Field Of Industrial Automation Research Articles

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.

Deep Learning–Based Soft Sensors for Improving the Flexibility for Automation of Industry

Automation in industries offers the benefits of enhancing quality and productivity while minimizing waste and errors, raising safety and adds stability to the production process. Industrial automation offers high profitability, reliability, and safety. It is beneficial to employ machine learning in the field of industrial automation as it helps in monitoring and performing maintenance on industrial machinery. Rational industrial development is closely associated with efforts for automating industrial techniques in all existing ways. Latest improvements in the automation of industrial systems resulted in decrease in cost of energy consumption and hardware. The proposed system is dealt with deep learning–based soft sensors for automation of industrial processes. The eminent benefits of soft sensors are versatility, flexibility, and low cost. With deep learning, many number of features could be processed. Thus, deep learning–based soft sensor encapsulates the above benefits. Soft sensors offer another way for the measurement of process variables, which are measured offline. Deep learning techniques are famous in the design of soft sensors for tough nonlinear systems due to the robustness and accuracy. The work depicted here designs a soft sensor based on deep learning algorithm for automation of industry. In the proposed system, a soft sensor contemplated on deep learning such as the deep neural network (DNN) is presented. The application of deep learning–based soft sensors in the automation of some industrial processes is also discussed here. The proposed system is tested on automatic control on solar power plants and in the measurement of reactive energy in industries. It was found that the proposed system yielded better results with its application in the automated industrial processes.

Conveyor Belt Speed Control Through CAN BUS in CoppeliaSim using Arduino Mega2560

Purpose: CAN Bus is a robust way to communicate inter-device Communication. Primarily it is used in the vehicle. But now, it is also used in various industrial automation fields. Among different branded robots, they use CAN-bus to control the robot movement. When the new researchers integrate the can bus into their project, they need to spend time running and handling the device through the CAN bus. This research work is basically for those trying to integrate the CAN bus into their project in a short period. Here, we briefly describe the CAN bus protocol first, then go through a practical experiment to better understand it. In our experiment, we feed the Analog value as a speed profile through the CAN bus to the CoppeliaSim virtual environment to see the speed variation of the simulated conveyor belt upon rotating the potentiometer. The complete project code and demo video are available on Github. The interested researcher can download and experiment on it. Design/Methodology/Approach: This research work demonstrates how to exchange data through CAN bus between a virtual and a natural environment. We created a virtual environment using the CoppeliasSim robot simulator. Inside the simulator, we add a conveyor belt. Using c#, we created a bridge application that translates the data between the simulator and the natural environment outside it. We used two Arduino Mega 2560 boards and two MCP2515 based TJA1050 popular CAN driver modules. The CoppeliaSim requests data to the remote master through the bridge application and CAN bus. The remote master reads the analog value and sends it back to the simulator through the local master. CoppeliaSim changes its conveyor belt speed by parsing the data and converting it to the speed value. Findings/Result: We can find some concepts and procedures to control the virtual elements from the external world through the CAN bus. We can convert any environmental analog parameter into digital form and display it locally or remotely inside the simulator. We can build the digital twins used to process monitoring in the industrial automation field following this experiment. Originality/Value: Most CAN bus-related documents lack a practical approach or an accumulation of workable summaries. We demonstrate the CAN bus differently in this research work. This paper is an entirely practical-oriented demonstration. This is the unique approach to controlling the virtual element from the real world through the CAN bus. The new researcher can get helpful references to integrate the CAN bus into their project. Paper Type: Experimental-based Research.

Desain Prototipe Handwell Universal Tool Grinding Machine Menggunakan Konsep Manufaktur Teknologi 3D Printer

Abstract : The presence of the manufacturing world has brought a huge impact on the progress of science and technology in the industry which is currently growing rapidly, especially in the field of industrial automation. Additive Manufacturing known as 3D printing Technology in its philosophy of helping humans in completing every task by playing a very important role in product development, prototyping, and manufacturing process.The results of the manufacturing process using a 3D printer are very good, where the design results can be translated to a 3D Printer and produce objects that match the design results from the software inventor. Success in this manufacturing process is obtained from the results of setting the 3D printer parameters during the proper slicing process, such as setting material temperature, support, printer speed, fan speed, layer thickness (quality), wall thickness (Shell), Infill (filler). and others. Keywords: Prototype Design, 3D Printer, Manufacturing Process

A Defect-Inspection System Constructed by Applying Autoencoder with Clustered Latent Vectors and Multi-Thresholding Classification

Defect inspection is an important issue in the field of industrial automation. In general, defect-inspection methods can be categorized into supervised and unsupervised methods. When supervised learning is applied to defect inspection, the large variation of defect patterns can make the data coverage incomplete for model training, which can introduce the problem of low detection accuracy. Therefore, this paper focuses on the construction of a defect-inspection system with an unsupervised learning model. Furthermore, few studies have focused on the analysis between the reconstruction error on the normal areas and the repair effect on the defective areas for unsupervised defect-inspection systems. Hence, this paper addresses this important issue. There are four main contributions to this paper. First, we compare the effects of SSIM (Structural Similarity Index Measure) and MSE (Mean Square Error) functions on the reconstruction error. Second, various kinds of Autoencoders are constructed by referring to the Inception architecture in GoogleNet and DEC (Deep Embedded Clustering) module. Third, two-stage model training is proposed to train the Autoencoder models. In the first stage, the Autoencoder models are trained to have basic image-reconstruction capabilities for the normal areas. In the second stage, the DEC algorithm is added to the training of the Autoencoder model to further strengthen feature discrimination and then increase the capability to repair defective areas. Fourth, the multi-thresholding image segmentation method is applied to improve the classification accuracy of normal and defect images. In this study, we focus on the defect inspection on the texture patterns. Therefore, we select the nanofiber image database and carpet and grid images in the MVTec database to conduct experiments. The experimental results show that the accuracy of classifying normal and defect patch nanofiber images is about 86% and the classification accuracy can approach 89% and 98% for carpet and grid datasets in the MVTec database, respectively. It is obvious that our proposed defect-inspection and classification system outperforms the methods in MVTec.