Field Of Industrial Automation Research Articles

Challenges of Wireless Sensor Networks and their Solutions

Wireless Sensor Networks (WSNs) have emerged as a critical technology with uses in the fields of smart cities, industrial automation, healthcare, and environmental monitoring. This research paper provides a thorough review of the challenges inherent in WSNs, with the intention of providing light on the challenges that practitioners and researchers have while setting up and maintaining these networks. The challenges involve several aspects, comprising data management, security, scalability, energy efficiency, and dependability. Security concerns: given that these networks' communication is open and wireless, maintaining data integrity and secrecy presents substantial issues. Furthermore, it is essential to guarantee data transmission dependability in dynamic, frequently severe situations. This research analyzes these challenges, looks at the methods and solutions that are currently being offered in the literature, and also points out areas that need more research and development in the area of wireless sensor networks. Comprehending and resolving these obstacles is essential to realizing WSNs' complete potential and promoting their extensive integration in various fields.

Development and potential of robotisation and automation in Kazakhstan's automobile industry: a bibliographical and analytical review

The engineering sector, which includes automobile production, plays an important role in the national economy and industrial progress. It affects the development of production, scientific and technical progress, labor productivity, national security, quality of life. In order to increase productivity, reduce labor costs and improve product quality, industrial automation and robotization processes have been significantly developed in industrialized countries of the world. The global market for industrial robots is expected to grow 14.3% annually and reach $30.8 billion by 2027. However, Kazakhstan faces special difficulties in adopting these technologies due to the lack of development of automation infrastructure. This study aims to develop priorities and directions of advanced scientific and technological solutions for intelligent joint systems of robotics and automation, especially in the automotive industry of Kazakhstan. In this regard, the purpose of the study is to assess the current state of automation in the automotive industry of Kazakhstan, to identify challenges and opportunities associated with the introduction of robotics, and to provide a strategic basis for the advancement of these technologies. The research corresponds to the state programs of Kazakhstan on science, technology and digitization. The scientific and practical importance of this work is that it is aimed at providing solutions for increasing efficiency by adapting advanced robotic technologies to local production systems. Through the development of domestic innovations and the transfer of global technologies, research contributes to the field of industrial automation and helps Kazakhstan's industries to achieve global competitiveness. It is expected that the practical results of the research will contribute to the improvement of production quality, efficiency and productivity in the automotive sector of Kazakhstan, contributing to sustainable development in accordance with world trends. The value of the work lies in providing individual, technologically advanced solutions to meet the nation's urgent need for robotics automation and integration.

Using Mixed Reality for Control and Monitoring of Robot Model Based on Robot Operating System 2

This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction.

Live Data Monitoring in Industry

Abstract: In contemporary industrial environments, real-time monitoring of critical parameters is vital for ensuring safety, efficiency, and compliance with regulations. This thesis details the development and implementation of an advanced live data monitoring system designed to track temperature, humidity, fire presence, and oxygen levels. Key components of the system include a DHT sensor for temperature and humidity measurement, a fire sensor for fire detection, an oxygen sensor for monitoring atmospheric oxygen, and a buzzer for immediate auditory fire alerts. Central to the system is the ESP8266 WiFi module, which facilitates the wireless transmission of sensor data to ThingSpeak, a cloud-based data collection and visualization platform. This setup allows for continuous, remote monitoring of environmental conditions, providing critical insights and alerts to industrial operators. The project delves into the hardware configuration, sensor calibration, data acquisition processes, and wireless communication protocols essential for effective real-time monitoring. It also addresses the challenges of integrating multiple sensors and ensuring reliable data transmission in an industrial setting. The results demonstrate the system's capability to provide accurate, timely data, enhancing safety protocols and operational efficiency. By utilizing IoT technology, the proposed system offers a scalable solution for industrial environments aiming to implement advanced monitoring and alert systems. This work contributes to the field of industrial automation by illustrating a practical application of IoT in improving monitoring and safety measures, highlighting both the technical achievements and practical implications of the implemented system.

