Automatic Line Research Articles

Design and analysis of automatic assembly line for battery cell and reed pipe

Design and analysis of automatic assembly line for battery cell and reed pipe

Dynamic control of cardboard-blank picking by using reinforcement learning

Abstract Standard packaging lines with high output rates often struggle when dealing with uncertainties in the conditions of the handled materials. This paper focuses on a piece of machinery in an automatic packaging line, namely an automated apparatus that extracts cardboard blanks from a buffer and transfers them to the next section through suction cups. In this context, the success of the operation depends on various controllable parameters, disturbances, and time-dependent variables, whose mutual relationships are not easily identifiable and whose understanding has so far been entrusted to the experiential knowledge of human operators. Currently, drops in picking success rates require the machine to be stopped and operators to intervene on-site, making use of their expertise to identify the issue and recalibrate the machine. To address the problem, this paper presents an artificial-intelligence-enabled controller, capable of continuously and autonomously recalibrating the apparatus and compensating for disturbances, in order to avoid missed or incorrectly picked cardboard blanks. In particular, this work exploits experimental data to build a model of the system, on which a reinforcement-learning algorithm is trained. The controller is tasked with regulating the controllable parameters while monitoring process variables. The developed agent is tested on the real apparatus to assess its performance.

Design and testing of a low-cost mobile platform for contactless building surveying

After the completion of building construction, evaluating the accuracy of the distance between the lower edge of electrical switches and the one-meter line of the building is a crucial metric for assessing construction quality. Currently, the traditional measurement method basically uses manual measurement and is inefficient. To overcome this problem, we present a low-cost, non-contact mobile platform for building measurement, based on a 2D laser rangefinder and RGB camera. The system mainly consists of six key components: a mobile chassis, dual-axis gimbal, laser rangefinder, RGB camera, industrial control computer, and an automatic one-meter line calibration device, which can achieve high-precision distance measurement. Firstly, a sensorless control method for brushless DC motors (BLDCM) based on the Extended Kalman Filter (EKF) algorithm is proposed, which effectively improves the accuracy and robustness of rotor position estimation. Secondly, an automatic calibration device is developed to ensure the laser line consistently remains at a height of one meter from the ground. Finally, by combining image processing technology and 2D laser range data, the distance between the lower edge of the electrical switch and the building’s one-meter line is precisely measured using a triangulation algorithm. Experimental results show that the system’s measurements, compared to manual measurements, have a relative error percentage of 0.57%, while the automatic calibration device has a maximum error of only 0.2 centimeter. Therefore, the system shows great potential for widespread application in the field of building measurement.

A robotic fish processing line enhanced by machine learning

This paper presents the design of a comprehensive automatic fish processing line utilizing machine learning algorithms. The processing line encompasses several essential steps, including fish identification by type, fish sorting by size, fish orientation based on shape, and fish cutting at the optimal chopping points. The primary objective of this design is not just automation but also maximizing economic benefits by preserving the maximum amount of fish meat during the cutting process, achieved through the application of machine learning algorithms. To accomplish these goals, we employ a combination of transfer learning and convolutional neural networks to establish criteria for actions across all stages of automatic fish processing. At the heart of the processing station is a conveyor belt equipped with numerous sensors and lenses. Positioned along this conveyor belt are two robotic arms, responsible for precise positioning and cutting operations, all guided by the machine learning algorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.

Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line

Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB camera and a machine learning classifier. The paper is focused on protecting the sorting process against prohibited and dangerous items potentially present in the sorted material that pose a threat to the sorting process or the subsequent metallurgical process. To achieve this, a convolutional neural network classifier was applied under real-life conditions to detect forbidden elements in copper-based metal scrap. A laboratory stand simulating the working conditions in a high-speed scrap sorting line was prepared. Using this custom stand, training and test sets for machine learning were gathered and labeled. An image preprocessing algorithm was designed to increase the robustness of the resulting forbidden element detector system. The performance of multiple neural network architectures and data set augmentations was analyzed. The highest accuracy of 98.03% and F1-score of 97.16% were achieved with a DenseNet-based classifier. The results of this paper show the feasibility of using the presented solution on a high-speed industrial line.

