Intelligent Manufacturing Mode for Sophisticated Equipment Assembly Workshop

The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents, and the development of new productivity is bound to force enterprises and employees to make adaptive adjustments. The upgrading of intelligent manufacturing is not only the upgrading of intelligent machines but also the upgrading of the human brain, which includes the reshaping and cultivation of industrial talents. Based on field research, this study analyzes the different characteristics of the traditional and the new intelligent manufacturing model, as well as summarizes the characteristics of industrial talents and the changing trend of talent demand in view of the intelligent manufacturing model in Dongguan.

As the main tool for rock breaking, the bit with features of personalized and high efficiency is more and more popular in different strata, different well sections and different working conditions. By using Intelligent design and manufacturing technology, the optimization and manufacturing efficiency of bit are improved. The intelligent bit design and manufacturing technology includes three core elements: intelligent design, real-time drilling monitoring & risk identification, and post-drill evaluation. This technology has been applied in more than 10 wells in the Sichuan-Yunnan region, which produced the remarkable results. The bits’ manufacturing efficiency was increased by 800%, the footage and ROP were improved. At the meantime, the number of bits consumed in a single well has been greatly reduced. Next, the best optimization should be implemented in these respects, such as intelligent individualized bit design and application technology, big data analysis model, intelligent manufacturing and the image recognition technology, and then the application range can be extended to more regions.

In recent years, the deep integration of a new generation of information technology and manufacturing technology as an intelligent manufacturing model. It triggers a new round of manufacturing transformation. As an important carrier to realize intelligent manufacturing, smart factory realizes the intelligence of the production process by building intelligent production systems and networked distribution production facilities. It further improves production efficiency. However, the traditional communication system based on C/S architecture cannot realize the real-time transmission of data generated by much industrial equipment and sensor devices in the smart factory. This situation further leads to increased time delay and reduced data transmission efficiency for various types of data such as device status data, sensing data, audio, and video data, etc. The system is unable to monitor and manage each device in real-time. This paper proposes a distributed communication model for smart factories. According to the actual production requirements, we design the general architecture of the distributed communication model for smart factories. A message agent is used to implement the publish/subscribe mechanism of the distributed communication model, which has the advantages of low power consumption, security, reliability, and scalability. Meanwhile, a dynamic awareness scheduling algorithm based on WFQ (DAWFQ) is proposed. According to the length of the queue backlog, the algorithm dynamically adjusts the weights of real-time data streams and non-real-time data streams. It enables the model to send real-time data in priority and meet the demand for reliable transmission of real-time data. The experimental results show that the model designed in this paper can complete the distributed communication of the smart factory network. It shows good performance in terms of both real-time and reliability. The model ensures the real-time and reliable demand of smart factory devices under the constrained network resources and improves the quality of service of the whole communication network.

With the rapid development of computer network technology, machine tools are required to upgrade to intelligent and networked manufacturing units. The application of big data processing methods has become the core of the development of machine tools. In this paper, through the establishment of the overall architecture of intelligent machine tools, the sensing and acquisition system of intelligent machine tools is developed. The Data Collector module is developed based on MTConnect protocol to realize data transmission from agent to MySQL database, and data storage in the actual processing process is realized by using database technology. On this basis, the Back Propagation (BP) neural network algorithm is used to predict the power of the milling process. Finally, the accuracy and predictability of the BP neural network model are verified by experiments.

In the manufacturing industry, priority is given to quality models in the product, in order to make companies competitive, that is why it has been proposed to implement haptic interfaces capable of detecting anomalies in the operation of the electronic equipment of the companies. Cars in the manufacturing sector, that is why through the implementation of Artificial Intelligence and Smart Manufacturing Models, we can get to build intelligent systems for the correct decision making in the auto parts sector in Mexico. Intelligent manufacturing is a subset that employs various techniques of artificial intelligence and emerging technologies coupled with computer control and high levels of adaptability to adapt to changes in product improvement. Intelligent manufacturing is focused on taking advantage of advanced information technologies and even intelligent analysis of data and manufacturing via the Internet of things to allow flexibility in physical processes to address a dynamic market in each society and from a global perspective. There is more training related to the implementation of artificial intelligence of the workforce for such flexibility of adaptability of products and use of emerging technology instead of specific tasks as is usual in traditional manufacturing, and which requires a larger group of individuals for it.

With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems.

In the ever-evolving landscape of technological advancements and scientific innovations, the field of materials engineering and intelligent manufacturing stands as a beacon of progress and transformation. It is within this context that the 2024 International Conference on Materials Engineering and Intelligent Manufacturing (CMEIM 2024) has gathered scholars, researchers, engineers, and industry experts from across the globe to convene, collaborate, and share their groundbreaking insights and cutting-edge research. The CMEIM 2024 conference, held in Xiamen, China from October 18th to 20th, 2024, serves as a pivotal platform for discussing the latest trends, challenges, and opportunities in the realm of materials engineering and intelligent manufacturing. It is a testament to the relentless pursuit of excellence and the unwavering commitment to pushing the boundaries of knowledge and technology. The proceedings of this conference is a compilation of the finest research papers presented by the delegates. Each paper has undergone rigorous peer review to ensure its originality, technical accuracy, and relevance to the conference themes. The themes covered in the proceedings are diverse and comprehensive, encompassing a wide range of topics such as advanced materials, mechanical properties of materials, machine vision and intelligent inspection technology, manufacturing processes, intelligent systems, and sustainable production. The contributions in this paper volume not only reflect the depth and breadth of research being conducted in the field but also offer valuable insights into the future directions of materials engineering and intelligent manufacturing. They showcase the innovative thinking and groundbreaking work being done by researchers from different parts of the world, highlighting the global nature of scientific inquiry and technological innovation. The publication of this proceedings is a significant milestone in the journey of knowledge dissemination and academic exchange. It serves as a lasting legacy of the CMEIM 2024 conference, providing a valuable resource for researchers, educators, and practitioners in the field. We hope that the ideas and methods presented in this proceedings will inspire further research, foster collaborations, and contribute to the continued advancement of materials engineering and intelligent manufacturing. On behalf of the organizing committee, we extend our heartfelt gratitude to all the authors, reviewers, and participants who have made this conference a resounding success. Your contributions, dedication, and enthusiasm have been instrumental in shaping the CMEIM 2024 into a truly remarkable event. We also acknowledge the support and sponsorship of our partners and institutions, which have made the publication of the proceedings possible. As we look ahead, we are excited about the prospects of continued growth and development in the field of materials engineering and intelligent manufacturing. We believe that the insights and innovations presented in the proceedings will play a crucial role in shaping the future of this exciting and dynamic domain. List of Committee Member is available in this Pdf.

