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Applied Sciences
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Applications of Machine Learning and Computer Vision in Industry 4.0
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Applications of Machine Learning and Computer Vision in Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 15992

Special Issue Editors

Dr. Oto Haffner

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, 812 19 Bratislava, Slovakia
Interests: computer vision; machine vision; camera systems; discrete simulation; production system modeling and simulation; convolution neural networks; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Erik Kučera

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, 812 43 Bratislava, Slovakia
Interests: virtual and augmented reality; Internet of Things; cloud computing; computational intelligence; Petri Nets
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danica Rosinová

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia
Interests: robust control; optimization; LMI; decentralized control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0, also referred to as the fourth industrial revolution, is a complex concept representing the highest degree of current automation, interoperability, data exchange and decentralization in manufacturing systems and technologies. It is defined as an integrating concept for technologies, systems and the concept of value chain organization. It combines Cyber–Physical Systems (CPS), Internet of Service (IoS) and Internet of Things (IoT).

The development and implementation of CPS, IoS and IoT in smart factories requires the development of new methods and approaches. However, the new tasks will also bring new challenges that can only be solved by applying and developing the latest knowledge from a range of engineering disciplines: machine perception systems, intelligent sensing, intelligent human–machine and machine-to-machine communication, computer vision, and machine learning, etc. New insights from these fields will enable modeling and subsequent replacement of human decision-making activities by machine activities.

Artificial intelligence (AI) will play a key role in the new tasks and challenges in the context of Industry 4.0 and smart factories. This discipline offers solutions that will be as efficient as possible, provide flexibility, be able to fully exploit shared data storage (cloud solutions) and fully exploit the concept of the Internet of Things.

As a result of digitalization and the advent of the Industry 4.0 concept, it is undergoing a significant transformation and AI is one of the tools in bringing about this change. AI has evolved in its capabilities over the years and has found application in various areas of industrial manufacturing and automation. The integration of AI with other advanced technologies in the industrial ecosystem will enable manufacturers to gain strong support in the Industry 4.0 concept.

Computer and machine vision is able to replace humans in monotonous repetitive vision activities because of their ability to see and understand the environment. When considering the sensory perception of objects, it is often visual perception that is aimed at, as about 90–95% of the information is drawn through this sensory channel. The application is mainly in the field of quality control, where the human factor very often fails or is not able to meet the requirements for productivity or accuracy of the checks performed. Machine vision is an essential element of automation and smart systems. No other aspect of the production line captures more information or is more valuable in assessing products and detecting defects, as well as gathering data to guide operations and optimize the productivity of robots and other equipment. Unlike simple sensors, vision sensors generate large amounts of image data, increasing their usefulness in Industry 4.0 environments.

Deep learning-based image analysis combines the specificity and flexibility of human visual inspection with the reliability, consistency and speed of a computer system. Deep learning models can repeatedly and iteratively solve challenging computer vision applications that would be difficult to develop programmatically and that are often impossible to solve using traditional machine vision approaches. Deep learning models can distinguish unacceptable errors while tolerating natural variations in complex patterns. Moreover, they can be easily adapted to new examples without reprogramming their underlying algorithms.

Dr. Oto Haffner
Dr. Erik Kučera
Prof. Dr. Danica Rosinová
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning and computer vision for control, monitoring, inspection and quality control in Industry 4.0 applications
  • big data in digital and smart factories
  • intelligent transportation and communication systems in digital and smart factories
  • virtual, augmented and mixed reality in Industry 4.0 applications and services
  • simulation and optimization in Industry 4.0 applications
  • advanced control methods based on machine learning
  • new human–machine interfaces for mechatronic, robotic and production systems
  • education approaches for machine learning, computer vision and digital twins
  • predictive maintenance based on machine learning and digital twins
  • artificial intelligence in sustainable and human-centric solutions leading to Industry 5.0

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Published Papers (7 papers)

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Editorial

Jump to: Research

10 pages, 242 KiB  
Editorial
Applications of Machine Learning and Computer Vision in Industry 4.0
by Oto Haffner, Erik Kučera and Danica Rosinová
Appl. Sci. 2024, 14(6), 2431; https://doi.org/10.3390/app14062431 - 13 Mar 2024
Cited by 1 | Viewed by 1508
Abstract
Among the most important economic activities of humankind is industry [...] Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)