Application Status and Challenge of PROFIBUS Technology in Industrial Automation Field

This article first introduces the basic concepts and principles of PROFIBUS technology, and then elaborates on its application status in the field of industrial automation from three aspects: manufacturing, process control, and drive technology, including its wide range of applications and advantages. Subsequently, the main challenges currently faced by PROFIBUS technology were analyzed, such as integration with emerging industrial Ethernet technologies, data security issues, and the complexity of system upgrades and maintenance. Finally, the article looks forward to the future development trends of PROFIBUS technology, including further integration with industrial Ethernet technology and the introduction of encryption algorithms to enhance data security. Overall, PROFIBUS is a mature fieldbus technology. Still crucial in the field of industrial automation. However, with the development of new technologies and changes in application requirements, its future development still faces many challenges and opportunities.

Domain‐specific translation tool from structured text to C source code with code readability enhancement in programmable logic controllers

AbstractThe Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings.

Digital Twin in Fluid Power: Review- Technology Trends

The Digital Twin (DT) is the most modern technology in Industry 4.0, which combines digital and physical components, accurate visualization, and multifaceted perception to represent the digital-physical world in real-time. The use of simulation, monitoring, optimized performance, and projection are crucial and increasingly popular, particularly in the field of industrial automation. Fluid Power Application (FPA) is an essential industrial automation technology that offers accurate monitoring, immense power, flexibility, reliability, cost-effectiveness, and seamless compatibility with pre-existing automated systems through the utilization of hydraulic and pneumatic power. Nevertheless, certain technological elements and emergent DT technology in fluid power systems have not been extensively explored. Based on the conception and constituents addressed in a companion review paper (1), this paper looks into various technologies that allow the seamless integration of both physical and digital representations in diversified FPAs. This article intends to thoroughly examine the crucial role of technological enablers in the successful implementation of digital twin. The paper provides an in-depth review of the technological enablers that support DT, specifically focusing on software used for the simulation of FPA and programmable logic controllers (PLCs) with the accompanying software. This review paper enhances the academic discourse by emphasizing different emerging technologies and acts as a catalyst for DT in current investigation efforts and breakthroughs in FPAs.

ADAPTABILITY IN INDUSTRY 4.0: SERVICE-ORIENTED ARCHITECTURE TO DEPLOY ARTIFICIAL INTELLIGENCE ON INDUSTRIAL AUTOMATION

Industry 4.0 represents a revolution in the business environment, driving the integration of information technologies and industrial automation, with the main objective of reducing latency in decision-making. Artificial Intelligence (AI) plays an essential role in advanced data analysis and resource optimization, allowing accurate predictions and agile decisions, however its implementation presents challenges, such as algorithm complexity and integration with industrial automation systems. An innovative solution to overcome these challenges is the implementation of a service-oriented architecture, which creates modular and interoperable systems, a concept especially relevant in the practical application of industrial automation, in which the integration between Information Technology (I.T) and Automation Technology (A.T) is crucial. This work presents, through experimental research, an innovative solution based on the development of a computer vision application, isolated in a Docker container. This application is designed to inspect the assembly of parts by a robotic system and establish communication with a Programmable Logic Controller (PLC) to approve or disapprove the assembly. The results of the adopted architecture demonstrate a flexible approach that simplifies the operation of AI systems, allowing operation with both AI enabled and disabled, reducing potential disruptions to the workflow. This research promises to open paths for future innovations and advances in the field of industrial automation and Artificial Intelligence, offering a model that effectively combines the agility of AI with the robustness of automation.