Now that intelligent manufacturing is developing rapidly and intelligent production is becoming more and more popular, welding is an indispensable link in production, and the inspection of intelligent welding quality is very important. Therefore, this paper proposes welding based on a genetic algorithm to optimize BP neural network. Quality inspection method. The model takes welding fault undercuts, pores, cracks, pits, uneven welding width, and non-fusion as the input parameters of the model. The simulation results show that using BP neural network as a diagnostic method will occasionally fall into the local optimum. The genetic algorithm is introduced to optimize the initial value and threshold of the BP neural network to avoid falling into the local optimum after optimization, and the calculation accuracy is higher. Compared with the 72% diagnosis rate of the BP neural network, the diagnosis rate of improved BP neural network can reach 89%.

Intelligent manufacturing is the future development direction of manufacturing industry. There are still some problems lurk in the traditional mode of production, such as “information island” and “network security”, which result in low productivity but high-cost production, and information leakage. With application of the new generation information technology in manufacturing field is expanded, the deep integration of information technology and manufacturing industry promotes the development of production towards intellectualization, networking, and service. Single mode of production is gradually replaced by the way of large-scale collaborative production, so the traditional industrial internet of things (IoT) and manufacturing architecture are not suitable for the needs of actual production any more. For instance, the great challenges of compatibility and coordination of interface protocols, huge data, cost, and safety. In order to solve these problems, a new model of industrial IoT with security mechanism used in intelligent workshop is proposed. Firstly, this article analyzes the current situation of manufacturing industry and information technology, a secure transmission model of industrial IoT was designed based on the new generation information technology. Secondly, combined with the structure of the IoT and the real production demands, a six-tier industrial IoT architecture based on the new generation information technology is proposed. Finally, the three dimensions of the intelligent manufacturing model are expanded according to the architecture of the IoT. Not only a six-tier intelligent manufacturing system framework based on the new generation of information technology is obtained, but also the network security mechanism is introduced. On one hand, it realizes the coordinated operation of industrial chain, supply chain, and value chain of production process, on the other hand, it also improves the networking, intellectualization, and service level of industrial production. What is more the system can also provide the reference model for the various application of IoT.

Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

With the deep integration of information technology and the manufacturing industry, intelligent manufacturing has become an essential means of transformation and upgrading for the wooden furniture industry. However, the current development status, technological advancements, and future trends of wooden furniture manufacturing have not been comprehensively analyzed. This study employs bibliometrics and visualization techniques to comprehensively review the literature on wooden furniture manufacturing, addressing three main questions: (1) What is the current stage of global wooden furniture intelligent manufacturing development? (2) What are the primary research focuses in global wooden furniture intelligent manufacturing? (3) What should be the future research priorities for global wooden furniture intelligent manufacturing? The results indicate that while scholars have recognized intelligent manufacturing as an effective approach to transforming and upgrading the wooden furniture industry, there remains significant room for development in applying advanced technologies, sustainable development, talent training, management, resource utilization, and market dynamics. Finally, this study proposes a full value chain intelligent manufacturing system for wooden furniture enterprises, encompassing “Intelligent Product Design”, “Intelligent Strategic Decision-making”, “Intelligent Technology Research and Development”, and “Intelligent Manufacturing”, which aims to promote the sustainable development of intelligent manufacturing in the global wooden furniture industry.

This study investigates the iterative convergences of neural network for prediction turning tool wear. For the smart manufacturing, the intelligent prediction systems have been gradually developing for processing of CNC machine tools. Recently, many artificial intelligent algorithms of machine learning have been widely applied for forecasting and decision making in intelligent manufacturing. In general, the cutting tool wear in manufacturing of CNC machine tool plays a major role for a high quality and an efficient operation, but it is very difficult to diagnose and prognoses the tool wear for tool life due to many cutting parameters. Therefore, the study investigates the iterative gradient convergences of backpropagation neural network (BNN) algorithm for prediction tool life with analytics of its convergence and stability. The estimative methods of iterative convergences include stochastic gradient descent (SGD), momentum, adaptive gradient (Adagrad), adaptive delta (Adadelta), and adaptive moment (ADAM) algorithms. In BNN prediction model, the data inputs are the cutting speed, feed rate, and total material removal volume and data output is tool wear measured from the microscope. Results showed that the tool wear curves at different cutting conditions can be predicted and trained using BNN model for intelligent manufacturing. In addition, the convergence of ADAM gradient for the tool wear in all cases is the best prediction for the BNN model. However, it is worth to notice that the momentum gradient is faster training speed to converge to a constant error at fewer iteration numbers.

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance.

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.