Automatic Production Line Research Articles

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

A line scanning monitoring method for conveyor belt deviation using point cloud

Abstract A precise conveyor belt deviation monitoring method using line array point cloud data is proposed and demonstrated, which can ensure the healthy running of the conveyor system. The point cloud data characterizing the surface of the conveyor belt is collected in a line scanning way. Then, using a unique soft extraction method that weighted fusing three key features (cross-sectional variation, belt’s horizontal width, and previous frame) to process this data, the edge information of the conveyor belt can be accurately and robustly identified in real-time. Furthermore, the point cloud processing mode enables a belt-segmented deviation analysis method based on a standard sequence query. This can accurately determine the offset value and deviation trend of the conveyor belt, thereby achieving early warning of deviation faults. Experimental results show that the belt edge identification precision can reach 0.3 mm, and an early warning can be provided at least 57 m before the occurrence of a belt deviation fault. This belt deviation monitoring method can be widely applied in various working environments, especially in harsh conditions like mines and ports. It also has potential applications in automated production lines within Industry 4.0.

Exploring the Effects of Automated Production Lines on Traditional Manufacturing Jobs

Exploring the Effects of Automated Production Lines on Traditional Manufacturing Jobs

Industrial Sustainable Development: The Development Trend of Programmable Logic Controller Technology

Programmable Logic Controllers (PLCs) are indispensable for current and future industrial development, especially in smart factories, smart home technology, automated production lines, and machinery manufacturing. This study presents the trends in PLC software and hardware development through a technology roadmap and offers relevant suggestions to help industries achieve sustainable development, enhance market competitiveness, and provide references for research. Through expert interviews and fuzzy Delphi analysis, this study points out that future PLC development needs to focus on editing interfaces, syntax, Central Processing Units, Memory Units, and Communication Modules. Specific recommendations include visualizing regional/global label settings and connection settings, adding Python, JAVA, LabVIEW, and Scratch syntax, improving instruction execution speed, expanding program and expansion capacities, and adopting dual-channel Ethernet and connections to external networks and wireless networks. Fuzzy hierarchical analysis shows that Communication Modules are the most important component, followed by Central Processing Units and syntax expansion, and, finally, program and expansion capacity enhancements. These suggestions aim to promote product innovation and social environment demand evaluation, enhance product competitiveness, and achieve sustainable development goals.

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.

Virtual Simulation and Experiment of Quality Inspection Robot Workstation

(1) Background: Quality inspection robots are widely used in automated production lines. However, the design cycle is long, iteration costs are high, and algorithm development is challenging. It is difficult to perform effective validation during the design phase. Applying virtual reality technology to simulate quality inspection robot workstations offers a new approach to addressing the issues. (2) Methods: The research creates a simulation platform for quality inspection robot workstations based on a virtual reality architecture. The platform creates an immersive quality inspection robot workstation operation interface and conducts testing of the inspection process, thereby validating the rationality of the quality inspection robot workstation design. Building upon this foundation, we conducted experimental comparisons of various defect detection algorithms. (3) Results: Compared to the traditional YOLOv7 algorithm, the improved YOLOv7 algorithm achieved an 18.1% increase in recognition precision. Experimental results demonstrate that the quality inspection robot workstation simulation platform can be applied to validating workstation design proposals. (4) Conclusions: It has a positive impact on reducing the research and development costs of quality inspection robot workstations and shortening the defect recognition algorithm development cycle.

Collision Avoidance and Give Way of Heterogeneous and Variable-Sized Multiple Mobile Robots Based on Glued Nodes