IoT-Based Industrial Debug Message Display Using AWS, ESP8266 And C#

Purpose: In the industrial automation field, debugging is an essential part. Generally, most of the debugging we do in the product development phase and a little bit at the service time. The typical procedure to debug any electronic device is to display a “debug message” inside the terminal window. For this purpose, we commonly use various converters which convert from USB to other ports like RS232, RS485, etc. However, sometimes, we cannot connect our debug cable directly to the working devices. That would be better if the debug message is displayed on the working system terminal without any wire. Here we demonstrate a procedure to display the debug message without any wire. Using the IoT, we do that efficiently. The Code is available on GitHub. Interested researchers can download and continue further research on it. Design/Methodology/Approach: First, We need to create the IoT profile inside the AWS IoT cloud. Using the AWS IoT credential, We update the firmware of the Wifi module. Then it is installed inside the system, which we are debugging. The wifi module’s Transmit and receive pin will be connected to Device’s serial PIN (TX-RX, RX-TX). When the Device sends any debug message through the serial port, it is received by the Wifi module and will update the AWS cloud shadow register. We build an AWS IoT MQTT client in the C# (visual studio. NET). The IoT client fetches the updated data and notifies the UI main thread. One listbox is connected to it. Finally, the UI thread pushes the updated data into the Listbox's first row. Findings/Result: Sometimes, our Device needs to debug without any wire because there is no scope to connect it with the cables. So this procedure can help to debug wirelessly. This procedure has several advantages. Using this debug display eliminates to gets hazards from debugging systems. The debug message can be visible from a remote place. It is also possible to observe the message from multiple remote locations Opening multiple IoT Clients, and subscribing to the same IoT Topic. Originality/Value: The described procedure is a different way to display the debug message. Some advantages are available over traditional procedures. Using this procedure, the researcher can transmit real-time data. It can provide value to the researcher’s work. Using this procedure, the researcher can transmit their sensor data to a remote place. Furthermore, it can save to the remote server for future use. Paper Type: Experimental-based Research.

Electropneumatic Training to Improve the Professional Competence of Muhammadiyah Vocational Teachers: Industrial Revolution 4.0 Implementation

Vocational High Schools (SMK) are required to have competence and be able to meet the growing industrial needs. Competent vocational teachers play an important role in producing skilled human resources (students). To achieve these competencies, it is necessary to improve the quality of learning media. Therefore, the use of learning media, especially in the field of electropneumatics, is crucial. This training aims to provide knowledge and skills for operating electropneumatic systems on industrial equipment. The method used in this research is a combination of theory presented classically and practice using an electropneumatic trainer module designed and built by the team. A Pre-test and post-test were conducted to evaluate the results of the training. The training was held for one day with a total of 15 participants from SMK Muhammadiyah 3 Klaten Utara. The evaluation results showed that the teacher's (15 people’s) understanding of electropneumatic material increased by 56% from the pre-test with an average score of 40 to the post-test with an average score of 62. It is hoped that this training can help teachers at SMK Muhammadiyah 3 Klaten Utara to improve their competence in teaching and provide significant benefits for students facing challenges in the field of industrial automation, especially electropneumatics

A Semantic Information Decomposition Network for Accurate Segmentation of Texture Defects

Defect detection on textured surfaces remains a challenging task in the field of industrial automation due to the wide range of textures and defects. Current unsupervised learning-based texture defect detection methods based on texture background reconstruction, cannot detect texture defects with high precision because it is difficult to guarantee a high-precision reconstruction of the texture background while suppressing the defect foreground. In this study, we propose a novel semantic information decomposition network (SIDN) for accurate texture defect segmentation. The SIDN is trained on artificial defective images produced by a defect generation module (DGM). First, the SIDN uses a feature extraction module (FEM) to extract latent features with both texture semantic information and defect semantic information. Then, a novel feature separation extraction module (FSEM) for decomposing the texture semantic information and defect semantic information from the feature map generated by the FEM is proposed, preventing the coupling of the texture and defect semantic information from affecting the final segmentation accuracy. Next, a novel global semantic relation module (GSRM) is proposed to determine the relevance of the global semantic information to comprehensively consider the context and improve the feature representation. Finally, a segmentation module (SM) that directly segments the textures and defects instead of reconstructing the texture background is proposed. The final detection result is obtained by calculating a weighted average of the texture and defect segmentation results. The extensive experimental tests with the most popular and most challenging texture defect dataset demonstrate that the SIDN achieves accurate segmentation of various texture defects without using real defect samples.

Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process.

In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the efficiency and productivity of the manufacturing process. A combination of feature engineering and machine learning techniques was applied in order to analyze 11 signals generated during the milling process. The presented approach achieved high accuracy in detecting tool wear and predicting tool failure, outperforming traditional methods. The final findings demonstrate the potential of machine learning algorithms in improving tool condition monitoring in the manufacturing industry. This study contributes to the growing body of research on the application of artificial intelligence in industrial processes. In conclusion, the presented research highlights the importance of adopting innovative approaches to address the challenges of tool condition monitoring in the manufacturing industry. The final results provide valuable insights for practitioners and researchers in the field of industrial automation and machine learning.

Sliding Mode Control Based on High Gain Observer for Electro-Hydraulic Servo System

The electro-hydraulic servo system is widely used in industrial automation fields for its merits of the high force to weight ratio, compact size, and fast response. However, the parameter uncertainties and external disturbances of the electro-hydraulic servo system significantly deteriorate the control performance of conventional linear controller in practice. To deal with this problem, sliding mode controller (SMC) that incorporates high gain observer (HGO) is proposed in this paper. HGO is used to obtain the accurate time derivative of position signal for sliding mode controller design. The stability of the control system is guaranteed by Lyapunov stability theory. Comparation simulation is conducted to validate the effectiveness of the presented control scheme.

Robotic Manipulation in the Ceramic Industry

Robotic manipulation, an area inside the field of industrial automation and robotics, consists of using a robotic arm to guide and grasp a desired object through actuators such as a vacuum or fingers, among others. Some objects, such as fragile ceramic pieces, require special attention to the force and the gripping method exerted on them. For this purpose, two grippers were developed, where one of them is a rotary vacuum gripper and the other is an impact gripper with three fingers, each one equipped with a load sensor capable of evaluating the values of load exerted by the grip actuators onto the object to be manipulated. The vacuum gripper was developed for the purpose of glazing a coffee saucer and the gripper with three fingers was developed for the purpose of polishing a coffee cup. Being that the impact gripper with sensorial feedback reacts to the excess and lack of grip force exerted, both these grippers were developed with success, handling with ease the ceramic pieces proposed.

On Paradigm of Industrial Big Data Analytics: From Evolution to Revolution

The arrival of the intelligent manufacturing and industrial internet era brings more and more opportunities and challenges to modern industry. Specifically, the revolution of the production mode of traditional manufacturing is undergoing thanks to the techniques including but not limited to digits, network, intelligence, and industrial automation fields. As the core link between intelligent manufacturing and industrial internet platform, industrial Big Data analytics has been paid more and more attention by academia and industry. The efficient mining of the high-value information covered under industrial Big Data and the utilization of the real-life industrial process are among the hottest topics at present. Meanwhile, with the advanced development of industrial automation toward knowledge automation, the learning paradigm of industrial Big Data analytics is also evolving accordingly. Therefore, starting from the perspective of industrial Big Data analytics and aiming at the corresponding industrial scenarios, this article actively explores the revolution of the learning paradigm under the background of industrial Big Data: 1) The evolution of the industry Big Data analytics paradigm is analyzed, that is, from isolated learning to lifelong learning, and their relationships are further summarized; 2) Mainstream directions of lifelong learning are listed, and their applications in industrial scenarios are discussed in detail; 3) Prospects and future directions are given.