Research

Jump to: Editorial

21 pages, 6574 KiB  
Article
Recognition of Additive Manufacturing Parts Based on Neural Networks and Synthetic Training Data: A Generalized End-to-End Workflow
by Jonas Conrad, Simon Rodriguez, Daniel Omidvarkarjan, Julian Ferchow and Mirko Meboldt
Appl. Sci. 2023, 13(22), 12316; https://doi.org/10.3390/app132212316 - 14 Nov 2023
Viewed by 1046
Abstract
Additive manufacturing (AM) is becoming increasingly relevant among established manufacturing processes. AM parts must often be recognized to sort them for part- or order-specific post-processing. Typically, the part recognition is performed manually, which represents a bottleneck in the AM process chain. To address [...] Read more.
Additive manufacturing (AM) is becoming increasingly relevant among established manufacturing processes. AM parts must often be recognized to sort them for part- or order-specific post-processing. Typically, the part recognition is performed manually, which represents a bottleneck in the AM process chain. To address this challenge, a generalized end-to-end workflow for automated visual real-time recognition of AM parts is presented, optimized, and evaluated. In the workflow, synthetic training images are generated from digital AM part models via rendering. These images are used to train a neural network for image classification, which can recognize the printed AM parts without design adaptations. As each production batch can consist of new parts, the workflow is generalized to be applicable to individual batches without adaptation. Data generation, network training and image classification are optimized in terms of the hardware requirements and computational resources for industrial applicability at low cost. For this, the influences of the neural network structure, the integration of a physics simulation in the rendering process and the total number of training images per AM part are analyzed. The proposed workflow is evaluated in an industrial case study involving 215 distinct AM part geometries. Part classification accuracies of 99.04% (top three) and 90.37% (top one) are achieved. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)
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15 pages, 1771 KiB  
Article
Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
by Shiqi Yue and Yuanwu Shi
Appl. Sci. 2023, 13(15), 8994; https://doi.org/10.3390/app13158994 - 5 Aug 2023
Viewed by 1102
Abstract
With the rapid development of computer and artificial intelligence technology, robots have been widely used in assembly, sorting, and other work scenarios, gradually changing the human-oriented mechanical assembly line working mode. Traditional robot control methods often rely on application fields and mathematical models, [...] Read more.
With the rapid development of computer and artificial intelligence technology, robots have been widely used in assembly, sorting, and other work scenarios, gradually changing the human-oriented mechanical assembly line working mode. Traditional robot control methods often rely on application fields and mathematical models, and they cannot meet the emerging requirements of versatility and flexibility in many fields, such as intelligent manufacturing and customized production. Therefore, aiming at the relationship between the manipulator’s smooth control command parameters and the manipulator’s actual motion stability in the multi-step object sorting task, this paper proposes a method for predicting the stability of the manipulator based on Long Short-Term Memory Extreme Gradient Boosting. The acquisition signal of the manipulator vibration is segmented according to the action, and the boost model is used to learn the relationship between the control command parameters and the stability characteristic indexes. Next, the Extreme Gradient Boosting algorithm establishes a feature index-stationarity score prediction model. The minimum Mean Absolute Error predicted by the five indicators is 0.0024 so that the model can predict the manipulator’s motion stability level according to the manipulator’s command parameters. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)
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15 pages, 5396 KiB  
Article
Predicting Rutting Development of Pavement with Flexible Overlay Using Artificial Neural Network
by Chunru Cheng, Chen Ye, Hailu Yang and Linbing Wang
Appl. Sci. 2023, 13(12), 7064; https://doi.org/10.3390/app13127064 - 12 Jun 2023
Cited by 1 | Viewed by 1524
Abstract
Pavement maintenance and repair is a crucial part of pavement management systems. Accurate and reliable pavement performance prediction is the prerequisite for making reasonable maintenance decisions and selecting suitable repair schemes. Rutting deformation, as one of the most common forms of asphalt pavement [...] Read more.
Pavement maintenance and repair is a crucial part of pavement management systems. Accurate and reliable pavement performance prediction is the prerequisite for making reasonable maintenance decisions and selecting suitable repair schemes. Rutting deformation, as one of the most common forms of asphalt pavement failures, is a key index for evaluating the pavement performance. To ensure the accuracy of the commonly used prediction models, the input parameters of the models need to be understood, and the coefficients of the models should be locally calibrated. This paper investigates the prediction of the rutting development of pavements with flexible overlays based on the data of the Canadian Long-Term Pavement Performance (C-LTPP) program. Pavement performance data that may be related to rutting were extracted from the survey of Dipstick for data analysis. Then, an artificial neural network (ANN) was adopted to analyze the factors affecting the rut depth, and to establish a model for the rutting development of pavements with flexible overlays. The results of the sensitivity analysis indicate that rutting is not only affected by traffic and climatic conditions, but it is also greatly affected by the thickness of the surface layer and voids in the mixture. Finally, a rutting evaluation index was provided to describe the rutting severity, and the threshold of the pavement maintenance time was proposed based on the prediction results. These results provide a basis for predicting rut development and pavement maintenance. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)
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12 pages, 3547 KiB  
Article
TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks
by Hyeeun Ku and Minhyeok Lee
Appl. Sci. 2023, 13(8), 5098; https://doi.org/10.3390/app13085098 - 19 Apr 2023