Multiple mobile robot systems (MMRSs) play an important role in workshops, storage and other scenarios. A complex MMRS can contain heterogeneous robots, and even the robot sizes are not fixed, making it more difficult to avoid collisions. As a widely studied method, the zone-controlled method is not accurate enough for a system with heterogeneous and variable-sized robots, and it increases the difficulty of applying MMRS. Based on the concept of glued nodes, this paper proposes a more accurate calculation method than the zone-controlled method to avoid collisions among heterogeneous and variable-sized robots. In order to improve the efficiency of calculating glued nodes, this paper proposes a time-to-space method, which has a constant complexity after the system runs long enough. In addition to collision avoidance, this paper also studies the problem of giving way of idle robots. This paper proposes a method which can detect those idle robots blocking the working robots and plan paths for the idle robots to find avoidance nodes to give way to the working robots. Simulation experiments are carried out based on a real automated production line scenario, and the experimental results prove the effectiveness and efficiency of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In a complex MMRS, the size of different robots can be different, and even the size of a robot is different when loaded and unloaded. One of the motivations of this paper is to avoid collisions in such a complex MMRS. A collision avoidance method is proposed to avoid collisions among robots by allocating nodes in real time. At the same time, this paper also addresses the problem of idle robots blocking working robots. If an idle robot blocks some working robots, an avoidance node will be found for the idle robot to give way to the working robots. The methods proposed in this paper can be applied to the roadmap of any structure. This means that in a scenario, regardless of the structure of roadmap and the different sizes of robots, the methods can be applied directly. The methods have been applied to many practical industrial projects in warehousing, manufacturing and other scenarios, and greatly reduces the difficulty of applying MMRS. In future research, there is a need to improve the efficiency of the methods so that they can be applied to a large-scale system.

Japanese Prefabricated Housing Manufacturers

Japanese prefabricated housing manufacturers have gained international recognition for their innovative approaches to the whole design process, ranging from initial design to innovative cutting-edge technologies, state-of-the-art automated production lines, meticulous workmanship, and mass customisation. In this entry, three manufacturers (Daiwa House, Sekisui House, and Misawa Homes) were selected as case studies for close examination. By studying these leading companies, researchers and industry professionals can gain valuable insights into best practices, challenges, and innovations within the Japanese prefabricated housing sector. The research methods involved a desktop study of available information on websites, articles, and reports, as well as undertaking two study tours on residential sustainable design in Japan in 2022 and 2023. These three manufacturers were discussed and compared with respect to their development trajectories, design customisation, research capabilities and technological advancements, sustainable initiatives and procurement, as well as their after-sale services. They have demonstrated their adaptability and flexibility in response to natural disasters and the transformation of the needs in society. They are all keen on reducing the environmental impacts of their work towards zero carbon emissions and a sustainable future.

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.

Bottleneck identification and transfer prediction for automated production lines based on FNN

Abstract In a re-entrant production system, the throughput of the whole system depends on the capacity of the bottleneck machine. In this study, a new definition of bottleneck is proposed for a precision forging blade shop. The reinforcement learning algorithm is used to optimize the production scheduling to determine the most suitable scheduling scheme, which lays the foundation for bottleneck identification. Subsequently, the bottleneck identification index system was established according to the optimization objective, and the bottleneck identification problem was transformed into a multi-attribute decision-making problem. Finally, a fuzzy neural network is used for training, and the basic scheduling examples of each flow shop are utilized for bottleneck identification and prediction to verify their effectiveness.

Application of Industrial Robots on Automated Production Lines in Machine Manufacturing

With the development of industrial robot technology, production has been automated, mechanized and intelligentized. This research can not only improve production conditions and labor efficiency, but also promote the smooth development of the production and manufacturing industry, thus promoting the improvement of China s overall production capacity and raising the level of production. The article first provides an overview of industrial robots, followed by a detailed discussion of the characteristics and development trend of industrial robots, and finally discusses the specific application of industrial robots in manufacturing automation production lines for further research and promotion in the future.

Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels

INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.&#x0D; OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.&#x0D; METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.&#x0D; RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.&#x0D; CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.

A Time-sensitive Traffic Scheduling Algorithm for Industrial Network of Power Grid

Industrial Internet communications in power scenarios are highly time-sensitive and require low network latency and high stability. Therefore, in order to ensure the smooth operation of the automated production line, we propose a time-sensitive traffic scheduling (TsTS) algorithm combined with SRv6 technology based on the application background of industrial and civil converged networks. The method is: through the previous hop node sensing of network congestion Link congestion, and then adjust the detour path for time-sensitive traffic through Dijkstra’s algorithm. Moreover, in order to effectively reduce the link overhead of SRH and prevent bandwidth waste caused by large IPv6 addresses, we also propose a node list compression algorithm for detour paths. The core method is to put only necessary nodes into SRH, that is, Sensitive traffic can be forwarded with the help of ordinary routing. Experiments show that this method performs well in terms of delay and link utilization.