Coupled optimization of task sequence and hoist scheduling for electroplating production lines based on an improved salp swarm algorithm

Automatic electroplating production lines have been widely used in electronics industries to reduce the labour intensity and improve the production efficiency. In the multi-variety and low-volume electroplating production, it is known that the task loading sequence and hoist scheduling are coupled with each other, and they codetermine the production efficiency, while all the existing scheduling methods consider them separately, and thus the optimal production schemes become unavailable. Therefore, this paper develops a Task sequence-Hoist scheduling Coupled Optimization (THCO) model which simultaneously considers the requirements and practical constrains of task sequence and hoist scheduling, having an optimization objective of minimizing the maximum completion time. For this model, a double-layer code is developed and an Improved Salp Swarm Algorithm (ISSA) is developed by introducing three improvement strategies: the random spare strategy which is used to increase the population diversity, the nonlinear adaptive weight strategy which is used to balance the exploration and exploitation capacities, and a golden sine algorithm which is used to improve the convergence rate. Experiments based on 23 benchmark functions are then conducted. The obtained results show that ISSA has better convergence and solving quality than existing algorithms. Furthermore, several production cases prove that THCO can generate production schemes that better meet the requirements of production lines.

Automatic Design Line Detection in Urban Building Images through DCT

The conversion of urban building images into design lines is a valuable technique used in architecture. It aids in theanalysis of design styles and building components. Recent studies have shown a significant interest in usingcomputational methods to examine the design lines in architectural images, such as plans, facades, and streetscapes.This research presents an automated approach that utilizes kernel filters to extract edges and reduce noise fromvarious architectural images. Additionally, it introduces a new filter that separates lines based on their directionsutilizing the Discrete Cosine Transform (DCT). The proposed technique generates distinct images that display thevertical, horizontal, and curved lines extracted from the original images. The results demonstrate that the proposedtechnique is efficient not only with architectural drawings but also with photographs of existing buildings. Thistechnique opens the door for further experimentation in artificial intelligence, computational aesthetics, imagerectification and calibration, and 3D building reconstruction.

Research on Forging Dimension Online Measuring System Based on Vibration Point Cloud Compensation

Mechanical vibration in the high-temperature forging production line often causes large forging thermal dimensional measurement error in the detection task, so a vibration point cloud compensation method based on an acceleration sensor is proposed in this study. First, the vibration signal is obtained through the built-in acceleration sensor in the laser camera. After the acceleration of the camera vibration is detected, the displacement of the camera in three directions is solved by secondary integration. Subsequently, the coordinate value of the corresponding point is obtained by the rotation matrix transformation so as to compensate and correct the point cloud deviation caused by the camera vibration. Finally, the forging point cloud is matched using the surface matching algorithm in Halcon. An automatic forging production line for wheel hubs has been built, and the key dimensions of high-temperature forging products have been measured online using the developed method. After the forging point cloud is compensated, the average measurement error of dimensions is reduced from ±0.9 mm to ±0.1 mm, and the standard deviation is reduced from 0.52 mm to 0.056 mm. Using the vibration point cloud compensation method based on the acceleration sensor, as well as using silica aerogel insulation, vibration structural parts, heat insulation and constant temperature, a blue-violet 3D laser camera, and other measures, the dimensional detection accuracy of high-temperature forgings in the forging production line can be improved, and the instability of dimensional detection can be reduced.

Fuzzy decision-making model for automatic mail sorting line

The object of this research is the implementation of fuzzy control in an automated mail sorting line. To decrease the processing time of postal items, automated sorting lines with conveyor belts are used, which carry out transportation to predefined loading points. The principal limitation of such systems is the decision-making model's inherent ambiguity when implementing the designated sorting logic. To overcome these shortcomings, a fuzzy decision-making model for automated sorting lines has been developed. This model allows implementing the sorting logic of postal items to specified loading points under predefined conditions. For this, the fuzzy classification of objects based on their parameters has been formalized, with loading points determined for each class. The fuzzy inference process stages have been formalized based on the Takagi-Sugeno-Kanga algorithm. The input and output fuzzy linguistic variables have been identified, and a rulebase for fuzzy inference has been formalized for fuzzification. For defuzzification, the center of gravity calculation for singletons has been chosen. Three criteria have been defined to implement the sorting logic, facilitated by the fuzzy inference rulebase. The first criterion sorts items to loading points considering cost evaluation, irrespective of weight and dimensions. The second criterion considers weight evaluation independent of dimensions. The third one takes into account the combined evaluation of weight and dimensions. The developed model was tested using the Matlab environment. The simulation confirmed the model's functionality and the sorting management criteria for postal items to loading points.

