Automation Of Industrial Production Research Articles

Research on Visual Localization and Gripping Technology of Robotic Arm Based on Deep Learning

With the continuous development of industrial automation, robotic arms are more and more widely used in production lines. In order to realize the autonomous positioning and grasping function of robotic arm, this paper studies the visual positioning and grasping technology of robotic arm based on deep learning. The deep learning algorithm processes and analyzes the image, realizes the automatic identification and localization of the target object, and then guides the robotic arm to carry out accurate grasping. The experimental results show that this technology can effectively improve the positioning accuracy and grasping success rate of the robotic arm, which provides strong support for the intelligent upgrade of industrial automation production lines.

Exploration and Application of Cloud-Network Integration Control Architecture in Industry

Cloud-network integration(CNI) is an inevitable choice to enhance the supply capacity of industrial Internet. In traditional industrial control systems, there are problems such as insufficient depth of perception, insufficient connectivity and insufficient analytical predictability. Firstly, an industrial CNI control architecture is proposed in this paper. Then a software-defined PLC integrated development & runtime environment based on the cloud framework is developed and deployed on a private cloud platform. An efficient 5G private network is created to facilitate wireless omnidirectional connectivity of cloud PLC. The problems of complex industrial field circuits and difficult to unify protocols are solved through 5G intelligent terminal devices. Finally, the cloud control architecture and software platform are successfully applied to the remote centralized control of the tundish preparation system in a steel enterprise’s two steelmaking plants. The results shows that the innovative system architecture meet the requirements for high-performance access, transmission, computing and storage in industrial plants, which achieves the cloud control of entire process industrial automation production.

Path Planning: An Analytical Comparison of A* Algorithm Fusion DWA and Ant Colony Algorithm Fusion DWA

In recent decades, the robotics sector has grown significantly, and the automation of industrial production and daily life has become a shared objective. In recent years, self-driving cars have emerged around the globe, but the majority are still in the research phase and very few are in use in developed regions, not only because of their cost, but also because of their safety and efficiency for path planning and the speed of avoidance in the face of dynamic obstacles, which has been a concern. In this work, multiple path planning algorithms, including the A* method, the dynamic window technique, and the ant colony optimization (ACO) algorithm, will be empirically explored. Initially, the fundamental algorithm will be enhanced, and then the enhanced algorithms will be fused, resulting in a considerable increase in responsiveness and computing efficiency. The study will then attempt to combine the A* algorithm and the ACO algorithm with the dynamic window method and analyse the pros and cons of the two fusion algorithms for path planning in various situations. The A* algorithm fused with the dynamic window method is, on average, more suited for complex situations than the ACO algorithm fused with the dynamic window technique.

Review of Occupational Health and Safety Management System and Hazards Controls in the Motion & Industrial Automation Products Manufacturing Industries