Research on Online Measurement Method for Small Workpiece Circular Holes Based on Machine Vision

&lt;p&gt;In modern manufacturing, due to the narrow format, short size, and complex surface structure of small-sized workpieces, automatic detection of surface defects, dimensions, etc. on small-sized workpieces faces difficulties, making it difficult to meet the requirements of fully automated production lines. Especially, there is no good solution to the problem of high-precision automatic measurement of geometric features of circles and arcs on small-sized workpieces. This article takes the engine end cover circular hole thermal component as the research object, builds a hardware platform for image acquisition of small-sized workpieces based on machine vision, proposes an online size measurement method for small-sized circular holes, and realizes the measurement of the position size and aperture size of small-sized product shell workpieces. In order to achieve the above methods, this article builds a visual detection hardware system and describes a detailed machine vision hardware selection scheme. Then, the images collected by the system are preprocessed, including image denoising, image correction, and image feature extraction. Then, for the processed images, an improved Hough transform fitting method is used to obtain the online size of features. In order to visualize the results, the detection system and software are designed according to the actual application scenario. The overall scheme of this article is efficient and reasonable, and the detection result error is within a reasonable range.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Design and Optimization of Production Line Layout Using Material Flows

Businesses are constantly trying to improve their production by looking for bottlenecks to improve their market position. The introduction and innovation of automated production lines is necessary for both labor shortages and productivity and quality reasons. A combination of precision, fluidity, and speed, that is the basic definition of a production line. With the advent of new technologies, production lines have also begun to continuously speed up and innovate. Innovation is the subject of this paper, where the problem of designing a completely new layout for a new production line in the food industry has been addressed. The aim of this paper was to create a design for the optimal layout of the production line in preselected production areas. Optimal use of the space allocated for production is very important for every company today.

The Application of Industrial Robots in Automated Production Lines

Elaborate on the definition, structure, and characteristics of industrial robots, their application in automated production lines, and provide suggestions for balancing and optimizing industrial robots in automated production lines.

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

&lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Experimental investigation and theoretical modeling on Bernoulli gripper using water for supply power enhancement

Bernoulli gripper is a fluid-driven vacuum suction device, widely used in automated production lines, with suction force regarded as its key mechanical indicator. Previous design methods for optimizing the structure and parameters showed very limited enhancement in suction force due to the compressibility of air, which limits the supply pressure and supply power. Therefore, this study proposes an innovative design for replacing the fluid medium from compressible gas to incompressible liquid (e.g., water). It was experimentally validated that the gripper using water can increase the supply power several times and the suction force dozens of times compared with that using air. Then, the pressure distribution and flow field of the gripper were investigated experimentally, implying that the flow field exists in the water-film zone and the bubble zone, with radial lines of water flow of equal cross section formed in the bubble zone. Furthermore, a model of pressure distribution was established, including the water-film zone, the bubble zone, and the boundary between the two. The theoretical results were basically consistent with the experimental results. This study provides an innovative idea, theoretical model, and experimental data for the optimal design and application of the Bernoulli gripper.

Design of Data Monitoring System for Intelligent Production Line System Based on Digital Twin

&lt;p&gt;This article establishes a digital twin model for automated production lines. By establishing a more complete digital model, it achieves comprehensive reproduction of the production line, and then extracts and maps data from various production modules in the digital twin to ensure real-time data. In the software development phase, data analysis is conducted on the production and processing process to achieve prediction and management of production. Finally, based on the established digital twin, online simulation virtual debugging can be achieved when flexibly changing production goals, thereby saving production cycles and costs. Therefore, the digital twin platform established in this article has practical significance.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

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.