POSSIBLE SOLUTIONS TO THE PROBLEM OF PROVIDING IMPORTED EQUIPMENT OF BREWERIES IN RUSSIA WITH SPARE PARTS AND COMPONENTS

In the context of Western sanctions pressure and current bans on the import of machinery and components in the Russian brewing industry, a problem arose with the supply of new production equipment, original spare parts and components, industrial service on imported equipment, which is largely equipped with large and medium-sized enterprises, due to the inability to find suppliers of the required original spare parts in Russia, components and materials. The article provides an express analysis of possible solutions to this problem in order to determine the strategy of survival or development of the enterprise. The methodological basis is the traditional methods of technical, economic and logical analysis, systematization and ranking. The advantages and disadvantages of three options are considered: import substitution of the brewery's fixed assets by gradually replacing imported units, semi-automatic and automatic lines with domestic analogues with financial support from regional governments; parallel import of spare parts and components, supported by the restoration of worn-out parts of technological equipment of breweries using the services of specialized reverse engineering firms; organization of a specialized company as part of the Rosspetsmash Association, carrying out reverse-engineering, manufacturing of spare parts and assemblies for imported equipment of all breweries in the industry and industrial service of imported equipment based on a specific repair specialization. Based on the express analysis, it was suggested that the choice of a survival or development strategy for a particular plant depends on the condition of the equipment, the availability of an enterprise development strategy, the availability of its own finances and the support of state authorities.

ALGORITHM FOR CALCULATING THE COEFFICIENT OF LABOR PARTICIPATION OF OPERATORS OF LINES, MACHINES AND PLANTS USING THE 1C:ENTERPRISE SYSTEM

The purpose of the study was to improve the remuneration system for operators of automatic lines, machines and plants by using the labor participation rate, which will allow making optimal management decisions on accounting for payroll settlements with staff and the achieving of strategic goals of the organization. The article suggests the ways to solve the problem using the example of improving the system of awarding bonuses to the staff of a manufacturing enterprise, it is recommended to use formulas that allow evaluating the productivity of an individual employee. Software implementation in the 1C:Enterprise system is performed. The developed software solution allows you to automate the process of calculating bonuses for operators of lines, machines and plants, taking into account the amount of work each employee does. The proposed algorithm for calculating the labor participation coefficient is supposed to be developed in further studies to more accurately assess the contribution of an employee's labor, for example, by taking into account defects and evaluating the quality of manufactured products. Purpose. Development of an algorithm for calculating the coefficient of labor participation of operators of lines, machines and plants using the 1C:Enterprise system. Methodology: in article methods for formalizing and specifying formulas used in calculating the labor participation rate, and also development methods on the 1C:Enterprise platform were used. Results. The software implementation of calculating the premiums of operators of lines, machines and plants taking into account the labor participation coefficient is performed. Practical implications it is expedient to apply the received results the manufacturing enterprises staffed by operators of automatic or semi-automatic lines, machines and plants.

An Improved SSD Model for Small Size Work-pieces Recognition in Automatic Production Line

<p>Aiming at the problems of slow recognition speed and low recognition accuracy of arbitrarily placed workpiece by machine vision in traditional automated production lines, a workpiece recognition algorithm based on improved SSD is proposed. Firstly, the improved DarkNet53 is used to replace the backbone network in the original SSD network framework, and the network enhancement is used in the backbone network to solve the defect of small target missed detection. Then, channel attention module and deep semantic feature fusion module are added, in order to improve the recognition ability and detection accuracy of the small target features. Lastly, the loss function was optimized, and the problem caused by sample imbalance was solved by changing the weight distribution of positive and negative samples. In the experiment, image datasets of typical bolts, nuts, and connecting plates were constructed for the network training, the experimental results showed that, the recognition accuracy and speed have been optimized and meet the requirements of automatic work-piece detection in actual production, compared with traditional YOLOv4 and the original SSD algorithm in the work-piece recognition task.</p> <p> </p>

Automatic Coating Line for Plastic Parts

Automatic Coating Line for Plastic Parts

Design of an Intelligent Feeding System

The article elaborates on the design method of an intelligent feeding system . The feeding system is an important part of the automatic production line, which provides raw materials for other units of the automatic production line. It is controlled by PLC, which is described from many aspects, such as structure design, pneumatic circuit design, control circuit design and PLC program design. It analyzes the design idea and principle of intelligent feeding system in detail, and provides an effective reference for the design of automatic production line.