Occupational injuries are a major problem worldwide and affect all countries, particularly developing ones. In recent decades, the application of approaches such as the Occupational Health and Safety Management System (OHSMS) has led to the successful control of workplace injuries in high-income countries. The Occupational Health and Safety Assessment Series (OHSAS) 18001 as a world- recognized OHSMS has gained considerable acceptance by a large number of organizations. However, few studies have examined the effectiveness of ISO 45001on safety performance in certified organizations. This study consisted of four sub-studies, and was conducted to explore the effect of ISO 45001on the occupational injury, safety climate, and Occupational Health and Safety (OHS) practices in ISO 45001-certified companies compared with a control group in Iran. ISO 45001practices were also examined in the certified companies, where interviews were conducted to explore the influencing factors on the effectiveness of ISO 45001. A negative binomial regression indicated no significant effect of ISO 45001certification on the occupational injury rate. The second sub-study applied a new safety climate questionnaire, and a hierarchical regression indicated that the safety climate was influenced by the implementation of ISO 45001and safety training. The third sub-study pointed to the better OHS practices of the certified companies compared with the control ones. The results also showed that adopting the ISO 45001standard improved the documentation for the management of OHS, but did not lead to continuous improvement in the required practices. The evaluation of the collected evidence revealed the main reasons for a poor safety culture. The interviewees emphasized the internal and external influencing factors in the effectiveness of OHSAS including commitment of top management and the enforcement of OHS legal requirements. It can be concluded that the implementation of ISO 45001in an organization is not a guarantee of improved safety performance and of the existence of a high-quality management system. This study suggests that certified companies should focus on proper improvement and maintenance of the implemented management systems by escalating their commitment to the requirements of the established management systems and by participating their employees in ISO 45001practices. This study also emphasized the importance of providing safety training for employees who work in the certified companies. These efforts may help the companies in the creation of a good safety culture and the transforming the paper systems into effective management systems to make improvement in OHS performance. The aim of this report to assess and evaluate the effective implementation of occupational health & management system and hazards controls in the Motion & Industrial Automation products manufacturing Industries as per the relevant ISO standards and hierarchy of hazards controls, which is a system used in industry to minimize or eliminate exposure to hazards. Falls, chemical spills, fire, explosions, working at height, confined space working, mechanical material handling, etc. are few kinds of incidents that can happen in a manufacturing environment. Such incidents are not only tragic and frequently fatal for workers, but also extremely expensive for the employers. Between worker’s compensation, regulatory penalties, reduced/diminished productivity and the costs of fixing or replacing damaged equipment, a single incident can financially devastate a business. To avoid industrial accidents / catastrophes, keep your people from harm, ensure manufacturing compliance, and protect your bottom line, you need to get serious about safety. Manufacturing Safety is everything organizations does to ensure the physical well-being of the workers in the manufacturing facility or facilities. Manufacturing Safety is also about more than checking a few boxes or putting up ‘safety first’ signage.  Manufacturing Safety keeps people alive and unharmed.  Manufacturing Safety is also tied to workforce productivity.  Manufacturing Safety is an essential form of risk management.  Manufacturing Safety is a legal requirement.  Manufacturing Safety and Profitability go hand-in-hand. Safe companies perform better and sustain in the business longer that unsafe companies.

Small target location algorithm based on optimized template matching

At present, few published template matching algorithms can achieve the performance of commercial software. This paper studies and optimizes the existing geometric feature template matching algorithm, and combines it with the improved Fourier Merlin transform registration algorithm, and finally obtains a fast small target location algorithm. The matching time of the algorithm is 1-4ms, its positioning accuracy is sub-pixel level, the angle accuracy is less than 1 °, and it has good robustness to illumination. The experimental data show that the algorithm meets the requirements of real-time and accurate industrial automation production.

ТЕОРЕТИКО-МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ КОНКУРЕНТОСПОСОБНОСТИ ПРОИЗВОДИТЕЛЕЙ ПРОМЫШЛЕННОЙ ПРОДУКЦИИ В РЕГИОНАХ РОССИЙСКОЙ ФЕДЕРАЦИИ

The article examines the issues of competitiveness of manufacturers of industrial products in the regions of the Russian Federation. The author analyzes the principles and factors influencing competitiveness, such as innovation, quality, efficiency, market orientation, flexibility and adaptation. The traditional classification of the competitiveness of manufacturers of industrial products is given and supplemented with features that take into account environmental sustainability, availability of financial resources, the degree of automation of industrial production, international competitiveness and the level of pricing policy. The features highlighted by the author will allow us to more accurately assess and characterize the competitiveness of manufacturers in various aspects.

Application of Distributed wMPS System in AGV Automatic Navigation

AGV vehicles play an important role in industrial automation production, and automatic navigation is the basis for automatic operation of AGV vehicles. In order to improve the navigation accuracy of AGV vehicles, based on the workspace measuring and positioning system (wMPS), this paper proposes to use upright and top mounted transmitter stations to build precision coordinate measuring fields, and analyzes the construction process and layout optimization principle of precision measuring fields in detail, Finally, the measurement accuracy of the coordinate field is illustrated through experiments. The experimental results show that the comparison error between the precision coordinate measurement field constructed by the method proposed in this paper and the measurement results of the tracker is controlled below 0.2 mm, which can effectively ensure the measurement accuracy and realize the automatic precision navigation of AGV vehicles.