Cited by 18 | Viewed by 5753
Abstract
Generative adversarial networks (GANs) have demonstrated remarkable potential in the realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional latent space interpolation and manifold interpolation (GAN-CLS-INT) encounter challenges in generating images that accurately reflect the given text descriptions. To overcome these limitations, we [...] Read more.
Generative adversarial networks (GANs) have demonstrated remarkable potential in the realm of text-to-image synthesis. Nevertheless, conventional GANs employing conditional latent space interpolation and manifold interpolation (GAN-CLS-INT) encounter challenges in generating images that accurately reflect the given text descriptions. To overcome these limitations, we introduce TextControlGAN, a controllable GAN-based model specifically designed for text-to-image synthesis tasks. In contrast to traditional GANs, TextControlGAN incorporates a neural network structure, known as a regressor, to effectively learn features from conditional texts. To further enhance the learning performance of the regressor, data augmentation techniques are employed. As a result, the generator within TextControlGAN can learn conditional texts more effectively, leading to the production of images that more closely adhere to the textual conditions. Furthermore, by concentrating the discriminator’s training efforts on GAN training exclusively, the overall quality of the generated images is significantly improved. Evaluations conducted on the Caltech-UCSD Birds-200 (CUB) dataset demonstrate that TextControlGAN surpasses the performance of the cGAN-based GAN-INT-CLS model, achieving a 17.6% improvement in Inception Score (IS) and a 36.6% reduction in Fréchet Inception Distance (FID). In supplementary experiments utilizing 128 × 128 resolution images, TextControlGAN exhibits a remarkable ability to manipulate minor features of the generated bird images according to the given text descriptions. These findings highlight the potential of TextControlGAN as a powerful tool for generating high-quality, text-conditioned images, paving the way for future advancements in the field of text-to-image synthesis. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)
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18 pages, 3317 KiB  
Article
Sensitivity Study of Highway Tunnel Light Environment Parameters Based on Pupil Change Experiments and CNN Judging Method
by Bo Liang, Mengdie Xu, Zhiting Li and Jia’an Niu
Appl. Sci. 2023, 13(5), 3160; https://doi.org/10.3390/app13053160 - 1 Mar 2023
Viewed by 1736
Abstract
There is a sparsity of research regarding the nonlinear relationship between the sensitivity of the light environment parameters in the middle section of the tunnel under multi-factor conditions in multiple samples. Due to the lack of research, the present study was conducted in [...] Read more.
There is a sparsity of research regarding the nonlinear relationship between the sensitivity of the light environment parameters in the middle section of the tunnel under multi-factor conditions in multiple samples. Due to the lack of research, the present study was conducted in order to investigate said relationship. To determine the parameters of the eye-movement characteristics required for the convolutional neural network prediction evaluation, a tunnel simulation model was established using DIALux10 simulation software and a series of dynamic driving tests were conducted based on an indoor simulation experimental platform. Further, through employing the residual network ResNet to extract data features and the pyramidal pooling network module, a convolutional neural network judging model with adaptive learning capabilities was established for investigating the nonlinear relationship of sensitivity of light environment parameters. Following the test, the degree of influence on the diameter of the pupil for the different levels of each factor were: the optimal configuration of the staggered layout on either side of the lamp arrangement, the optimal 3 m height under the different sidewall painting layout height conditions, the optimal green painting color under the different sidewall painting color conditions, and the optimal 6500 k under different LED light source color temperature conditions. The results of the present study serve to expand the use of the convolutional neural network model in tunnel light environment research and provide a new path for evaluating the quality of tunnel light environment. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)
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13 pages, 14870 KiB  
Article
Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
by Rafaela Carvalho, Jorge Melo, Ricardo Graça, Gonçalo Santos and Maria João M. Vasconcelos
Appl. Sci. 2023, 13(4), 2315; https://doi.org/10.3390/app13042315 - 10 Feb 2023
Cited by 4 | Viewed by 2349
Abstract
The ongoing reading process of digital meters is time-consuming and prone to errors, as operators capture images and manually update the system with the new readings. This work proposes to automate this operation through a deep learning-powered solution for universal controllers and flow [...] Read more.
The ongoing reading process of digital meters is time-consuming and prone to errors, as operators capture images and manually update the system with the new readings. This work proposes to automate this operation through a deep learning-powered solution for universal controllers and flow meters that can be seamlessly incorporated into operators’ existing workflow. Firstly, the digital display area of the equipment is extracted with a screen detection module, and a perspective correction step is performed. Subsequently, the text regions are identified with a fine-tuned EAST text detector, and the important readings are selected through template matching based on the expected graphical structure. Finally, a fine-tuned convolutional recurrent neural network model recognizes the text and registers it. Evaluation experiments confirm the robustness and potential for workload reduction of the proposed system, which correctly extracts 55.47% and 63.70% of the values for reading in universal controllers, and 73.08% of the values from flow meters. Furthermore, this pipeline performs in real time in a low-end mobile device, with an average execution time in preview of under 250 ms for screen detection and on an acquired photo of 1500 ms for the entire pipeline. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Computer Vision in Industry 4.0)
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