Field Of Intelligent Manufacturing Research Articles

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

Forecasting Emerging Technologies in Intelligent Machine Tools: A Novel Framework Based on Community Analysis

Having emerged as strategic focal points in industrial transformation and technological innovation, intelligent machine tools are pivotal in the field of intelligent manufacturing. Accurately forecasting emerging technologies within this domain is crucial for guiding intelligent manufacturing’s evolution and fostering rapid innovation. However, prevailing research methodologies exhibit limitations, often concentrating on popular topics at the expense of lesser-known yet significant areas, thereby impacting the accurate identification of research priorities. The complex, systemic, and interdisciplinary nature of intelligent machine tool technology challenges traditional research approaches, particularly in assessing technological maturity and intricate interactions. To overcome these challenges, we propose a novel framework that leverages technological communities for a comprehensive analysis. This approach clusters data into specific topics which are reflective of the technology system, facilitating detailed investigations within each area. By refining community analysis methods and integrating structural and interactive community features, our framework significantly improves the precision of emerging technology predictions. Our research not only validates the framework but also projects key emerging technologies in intelligent machine tools, offering valuable insights for business leaders and scholars alike.

DSCU-Net: MEMS Defect Detection Using Dense Skip-Connection U-Net

With the rapid development of intelligent manufacturing and electronic information technology, integrated circuits play a vital role in high-end chips. The semiconductor chip manufacturing process requires precise operation and strict control to ensure chip quality. The traditional manual visual inspection method has a high workforce cost and intense subjectivity and is accompanied by a high level of misdetection and leakage. Computer vision-based wafer defect detection technology is gaining popularity in the industry. However, previous methods still find it challenging to meet the production requirements regarding accuracy. To solve the problem, we propose a defect detection network based on a coding and decoding structure, Dense Skip-Connection U-Net (DSCU-Net), which optimizes the skip connection between the encoder and decoder and enhances the profound fusion of high-level semantics and low-level semantics to improve accuracy. To verify the effectiveness of DSCU-Net, we validate it in actual microelectromechanical systems (MEMS) data, and the results show that DSCU-Net reaches an optimal level. Therefore, the DSCU-Net proposed in this paper effectively solves the defect detection problem in semiconductor chip manufacturing. This method reduces workforce cost and subjectivity interference and improves inspection efficiency and accuracy. It will help to promote further development in the field of intelligent manufacturing and electronic information technology.

Application of cloud computing technology in enterprise resource management

The progress of intelligent manufacturing has made enterprise resource planning (ERP) an essential core technology in the field of intelligent manufacturing. In the process of technology development, enterprises have been looking for a more optimized ERP architecture, and cloud-based ERP is one of the practical and efficient solutions. However, for the application of cloud computing in ERP, the current summary analysis based on this field is not comprehensive enough. In this paper, a comprehensive examination of the real-world implementation of cloud computing in ERP is conducted. It delves into the practical utilization and transformation of service-oriented architecture (SOA) and Model View Controller (MVC) within the context of ERP, while also providing an overview of the merits and drawbacks of SOA technology. At the same time, it summarizes the concept of layering and the practical application of cloud computing in different layers for human resource management system of enterprises. In addition, this paper summarizes the practical applications and development prospects of cloud computing in enterprise resource management, such as resource sharing and virtualization. Finally, the paper summarizes the full text, and puts forward a summary and prospect of the combination and future development of cloud computing and enterprise resource management.

Research practice and development direction of intelligent manufacturing of presonalized traditional Chinese medicine preparations

In recent years, as people's living standards continue to improve, and the pace of life accelerates dramatically, the demand and quality of traditional Chinese medicine(TCM) services from patients continue to rise. As an essential supplement to the existing forms of TCM application, such as Chinese patent medicine, decoction, and formulated granules, presonalized TCM preparations is facing an increasing market demand. Currently, manual and semi-mechanized production are the primary production ways in presonalized TCM preparations. However, the production process control level is low, and digitalization and informatization need to be improved, which restricts the automated and intelligent development of presonalized TCM preparations. Presonalized TCM preparations faces a significant opportunity and challenge in integrating with intelligent manufacturing through research and development of intelligent equipment and core technology. This paper overviews the connotation and characteristics of intelligent manufacturing and summarizes the application of intelligent manufacturing technologies such as "Internet of things" "big data", and "artificial intelligence" in the TCM industry. Based on the innovative research and development model of "intelligent classification of TCM materials, intelligent decision making of prescription and process, and online control and intelligent production" of presonalized TCM preparations, the research practice and achievements from our research group in the field of intelligent manufacturing of presonalized TCM preparations are introduced. Ultimately, the paper proposes the direction for developing intelligent manufacturing of presonalized TCM preparations, which will provide a reference for the research and application of automation and intelligence of presonalized TCM preparations.

