more.

The increasing complexity of artificial intelligence and machine learning models has raised concerns about balancing model accuracy and interpretability. While advanced software tools facilitate model design, they also introduce challenges in selecting models that offer both high quality and manageable complexity. Validation techniques such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Akaike Information Criterion (AIC) enable quantitative assessment, but empirical studies indicate that higher complexity does not always improve predictive performance. This study proposes an approach to evaluate model complexity versus accuracy in predicting the absorption properties of composite materials with varying textile fibre content (10%, 20%, 30%, 40%). Using MATLAB’s Curve Fitting Toolbox, we assessed polynomial, Fourier, and Gaussian regression models. The Gaussian regression model with six parameters (Gauss6) achieved the best balance between complexity and accuracy (R² = 0.9429; RMSE = 0.013537; MAE = 0.004885). Increasing parameters beyond six showed diminishing returns, as confirmed by AIC (−2806.93 for Gauss6 vs. −2847.17 for Gauss7). These findings emphasise that higher model complexity does not necessarily enhance quality, highlighting the importance of structured model validation. This study provides insights for optimising predictive modelling in material science and other domains. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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16 pages, 8593 KiB  

Open AccessArticle

Smart Machine Vision System to Improve Decision-Making on the Assembly Line

by Carlos Americo de Souza Silva and Edson Pacheco Paladini

Machines 2025, 13(2), 98; https://doi.org/10.3390/machines13020098 - 27 Jan 2025

Cited by 1 | Viewed by 745

Abstract

Technological advances in the production of printed circuit boards (PCBs) are increasing the number of components inserted on the surface. This has led the electronics industry to seek improvements in their inspection processes, often making it necessary to increase the level of automation [...] Read more.

Technological advances in the production of printed circuit boards (PCBs) are increasing the number of components inserted on the surface. This has led the electronics industry to seek improvements in their inspection processes, often making it necessary to increase the level of automation on the production line. The use of machine vision for quality inspection within manufacturing processes has increasingly supported decision making in the approval or rejection of products outside of the established quality standards. This study proposes a hybrid smart-vision inspection system with a machine vision concept and vision sensor equipment to verify 24 components and eight screw threads. The goal of this study is to increase automated inspection reliability and reduce non-conformity rates in the manufacturing process on the assembly line of automotive products using machine vision. The system uses a camera to collect real-time images of the assembly fixtures, which are connected to a CMOS color vision sensor. The method is highly accurate in complex industry environments and exhibits specific feasibility and effectiveness. The results indicate high performance in the failure mode defined during this study, obtaining the best inspection performance through a strategy using Vision Builder for automated inspection. This approach reduced the action priority by improving the failure mode and effect analysis (FMEA) method. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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16 pages, 4076 KiB  

Open AccessArticle

An Efficient Lane Detection Network with Channel-Enhanced Coordinate Attention

by Ke Xu, Zhicheng Hao, Ming Zhu and Jiarong Wang

Machines 2024, 12(12), 870; https://doi.org/10.3390/machines12120870 - 29 Nov 2024

Viewed by 693

Abstract

Lane detection based on semantic segmentation can achieve high accuracy, but, in recent years, it does not have a mobile-friendly cost, which is caused by the complex iteration and costly convolutions in convolutional neural networks (CNNs) and state-of-the-art (SOTA) models based on CNNs, [...] Read more.

Lane detection based on semantic segmentation can achieve high accuracy, but, in recent years, it does not have a mobile-friendly cost, which is caused by the complex iteration and costly convolutions in convolutional neural networks (CNNs) and state-of-the-art (SOTA) models based on CNNs, such as spatial CNNs (SCNNs). Although the SCNN has shown its capacity to capture the spatial relationships of pixels across rows and columns of an image, the computational cost and memory requirement needed cannot be afforded with mobile lane detection. Inspired by the channel attention and self-attention machine, we propose an integrated coordinate attention (ICA) module to capture the spatial-wise relationships of pixels. Furthermore, due to the lack of enhancement in the channel dimension, we created an efficient network with a channel-enhanced coordinate attention block named CCA, composed of ICA and other channel attention modules, for all-dimension feature enhancement. As a result, by replacing many repeated or iterative convolutions with the attention mechanism, CCA reduces the computational complexity. Thus, our method achieves a balance of accuracy and speed and has better performance on two lane datasets—TuSimple and ILane. At less than a few tenths of the computational cost, our CCA achieves superior accuracy compared to the SCNN. These results show that the low cost and great performance of our design enable the use of the lane detection task in autopilot scenarios. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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27 pages, 2985 KiB  

Open AccessArticle

Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing

by Katarzyna Antosz, Lucia Knapčíková and Jozef Husár

Appl. Sci. 2024, 14(22), 10450; https://doi.org/10.3390/app142210450 - 13 Nov 2024

Cited by 2 | Viewed by 1034

Abstract

This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the [...] Read more.