Production automation upgrades and the mystery of workers' overwork: Evidence from a manufacturing employer-employee matching survey in China

The automatic production line will alter the workflow and workshop production system, which will significantly impact how the workers perform their jobs. Using employer-employee matching survey data from the manufacturing sector in Guangdong Province, this paper found that production automation upgrades significantly increase working hours. The findings remain robust even after dealing with endogeneity issues. A plausible explanation is that improved general-purpose technology integrated into the production system will damage employees' unique talents by "deskilling". The heterogeneity analysis shows that labor degradation caused by technological change can be effectively mitigated by labor union protection and the accumulation of workers' human capital. Furthermore, production automation upgrades will probably change the way wages are paid, with less use of the piece-rate system. Although workers may receive some wage compensation for overwork, production automation upgrades may have unfavorable consequences (such as mental health, etc.). This study sheds light on how shop floor workers perform their jobs in the face of the wave of automation, offering fresh policy insights for promoting decent work and achieving inclusive growth.

Identification of fruit using a flexible tactile sensor array

This article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit recognition method based on a flexible tactile sensor array is described. This method enables the robot to directly perceive the attributes of the objects and identify fruits using a flexible gripper, facilitating intelligent sorting. A novel flexible tactile sensor array is utilized to construct a flexible hand tactile information acquisition platform, which collects tactile time series data for the fruits. Principal component analysis is then employed for dimensionality reduction, followed by the development of an improved particle swarm optimization for the support vector machine model. Through an experimental study, the optimized model is compared with four other models, demonstrating better classification performance. The optimized model achieves an average tactile classification accuracy of up to 98.10% for the five types of fruits. A comparison between the improved optimization algorithm, genetic algorithm, and grid search algorithm reveals the superior optimization performance of the new approach. In the future, this method is expected to be implemented in industrial sorting robots for intelligent sorting on automatic production lines. Furthermore, the algorithm will be further refined to enhance the classification accuracy and efficiency.

Application of a Digital Twin Model in the Packaging Process of the Panel Furniture Industry

Abstract The packaging process in panel furniture manufacturing enterprises is the final process in production. However, there exists a lack of effective interaction and integration between the manufacturing processes and the information system. This challenge becomes more significant in the context of intelligent manufacturing. To address this, we propose a generalized digital twin workshop model for the packaging process in panel furniture manufacturing enterprises. Using this model, we establish a digital twin workshop for Company W’s packaging process, which improves interaction and integration with the information system. By analyzing the data from the workshop service system and the workshop twin data, we identify inefficiencies in the automatic packaging line and propose an optimization scheme using the digital twin workshop model. Through simulation in the virtual workshop and actual verification, we demonstrate that the proposed scheme improves the production efficiency of the automatic packaging line by approximately 20 percent and reduces the need for 5 to 6 packaging workers. Our study presents a new solution for upgrading the information system in the packaging process of panel furniture, contributing to the development and optimization of the furniture industry in the era of intelligent manufacturing.

Development of steel with specified properties for processing on an automatic line

Development of steel with specified properties for processing on an automatic line

Application Research on an Automatic Painting Production Line for the Formation of Locks

Application Research on an Automatic Painting Production Line for the Formation of Locks

In-line measuring and feature similarity-based group and pairing assembly for metallic BPPs

Assembly of proton exchange membrane (PEM) fuel cell stacks with metallic bipolar plates (BPPs) is a process of high precision stacking for hundreds of individual sheet components. The accuracy of stacking process and the uniformity of contact status for layers effect the working efficiency of the stack. The manufacturing of metallic BPPs consists of several processes including pressing, welding and coating, in which each process will have an impact on the final shape of BPPs. Especially for large-scale metallic BPPs, which are critical parts for manufacturing high power fuel cell stack, the shape deviation will be magnified. In this research, a batch of normal size metallic BPPs are investigated as preliminary study for large-scale BPPs. They are measured by a 3D laser scanner and their geometric deviation are analyzed. An approach of in-line measuring integrated automatic assembly line for BPPs and a method of feature similarity-based group and pairing assembly are proposed with which the uncertainty of stacking result with random placing order of fuel cell components is expected to be reduced without changing previous manufacturing processes. The scanned features are compared to the nominal model of BPP and then fitted into surfaces to visualize the individual deviations. K-medoids algorithm is used to cluster these BPPs into groups. The result shows feasibility of applying this method to large-scale BPPs in future work.