Modeling and balancing of parallel U-shaped assembly line based on improved genetic algorithms

Aiming at the improvement of the efficiency of the assembly line in the intelligent manufacturing field, a parallel U-shaped assembly line balancing problem (PUALBP) appears. In this paper, a mathematical model of the PUALBP-I is established, and an improved genetic algorithm (IGA) is innovatively designed based on the allocation strategy. Combined with 56 classic examples, the IGA is used to solve the mathematical models of SUAL and PUAL respectively, and the balance results and the balance effect evaluation indicators are obtained. The comparison with Parallel U-line Heuristic (PUH) shows that the results of PUAL are better than SUAL, and verifies that the IGA is effective. The results demonstrate that the IGA in calculating large-scale problems is superior to the small-scale problems, which provides a useful reference for solving PUALBP.

Applications and Challenges of Fieldbus Technology in Intelligent Manufacturing Systems

Abstract This study advances the field of intelligent manufacturing by integrating automation, information technology, and digitalization into a novel “five horizontal and three vertical” system architecture. We introduce an enhanced model-free adaptive control (MFAC) algorithm that utilizes a dynamic linearization method alongside pseudo-partial derivatives (PPD) for optimizing data transfer within intelligent manufacturing systems. Using CANoe software for simulation, our research demonstrates the improved MFAC algorithm’s superior bus message transmission efficiency by 0.0224 to 0.026 and a significant reduction in the network load factor by 0.2068. These advancements highlight the efficacy of fieldbus technology in reducing network congestion and enhancing real-time performance in intelligent manufacturing environments.

Unified seam tracking algorithm via three-point weld representation for autonomous robotic welding

Autonomous robotic welding based on real-time seam tracking has shown excellent prospects in the field of intelligent manufacturing. In previous research, real-time seam tracking requires teaching trajectories, which has significant limitations. Moreover, as different types of welds have different shapes, their geometric distribution also exists in various forms such as straight lines, planar curves, and spatial curves, which brings significant challenges to the visual recognition of welds. The use of existing methods for tracking specific types of welds has limited applications. Currently, the existing methods are carried out for tracking specific types of welds, resulting in limited application scope. To handle various types of weld joints, a unified tracking paradigm named the three-point seam tracking algorithm (TSTA) is proposed in this study. First, a feature description method using three feature points is established to define different weld joints. The feature points are employed to reconstruct the position and attitude of welds, which allows the method to track welds of arbitrary geometries. Subsequently, a recognition method for the feature points combining morphology extraction and kernel correlation filter (KCF) tracker is designed. Combined with their respective advantages, the algorithm can identify the three feature points accurately and robustly in strong-noise environments. Extensive experiments including actual welding were conducted to verify the superiority of the proposed TSTA. Eleven representative types of weld seams were tested and all of them could be tracked in real time without any prior information. Compared with similar studies, the proposed recognition algorithm currently achieves the highest accuracy and best robustness. The TSTA provides a complete solution in autonomous robotic welding for unknown welds and shows excellent application prospects.

Application of Computer Vision in Intelligent Manufacturing under the Background of 5G Wireless Communication and Industry 4.0

With the development of 5G wireless communications and the arrival of Industry 4.0, computer vision has further penetrated the manufacturing sector. This paper makes a comprehensive study on the field of intelligent manufacturing. By constructing a model analysis method, the current situation of computer vision intelligent manufacturing under the background of 5G wireless communication and industry 4.0 is comprehensively analyzed. It is found that today 's computer vision technology is developing rapidly, while the Industry 4.0 stage and 5G communication technology have helped the field of intelligent manufacturing. At the same time, the author deeply analyzes the characteristics of intelligent manufacturing enterprises supported by computer vision and finds that it has the problem of unbalanced distribution of enterprises. After that, the author demonstrates the application of computer vision re-intelligent manufacturing from multiple dimensions. The application of computational optimization methods provides more diverse and comprehensive algorithms for labor saving and resource utilization improvement. Computer vision technology drives and promotes the development of intelligent applications and promotes a more comprehensive development of intelligent manufacturing.