This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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25 pages, 6925 KiB  

Open AccessArticle

Investigation of Effect of Part-Build Directions and Build Orientations on Tension–Tension Mode Fatigue Behavior of Acrylonitrile Butadiene Styrene Material Printed Using Fused Filament Fabrication Technology

by Ibrahim S. El-Deeb, Cezary Grabowik, Ehssan Esmael, Ahmed Nabhan, Maher Rashad and Saad Ebied

Materials 2024, 17(20), 5133; https://doi.org/10.3390/ma17205133 - 21 Oct 2024

Cited by 1 | Viewed by 1025

Abstract

This article explores the fatigue characteristics of acrylonitrile butadiene styrene (ABS) components fabricated using fused filament fabrication (FFF) additive manufacturing technology. ABS is frequently used as a polymeric thermoplastic material in open-source FFF machines for a variety of engineering applications. However, a comprehensive [...] Read more.

This article explores the fatigue characteristics of acrylonitrile butadiene styrene (ABS) components fabricated using fused filament fabrication (FFF) additive manufacturing technology. ABS is frequently used as a polymeric thermoplastic material in open-source FFF machines for a variety of engineering applications. However, a comprehensive understanding of the mechanical properties and execution of FFF-processed ABS components is necessary. Currently, there is limited knowledge regarding the fatigue behavior of ABS components manufactured using FFF AM technology. The primary target of this study is to evaluate the results of part-build directions and build orientation angles on the tensile fatigue behavior exhibited by ABS material. To obtain this target, an empirical investigation was carried out to assess the influence of building angles and orientation on the fatigue characteristics of ABS components produced using FFF. The test samples were printed in three distinct directions, including Upright, On Edge, and Flat, and with varying orientation angles ([0°, 90°], [15°, 75°], [30°, 60°], [45°]), using a 50% filling density. The empirical data suggest that, at each printing angle, the On-Edge building orientation sample exhibited the most prolonged vibrational duration before fracturing. In this investigation, we found that the On-Edge printing direction significantly outperformed the other orientations in fatigue life under cyclic loading with 1592 loading cycles when printed with an orientation angle of 15°–75°. The number of loading cycles was 290 and 39 when printed with the same orientation angle for the Flat and Upright printing directions, respectively. This result underscores the importance of orientation in the mechanical performance of FFF-manufactured ABS materials. These findings enhance our comprehension of the influence exerted by building orientation and building angles on the fatigue properties of FFF-produced test samples. Moreover, the research outcomes supply informative perspectives on the selection of building direction and building orientation angles for the design of 3D-printed thermoplastic components intended for fatigue cyclic-loading applications. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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24 pages, 11469 KiB  

Open AccessArticle

A Multidisciplinary Learning Model Using AGV and AMR for Industry 4.0/5.0 Laboratory Courses: A Study

by Ákos Cservenák and Jozef Husár

Appl. Sci. 2024, 14(17), 7965; https://doi.org/10.3390/app14177965 - 6 Sep 2024

Viewed by 1564

Abstract

This paper presents the development of a multidisciplinary learning model using automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) for laboratory courses, focusing on Industry 4.0 and 5.0 paradigms. Industry 4.0 and 5.0 emphasize advanced industrial automation and human–robot collaboration, which requires [...] Read more.

This paper presents the development of a multidisciplinary learning model using automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) for laboratory courses, focusing on Industry 4.0 and 5.0 paradigms. Industry 4.0 and 5.0 emphasize advanced industrial automation and human–robot collaboration, which requires innovative educational strategies. Motivated by the need to align educational practices with these industry trends, the goal of this research is to design and implement an effective educational model integrating AGV and AMR. The methodology section details the complex development process, including technology selection, curriculum design, and laboratory exercise design. Data collection and analysis were conducted to assess the effectiveness of the model. The design phase outlines the structure of the educational model, integrating AGV and AMR into the laboratory modules and enriching them with industry collaboration and practical case studies. The results of a pilot implementation are presented, showing the impact of the model on students’ learning outcomes compared to traditional strategies. The evaluation reveals significant improvements in student engagement and understanding of industrial automation. The implications of these findings are discussed, challenges and potential improvements identified, and alignment with current educational trends discussed. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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22 pages, 8779 KiB  