Automatic Control System Design for Industrial Robots Based on Simulated Annealing and PID Algorithms

The industry is at the heart of a country’s development, and robot automation is the key to this. The automation of industrial robots is extremely beneficial to industrial development. Although China has been increasing its investment in industrial robot automation and control research, there are still many problems in the field of automation and control in the application of industrial robot technology, and there is still a certain gap between the developed countries. On the one hand, this is a serious obstacle to the development of industrial robotics in China, and on the other hand, it has a direct impact on the automation of industrial production, which has a very restrictive effect on industrial development. Therefore, it is necessary to focus on the value of computer technology, automation control technology, and sensor technology in the field of automation. The use of industrial robots can replace monotonous manual labour, reduce the intensity of operations, and increase productivity through high operational precision values. Scientific progress has driven the development process of industrial automation to a new level, allowing fully automated production equipment to become mainstream and gradually replace manual operations. In recent years, the development of industrial automation has led to the development of a series of related industries, of which automated manipulators are one. As a new automatic production line operation equipment, the development of manipulators can greatly improve the automation production level of enterprises and improve labour efficiency. This paper explores the development status of automated manipulators and explore the optimal motion path of manipulators in automatic control engineering by combining simulated annealing algorithm and PID algorithm, so as to greatly enhance the application of truss manipulators in production lines.

A Four-Stage Product Appearance Defect Detection Method With Small Samples

With the automation of industrial production, appearance defect detection based on machine vision plays an important role in product quality control. The scarcity of defect samples and real-time requirement are the main challenges in this field. Many existing studies are based on semantic segmentation network, but they cannot provide a classification confidence score for each image and only report the segmentation tasks metrics, which ignore that the positive or negative decisions are the key of defect detection. Therefore, this paper proposes a four-stage appearance defect detection model: contrast enhancement, segmentation, correction, and decision, which can achieve high detection accuracy with a severe shortage of positive samples. Since the proposed model simplifies U-Net to segment those candidate defect regions, and constructs a lightweight decision network based on the candidate regions and segmented mask, the proposed method not only achieves fast inference speed, but also obtain good performance with fewer defect samples. Experiments are implemented on three public datasets: magnetic tile dataset, Kolektor surface defect dataset and DAGM2007 dataset. The influence of each module on the detection accuracy is analyzed. Experimental results show that the proposed model achieves excellent performance comparing with other state-of-art methods.

Surface Defect Detection Model of Motor Commutator Based on Semantic Segmentation

The surface defect detection of industrial parts is very important in industrial automation production, but there are problems with the small number of defect samples and the small-scale defect. To solve the above problems, this paper proposes a surface defect detection model of motor commutator based on semantic segmentation. The model is divided into two parts: segmentation network and classification network. First, the segmentation network uses an encoder-decoder to better capture small targets. The encoder uses an improved lightweight network MobileNet V3 as a feature extractor. Effectively learn the optimal features from a small number of samples, and improve the segmentation accuracy of the network. Then the classification network uses the segmentation results to make predictions, and the segmentation results provide interpretability for the prediction of the classification network. Experiments show that the proposed model has good generalization ability on a small number of samples, can effectively detect small-scale defect, and has high accuracy.

Application and Innovation of Electronic Information Technology in Industrial Electrical Automation

With the continuous development and progress of my country’s social economy, electronic information technology has begun to penetrate into all areas of real life. Today, electronic information technology has become an indispensable key technology. Applying this technology to industrial electrical automation can not only bring faster and more convenient technology to industrial production, but also help the development of industrial production automation control. Therefore, electronic information technology has been widely used in industry. This article mainly analyzes the overview of electronic information technology and its application significance in industrial electrical automation, and then puts forward the practical application and innovative measures of electronic information technology in industrial electrical automation, hoping to provide reference for relevant personnel.

Research on slipping-pattern recognition and positioning error compensation of wheeled robot.