The Application of Machine Vision in Intelligent Manufacturing

Machine vision technology has a rapid development rate in recent year especially in intelligent manufacturing. The field of intelligent manufacturing will undergo a revolution. Machine vision technology is currently developing and becoming more sophisticated. The related hardware and software items are continuously improved. They increasingly became significant in the industrial production process. Machine vision can replace some human labor in intelligent manufacturing, which effectively increases production efficiency and product quality. As a result, it might help businesses achieve full production automation, quality control standardization, automation, and automation. This paper discussed the key technologies of machine vision technology used in intelligent manufacturing industry, as well as the application of machine vision in intelligent manufacturing. The Key technologies of machine vision include image acquisition, image segmentation, and image recognition. The current application of machine vision in intelligent mainly include manufacturing dimensional measurement, part inspection, and object localization.

Visual sensing and controlling of the keyhole in robotic plasma arc welding

With an advantage of “single-sided welding and double-sided forming,” plasma arc welding (PAW) has a great application potential in modern industrial production. The welding quality can be guaranteed by sensing and controlling of the keyhole. However, it is difficult to make an on-line observation on the back of base metal, and realize a dynamic registration of the visual sensor and welding torch. In this study, it has investigated the relationship between the welding condition and image feature of keyhole. Image processing is designed to obtain the weld pool image and conduct a template matching of the keyhole. The target feature of weld zone will be extracted and processed in real time. Besides, it has designed a digital controller for the welding robot and power source in this study and discussed control method to stabilize the keyhole and achieve good welding quality. Eventually, experiments are conducted to inspect the comprehensive performance of the welding control system with varying disturbance. This study is of important significance for the visual sensing and controlling of the keyhole in PAW. It will provide technical support for the weld quality control, and promote the development of welding technology based on machine vision in intelligent manufacturing field.

A pushing-grasping collaborative method based on deep Q-network algorithm in dual viewpoints

In the field of intelligent manufacturing, robot grasping and sorting is important content. However, there are some disadvantages in the traditional single-view-based manipulator grasping methods by using a 2D camera, where the efficiency and the accuracy of grasping are both low when facing the scene of stacking and occlusion for the reason that there is information missing by single-view 2D camera-based methods while acquiring scene information, and the methods of grasping only can’t change the difficult-to-grasp scene which is stack and occluded. Regarding the issue above, a pushing-grasping collaborative method based on the deep Q-network in dual viewpoints is proposed in this paper. This method in this paper adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, which solved the problem of lack of information missing. What’s more, it combines the pushing and grasping actions with the deep Q-network, which make it have the ability of active exploration, so that the trained manipulator can make the scenes less stacking and occlusion, and with the help of that, it can perform well in more complicated grasping scenes. In addition, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before.

Exploration of Intelligent Manufacturing Methods for Complex Products Driven by Multisource Data

In order to improve the multisource data-driven fusion effect in the intelligent manufacturing process of complex products, based on the proposed adaptive fog computing architecture, this paper takes into account the efficient processing of complex product intelligent manufacturing services within the framework and the rational utilization of fog computing layer resources to establish a fog computing resource scheduling model. Moreover, this paper proposes a fog computing architecture for intelligent manufacturing services for complex products. The architecture adopts a three-layer fog computing framework, which can reasonably provide three types of services in the field of intelligent manufacturing. In addition, this study combines experimental research to verify the intelligent model of this article and counts the experimental results. From the analysis of experimental data, it can be seen that the complex product intelligent manufacturing system based on multisource data driven proposed in this paper meets the data fusion requirements of complex product intelligent manufacturing.

Analysis of the application of industrial robots in intelligent manufacturing

In the context of the new era, industrial robots play an increasingly important role in the field of intelligent manufacturing, which not only boosts the high-quality development of China's economy, but also helps promote the optimization and upgrading of industrial structure and enhance the momentum of economic development. At present, industrial robots are widely used in intelligent manufacturing in welding operation, automobile manufacturing, automatic assembly and other aspects, and have formed a certain industrial scale and economic effect. Therefore, this article mainly from the industrial robot in the field of intelligent manufacturing development present situation, analyzes the industrial robot in the field of intelligent manufacturing applications, and put forward from the stick to establish and improve the personnel training mechanism, vigorously strengthen the technological innovation capability and upgrading product application performance and so on three aspects to explore the optimum path of industrial robots in the field of intelligent manufacturing, In order to promote the long-term development of industrial robots from the perspective of intelligent manufacturing in the future.