Industrial automation. Non-standard examples of import substitution

Import substitution, at present, has become one of the main directions of the technological development of our state. At the same time, import substitution is traditionally understood as the development of a complete or functional analogue of a particular product. The article shows two real examples of «indirect» import substitution, while the apparent cardinal difference in the objects of import substitution indicates (according to the author) that such an approach is fully justified and can be used not only in the field of industrial automation.

IO-Link Wireless Sensitivity Testing Methods in Reverberation Chambers

Communication reliability is a challenging requirement, which implies the need for over-the-air (OTA) testing. Reverberation chambers (RCs) are widely used for OTA tests in various fields. Due to their properties, such as inherent radio channel emulation or the arbitrary orientation of the equipment under test (EUT) in the test volume, they can be used as advantageous test environments for wireless products in the field of industrial manufacturing automation, such as for the IO-Link Wireless (IOLW) standard. In this paper, the different OTA sensitivity test procedures total isotropic sensitivity (TIS), average fading sensitivity (AFS) and mean channel packet error (MCPE) method, which is based on the fundamental channel model of the wireless standard, are described and evaluated in various variants. A core aspect of the proposal is the impact of the possible use of frequency hopping of the wireless equipment under test. The respective advantages and disadvantages are shown. Overall, TIS proves to be a suitable alternative for IOLW OTA sensitivity testing.

DEVELOPMENT OF A DIDACTIC SIMULATOR FOR WATER TREATMENT SYSTEMS

This paper presents the implementation of a didactic simulator for industrial automation supported by a supervision system. As a result of this work, a solution based on a system of six reservoirs was developed, together with a supervision system that allows the simulator to be controlled and monitored through a Raspberry Pi with a touch screen display and an android application. The aim of this work is to develop a system that can be used by other undergraduate and master's students of the electrical engineering course at ISEL, for them to deepen their knowledge in automation. It is also intended that the simulator be used in fairs, exhibitions, and open days to internal and external students at ISEL, showing some of the work that has been developed in the master’s degree in electrotechnical engineering, especially in the field of industrial automation.

A Teleoperation Framework Based on Heterogeneous Matching for Hydraulic Manipulator

In recent years, hydraulic manipulators have been more and more widely used in the field of industrial automation because of their high power-to-weight ratio. Compared with autonomous control, bilateral teleoperation is a more reliable way to operate hydraulic manipulators on harsh occasions. However, teleoperation application in the hydraulic field is still limited due to operation accuracy and master–slave heterogeneous problem. In this paper, a teleoperation framework based on master–slave heterogeneous matching and nonlinear precise motion control for hydraulic manipulator is proposed, including workspace mapping algorithm, velocity-limited interpolator, inverse kinematics solver, trajectory planner, model-based controller, and feedback torque generator. The high-precision teleoperation of the hydraulic manipulator in the situation of master–slave heterogeneity is finally realized based on the designed teleoperation framework. The specialized experiment is set up, and the results also show that the method proposed in this paper can satisfy the teleoperation requirements of the hydraulic manipulator with the maximum operating accuracy up to 0.02 m under the condition of master–slave heterogeneity, which can greatly improve the applicability of hydraulic manipulator teleoperation in the case of master–slave heterogeneity.

Automatic defect detection of texture surface with an efficient texture removal network

Automated defect inspection of texture surface is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. We present a simple but powerful image transformation network to remove textures and highlight defects at full resolution. The simple full convolution network consists only of 3 × 3 regular convolution and several dilated convolution blocks, which makes it compact and able to capture multi-scale features effectively. To further improve the ability of the network to suppress texture and highlight defects, a polynomial loss function combining perceptual loss, structural similarity loss and image gradient loss is proposed. In addition, a semi-automatic annotation method mainly composed of wavelet transform and relative total variation is designed to generate a data set of image pairs containing the original texture image and the desired texture removal image. We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method. The method has been applied to the surface defect online detection system of an aluminum ingot milling production line, which effectively improves the surface inspection efficiency and product quality.