The autonomous navigation and positioning system is one of the key equipment in industrial automation production. Due to slippage, the odometer (OD) data cannot reflect the real rotation of the wheeled robot, which may cause the unmanned robot to deviate from the preset route. To solve this problem, the integration of the strap-down inertial navigation system (SINS) and the OD is used, and a novel slipping recognition algorithm based on neural network learning and an error compensation method is proposed to improve the navigation accuracy when skidding. First, the output data of the SINS and OD are studied and features are extracted for neural network learning. After training and modeling, the state of the robot slipping can be identified quickly and effectively. Second, different models are designed to compensate the errors of the different slip patterns. Experimental results show that the comprehensive recognition rate of five modes within 1s is better than 98.9%. After compensation, the positioning errors of the left and right slip modes have been reduced to 15mm and the positioning errors of the both side and locked slip modes have been reduced to 40mm. Compared with before compensation, the errors are reduced to less than 6%.

Application of Industrial Robots in Automated Production Lines under the Background of Intelligent Manufacturing

Intelligent manufacturing is the theme and main direction of the “Made in China 2025” development strategy, and the application of industrial robots is an important direction of intelligent manufacturing. In the next few years, industrial robots will be widely used in various enterprises, which will inevitably require a large number of high-tech industries. Industrial robots are high-tech products of modern society and play an important role in the process of economic development, especially in the manufacturing industry. Industrial robot technology is widely used in automated production lines, so that industrial production efficiency can be greatly improved. Instead of manual labor to carry out various complex production and operations, to achieve industrial production automation. Analyze the application of industrial robots in automated production lines at this stage, and explore their future development directions, so that they can better serve the manufacturing industry. This article takes the industrial robots and PLCs in the industrial automation production line as the research object, and takes the automated production line for mass production of a product as an example. It explains the joint control scheme of the PLC controller and the industrial robot controller, and provides for the construction of joint control of similar production lines in the future.

Application of Information Teaching in the Course of Online Programming and Debugging of Industrial Robots

With the development of industrialized society, how to improve industrial production efficiency has become the focus of social concern. With the support of emerging technologies such as artificial intelligence and computer technology, more and more industrial robots are applied to industrial production and become necessary equipment for industrial production automation. Doing a good job in the promotion and performance optimization of industrial robots is the top priority for the sustainable development of the manufacturing industry. Industrial robot online programming and debugging courses are professional courses for industrial robot technology, which involve touch screen technology, programmable controllers, sensors and other multidisciplinary knowledge. Educational and teaching activities must be able to develop online programming and debugging courses for industrial robots. Teaching reform and innovation, thereby enhancing students’ practical application ability in industrial robot technology, and laying a good talent and technical foundation for the batch and intelligent development of industrial robots are important.

Robotic Arm Control System Based on AI Wearable Acceleration Sensor

The position of mechanical arm in people’s life is getting higher and higher. It replaces the function of human arm, moving and moving in space. Generally, the structure is composed of mechanical body, controller, servo mechanism, and sensor, and some specified actions are set to complete according to the actual production requirements. The manipulator has flexible operation, good stability, and high safety, so it is widely used in industrial automation production line. With the development of science and technology, many practical production requirements for the function of the manipulator are more and more refined, especially in the high-end research field. For example, medical devices, automobile manufacturing, deep-sea submarines, and space station maintenance put forward higher requirements for it. In terms of miniaturization and precision, it can meet the needs of scientific research and actual production. But these are inseparable from the motion control system technology. This paper mainly introduces the research of manipulator control system based on AI wearable acceleration sensor, aiming to provide some ideas and directions for the research of wearable manipulator. This paper presents the research method of manipulator control system based on AI wearable acceleration sensor, including the establishment of manipulator kinematics model, common filtering algorithm, and PI algorithm of speed control system. It is used for the research and experiment of manipulator control system based on AI wearable acceleration sensor. The experimental results show that the average matching rate of the manipulator control system based on AI wearable acceleration sensor is as high as 88.89%, and the stability of the feature descriptor is high.

Deep representation for classification of refrigerator image via novel convolutional neural network

ABSTRACT Machine vision has played a vital role in household appliance assembly lines in recent decades. Being a practical manufacturing issue, the automatic identification of refrigerators from their front-view images captured from actual scenes is a potentially invaluable tool for industrial production automation. And a large number of approaches have been presented to classify the refrigerators according to their appearance. However, it remains a challenge since there are several hardships in this process. To bridge this gap, we propose an unsupervised convolutional neural network-based pipeline for recognizing the category of each input refrigerator image. By using multi-channel network architecture and a double convolutional operator, the proposed approach can utilize the information from intra-class and inter-class images, simultaneously. To evaluate the performance of this proposed method, we conducted comparison experiments between the state-of-the-art techniques and ours on 39,782 manually sampled images of refrigerators divided into 47 categories. The experimental results demonstrate that the proposed approach outperforms the methods presented in the literature with an accuracy of 99.97%.

