Research on Intelligent Bit Design and Manufacturing Technology

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

The incorporation of high‐precision intelligent manufacturing technologies into patterned metal halide perovskite production has attracted scientific and commercial interest because of advantages in terms of designable and controllable processes, multiscale and multimaterial construction strategies, and improved energy‐conversion performance. Here, the current developments in intelligent manufacturing technologies are reviewed for patterned and tailored perovskite structures. Six high‐precision approaches, namely, nanoimprint lithography, space‐confined growth, ink‐based direct printing, laser‐induced nucleation, modification using a laser, and high‐energy etching, are investigated and summarized. The focus of the review is on the basic principles of intelligent manufacturing methods, process optimization, and controllable perovskite microstructures. Additionally, the performance improvements of patterned and tailored perovskites and their devices, including photodetectors, solar cells, lasers, information storage and encryption devices, anticounterfeiting devices, and full‐color display applications, are discussed. The potential future applications of shaped perovskites fabricated via intelligent manufacturing technologies are promising.

When using graph theory for kinematic structure enumeration, the isomorphism identification of graphs is an important and complicated problem. The problem is known to be an NP-complete problem. This paper presents a mixed algorithm based on a mapping property, a genetic algorithm, and a simulated annealing algorithm for the isomorphism identification problem. A validity encoding scheme was developed by considering the mapping relationship between two graphs, and some reset measures for the crossover and mutation operators were developed based on the characteristics in which the encoding cell could not be reiterated. A simulated annealing algorithm was introduced into the mixed algorithm to prevent premature convergence in resolution, and some other measures were developed for improving the efficiency, based on the parameter selection. An example shows that the mixed algorithm is a valid algorithm for the isomorphism identification of kinematic structure graphs in mechanism design. It will be a reliable isomorphism identification algorithm for intelligent computer-aided design (CAD) and manufacturing (CAM).

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.

With the rapid development of intelligent manufacturing technology, the mechanical processing industry is facing an urgent need to transform from traditional modes to high efficiency, precision, and flexibility. However, there are problems in traditional processes such as the disconnect between standardization and intelligence, and limited local optimization efficiency. Therefore, this article focuses on the intelligent transformation and design of mechanical processing technology, and proposes an integrated solution that integrates standardized benchmarks and intelligent technology. This plan constructs a framework of “standardized foundation construction intelligent technology empowerment closed-loop collaborative optimization”, based on a standardized system, integrating industrial Internet of Things, machine learning, and digital twin technologies to achieve real-time data collection, dynamic parameter optimization, and virtual real collaborative simulation. Experimental verification shows that this scheme can significantly improve machining performance: surface roughness is reduced by 44%, machining cycle is shortened by 36%, and equipment failure warning accuracy reaches 95%. The research results confirm that by ensuring data quality and operational consistency through standardization, combined with intelligent technology to achieve dynamic control of the entire process, it can effectively break through the limitations of traditional processes and provide a feasible path for the intelligent upgrade of mechanical processing technology.

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.

ForewordThe 2018 International Conference on Manufacturing Technology, Materials and Chemical Engineering (MTMCE 2018), as an international forum aiming at presenting and exchanging technological advances and research results in the broad fields of Manufacturing Technology, Materials and Chemical Engineering, was held at the Jinguan Holiday Hotel during June 22-24, 2018 in Zhuhai, China. MTMCE 2018 was organized by Tongji University, Beijing Normal University and Anhui University of Technology. The conference brought together leading researchers, engineers and scientists in the domain of interest from around the world. During the conference, the participants shared their research results and discussed the development and research applications in these fields.The conference invited submission of research focusing on Manufacturing Technology, Materials and Chemical Engineering. Accepted papers after proper registration and presentation will be published into conference proceedings. Eventually, the total number of submitted papers, whose topics included Building Materials, Iron and Steel, and Ceramic, Chemical Materials Testing and Evaluation, Environmental Chemical Engineering, Biological Chemical and Technology, Intelligent Manufacturing and Information Technology, was about 600, among which 371 were accepted. All those accepted papers had already passed the peer-reviewing by 2-3 expert referees and been accepted for publication. We hope that those papers will not only provide the readers a broad overview of the latest advances but also provide the researchers a valuable summary and reference in these fields.List of Committees are available in this PDF.