Open AccessArticle

Reinforcement-Learning-Based Path Planning: A Reward Function Strategy

by Ramón Jaramillo-Martínez, Ernesto Chavero-Navarrete and Teodoro Ibarra-Pérez

Appl. Sci. 2024, 14(17), 7654; https://doi.org/10.3390/app14177654 - 29 Aug 2024

Cited by 3 | Viewed by 3808

Abstract

Path planning is a fundamental task for autonomous mobile robots (AMRs). Classic approaches provide an analytical solution by searching for the trajectory with the shortest distance; however, reinforcement learning (RL) techniques have been proven to be effective in solving these problems with the [...] Read more.

Path planning is a fundamental task for autonomous mobile robots (AMRs). Classic approaches provide an analytical solution by searching for the trajectory with the shortest distance; however, reinforcement learning (RL) techniques have been proven to be effective in solving these problems with the experiences gained by agents in real time. This study proposes a reward function that motivates an agent to select the shortest path with fewer turns. The solution to the RL technique is obtained via dynamic programming and Deep Q-Learning methods. In addition, a path-tracking control design is proposed based on the Lyapunov candidate function. The results indicate that RL algorithms show superior performance compared to classic A* algorithms. The number of turns is reduced by 50%, resulting in a decrease in the total distance ranging from 3.2% to 36%. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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28 pages, 16108 KiB  

Open AccessArticle

GC Snakes: An Efficient and Robust Segmentation Model for Hot Forging Images

by Xiaoyu Pan and Delun Wang

Sensors 2024, 24(15), 4821; https://doi.org/10.3390/s24154821 - 25 Jul 2024

Viewed by 791

Abstract

Machine vision is a desirable non-contact measurement method for hot forgings, as image segmentation has been a challenging issue in performance and robustness resulting from the diversity of working conditions for hot forgings. Thus, this paper proposes an efficient and robust active contour [...] Read more.

Machine vision is a desirable non-contact measurement method for hot forgings, as image segmentation has been a challenging issue in performance and robustness resulting from the diversity of working conditions for hot forgings. Thus, this paper proposes an efficient and robust active contour model and corresponding image segmentation approach for forging images, by which verification experiments are conducted to prove the performance of the segmentation method by measuring geometric parameters for forging parts. Specifically, three types of continuity parameters are defined based on the geometric continuity of equivalent grayscale surfaces for forging images; hence, a new image force and external energy functional are proposed to form a new active contour model, Geometric Continuity Snakes (GC Snakes), which is more percipient to the grayscale distribution characteristics of forging images to improve the convergence for active contour robustly; additionally, a generating strategy for initial control points for GC Snakes is proposed to compose an efficient and robust image segmentation approach. The experimental results show that the proposed GC Snakes has better segmentation performance compared with existing active contour models for forging images of different temperatures and sizes, which provides better performance and efficiency in geometric parameter measurement for hot forgings. The maximum positioning and dimension errors by GC Snakes are 0.5525 mm and 0.3868 mm, respectively, compared with errors of 0.7873 mm and 0.6868 mm by the Snakes model. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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23 pages, 9408 KiB  

Open AccessArticle

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

by Jing You, Zhiyuan Wu, Wei Wei, Ning Li and Yuhua Yang

Appl. Sci. 2024, 14(14), 6369; https://doi.org/10.3390/app14146369 - 22 Jul 2024

Cited by 2 | Viewed by 2126

Abstract

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the [...] Read more.

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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24 pages, 2248 KiB  

Open AccessArticle

Deep Reinforcement Learning and Discrete Simulation-Based Digital Twin for Cyber–Physical Production Systems

by Damian Krenczyk

Appl. Sci. 2024, 14(12), 5208; https://doi.org/10.3390/app14125208 - 14 Jun 2024

Cited by 3 | Viewed by 1608

Abstract

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. [...] Read more.

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the “cyber” layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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21 pages, 12038 KiB  

Open AccessTechnical Note

Image-to-Image Translation-Based Deep Learning Application for Object Identification in Industrial Robot Systems

by Timotei István Erdei, Tibor Péter Kapusi, András Hajdu and Géza Husi

Robotics 2024, 13(6), 88; https://doi.org/10.3390/robotics13060088 - 2 Jun 2024

Cited by 2 | Viewed by 2119

Abstract

Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition [...] Read more.

Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

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