Research on the Development Trend of Industrial Automation Control Technology Based on Big Data Analysis

Industrial production automation refers to the general term for measuring machinery and equipment or production process based on big data, information processing and process control without direct manual intervention according to big data. In the context of the gradual disappearance of China’s demographic dividend, supply-side structural reform has become an important focus of future policy intentions. Chinese manufacturing companies are gradually applying advanced technologies (such as human-computer interaction and intelligent machines) to the entire industrial production process to realize production automation and improve the overall productivity of industrial enterprises.

Dynamic Modeling and Cooperative Process Simulation in Cooperative Dual-Arm Robot Based on Adams

The demand of cooperation task of cooperative dual-arm robot in industrial automation production is increasing day by day. It is analyzed that the kinematics and dynamics mathematical model of the cooperative dual-arm robot with six degrees of freedom in one arm and the relationship is constructed between the arms’ position and the attitude for the robot in the paper. The mechanical characteristics of the cooperative tasks are analyzed that contains carrying, assembling and processing in the industrial field. The dynamic simulation software Adams is used for the simulation calculation and analysis. It is obtained that the position and attitude transformation characteristics of the forward kinematics solution through the simulation analysis, and the curve of the distribution force for the dual-arm in the process of cooperation is obtained. It provides the basis for setting up the reasonable driving force distribution and load distribution when realizing the task for dual-arm robot in cooperation.

Research on Automatic Calibration and Correction Method of Intelligent Turbine Flowmeter

In the process of industrial automation production and daily life, the flow is an important parameter that often needs to be measured and controlled. In order to ensure the accurate measurement of flow meter, the flowmeter needs to be calibrated before production, during use and after maintenance. However, most of the existing calibration methods are complicated in the calibration process, and there are many human interventions. The functions of the intelligent flowmeter are not fully utilized, and the unified management requirements for equipment during large-scale deployment cannot be met. This paper provides a method for automatic calibration of intelligent turbine flowmeter, which can be applied to a variety of intelligent instruments. In the calibration phase, the flowmeter automatically completes the calibration data collection at each calibration point set by the user. After all calibrations are completed, the calibration data will be stored in the flowmeter inside and it also will be uploaded to the host computer through the wireless network. At the same time, the configuration information can be received remotely. In the measurement phase, according to the internal calibration data and the K value fitting method selected by the user, the flowmeter can realize the non-linear correction of flow measurement and improve the accuracy of measurement results. User also can self-configure the instrument parameters. Compared with the existing calibration and correction methods of flowmeter, it simplifies the calibration process, has a high degree of automation, improves the accuracy of measurement results, and gives full play to the functions of intelligent instruments.

RETRACTED ARTICLE: Image processing algorithm of Hartmann method aberration automatic measurement system with tensor product model

Nowadays, the society has entered the digital information age, and the information contained in the image is far more than the sum of the information contained in other media. In the Internet industry, image processing technology can be used to quickly find the required picture information. Other applications include disaster prevention, industrial automation production lines, semiconductor, electronics, tobacco, and food industries. After the meter glyph spot image is collected, there are several spots in the image, and the corresponding pixel values are stored in memory. In order to process images, they should be distinguished and marked so that the spot has definite eigenvalues. To this end, this paper proposes an image processing method. Firstly, an image denoising method combining self-snake model and P-M model is introduced. Secondly, the recursive HOSVD dimensionality reduction algorithm based on tensor product model is used to further process the image. The center of the Hartmann aperture image is solved by the centroid of all the spots, and the center overlap algorithm for determining the centroid distance of the aperture image by the symmetry of the centroid of the spot centroid can reduce the number of calculations. The experimental results show that this method can effectively identify and process the spot of the image and greatly reduce the time complexity and computational complexity of the algorithm.