A Novel Fault Identification Method Driven by Knowledge and Data

In the field of intelligent manufacturing, fault identification is an effective way to improve product service by identifying the cause of failures. For addressing it, the Generalized Bayesian Network (GBN) model is extended based on the traditional Bayesian Network in this paper, which redefines the directed edges and probability parameters among nodes. Compared with Bayesian Network, the GBN model has the ability to simultaneously define causality and correlation of variables. In addition, the structure of network is not only based on statistical data but also driven by expert knowledge. In order to achieve the collaboration of data and knowledge while maintaining the consistency, a hierarchical collaborative framework is designed including the data layer and knowledge layer. Furthermore, a hierarchical multi-objective optimization algorithm, namely Hierarchical Non-dominated Sorting Genetic Algorithm II (HNSGA-II), is advanced to solve the proposed model. Finally, an industrial case study for fault cause identification targeting the product service helps illustrate all details.

Digital catch-up through resource orchestration: a case study of latecomers in intelligent manufacturing

ABSTRACT Digital technologies provide latecomer manufacturers with technological catch-up opportunities in emerging field of intelligent manufacturing (IM). Prior studies on IM mainly focused on firms from developed countries or digitally-savvy firms. However, latecomer firms that have inadequate resources should identify and leverage diverse digital-related resources to achieve digital catch-up in IM, and the research on their digital catch-up remains limited. Resource orchestration is a sound guiding theory that we adopt to examine this phenomenon. This study conducts an in-depth case study of the successful catch-up of a role-model firm in China and reveals a three-phase process model for the digital catch-up of latecomers in IM. Our findings contribute to existing literature on IM fields, latecomers’ catch-up phenomena, resource orchestration theory, as well as knowledge of technology analysis and strategic management. The model also can be practical guidance for latecomers to achieve digital catch-up step by step.

Image denoising based on BCOLTA: Dataset and study

Robot deburring is an effective method for improving the surface quality of the high-voltage copper contact. The first step of robot deburring is to acquire the burr images. We propose a new burr mathematical model and build a real burr image dataset for burr image denoising. In order to improve burr image denoising effects of the high-voltage copper contact, this study proposes an online burr image denoising algorithm, that is, block cosparsity overcomplete learning transform algorithm (BCOLTA). The penalty term and the condition number are affected by the burr parameter. The clustering and transform alternate minimisation algorithms are adopted to achieve lower computational cost and better denoising effect. In addition, BCOLTA also has a good adaptibility to inherent noise images, especially in Gaussian noise. Compared with other traditional and deep learning algorithms by no reference and full reference image quality assessment methods, BCOLTA has state-of-the-art denoising effects and computational complexity on dealing with burr images. This research will play an important role in the intelligent manufacturing field.

Transient analysis of a single server queueing system with infinite buffer

As a typical single server queueing system, computer integrated manufacturing system (CIMS) has been widely used in the field of intelligent manufacturing. However, how to derive its instantaneous index is still an important issue. This paper investigates the transient behavior of the CIMS with spectral method. By constructing an asymptotic system and analyzing the spectral distribution, we derive the explicit transient solution of the asymptotic system. Trotter–Kato theorem is used to prove that the transient solution of the CIMS is just the limitation of explicit transient solution of the asymptotic system. At the end of the paper, numerical examples are shown to illustrate the effectiveness of the proposed approximation.

Application Analysis of Infrared thermal imaging Technology in Intelligent Manufacturing Field

With the continuous development of science and technology, traditional manufacturing technology has also been developed. It has gradually developed in the direction of intelligent manufacturing technology through continuous integration with the advantages of machinery, energy, materials and other technologies. Intelligent manufacturing technology has been widely used by virtue of its own advantages such as high quality, high efficiency and energy saving. At present, in order to better improve the efficiency of intelligent manufacturing technology, relevant personnel have begun to apply infrared thermal imaging technology to the manufacturing field.