This paper analyzes a series of problems which need to be solved for a long time in China’s fuze manufacturing industry, such as high resource consumption, poor economic benefit, low technology content, mostly in the low end of the industrial value chain, and points out that advanced manufacturing technology can promote the transformation and development of China’s fuze manufacturing industry. Based on the brief introduction of the concept, development history and key technologies of advanced manufacturing technology, this paper focuses on the application background, theoretical research and engineering progress of micro nano structure precision machining technology, intelligent additive manufacturing technology, intelligent manufacturing system and industrial internet. It is proposed that China should take “made in China 2025” as the traction, vigorously develop advanced manufacturing technology, and promote the sustainable, green and healthy development of China’s manufacturing industry.

In terms of the artificial intelligence (AI) application in intelligent manufacturing, this paper analyzes the system realization form of intelligent manufacturing, and further discusses the main role of AI technology in intelligent manufacturing. With aggregating and classifying typical application scenarios of AI in intelligent manufacturing, this paper sums up and puts forward the application map of AI in intelligent manufacturing and the generic technologies of AI in intelligent manufacturing. Based on the investigation results of AI applications in intelligent manufacturing, this paper shows the driving factors of enterprises to implement AI applications. Finally, this paper puts forward the standardization demands of AI in intelligent manufacturing.

In order to meet with the requirements of equipment network integration, flexible customized assembly, lean logistics management and control of assembly process, an intelligent manufacturing model of sophisticated equipment assembly workshop is proposed in this paper. By modeling the requirement of intelligent manufacturing mode in sophisticated equipment assembly workshop, an intelligent manufacturing technology architecture system and its operation mode are built, which include six layers as user layer, core functional layer, system integration layer, intelligent perception layer, resource layer and basic support layer. The key technologies such as the intelligent perception technology of assembly resources by using intelligent information interaction device based on IPv6 and the abnormal early warning method of critical assembly process based on BP neural network are studied. Finally, the analysis is illustrated in the case of a realistic application from a local manufacturing enterprise to highlight the proposal’s feasibility and applicability by applying the proposed intelligent manufacturing model.

In intelligent manufacturing and robotic technology, various sensors must be integrated with equipment. In addition to traditional sensors, stretchable sensors are particularly attractive for applications in robotics and wearable devices. In this study, a piezoresistive stretchable strain sensor based on laser-induced graphene (LIG) was proposed and developed. A three-dimensional, porous LIG structure fabricated from polyimide (PI) film using laser scanning was used as the sensing layer of the strain sensor. Two LIG pattern structures (parallel and vertical) were fabricated and integrated within the LIG strain sensors. Scanning electron microscopy, an X-ray energy dispersive spectrometer, and Raman scattering spectroscopy were used to examine the microstructure of the LIG sensing layer. The performance and strain sensing properties of the parallel and vertical stretchable LIG strain sensors were investigated in tensile tests. The relative resistance changes and the gauge factors of the parallel and vertical LIG strain sensors were quantified. The parallel strain sensor achieved a high gauge factor of 15.79 in the applied strain range of 10% to 20%. It also had high sensitivity, excellent repeatability, good durability, and fast response times during the tensile experiments. The developed LIG strain sensor can be used for the real-time monitoring of human motions such like finger bending, wrist bending, and throat swallowing.

At China Nineteenth National Congress of the Communist Party, General Secretary Xi Clearly Pointed out That We Should Accelerate the Development of China's Manufacturing Industry, Strive to Build China into an Advanced Manufacturing Powerhouse, and Further Promote the Integration of Information Technology and Industrial Manufacturing. Thus, China's Manufacturing Industry Will Be Featured with Smart Information, Informatization and Digitization. in This Article, the Basic Content of Artificial Intelligence Technology and Intelligent Manufacturing is Firstly Elaborated. on This Basis, the Specific Application of Artificial Intelligence Technology in Intelligent Manufacturing is Clarified. This Part Mainly Uses Machine Vision and 4d Printing Technology as Examples. the Main Purpose of This Article is to Accelerate the Development and Application of Intelligent Manufacturing Technology and Industry under the Guidance of Artificial Intelligence Technology.

In the realm of intelligent manufacturing, driven by the push towards smart factories and Industry 4.0, optimizing technology selection and sequencing is paramount for intelligent transformation. This complex decision-making process resembles a novel project selection and scheduling problem, characterized by Multiplicative Enhancement Effects (MEEs) where the collective benefits of multiple projects can exceed their individual contributions. Additionally, the uncertain duration of projects further adds complexity. Motivated by these practical challenges, a stochastic mixed-integer programming model that incorporates MEEs and uncertain delays is established. A conditional value-at-risk-based risk measure is integrated to control delay risk. The solution approach leverages an efficient branch and bound-based approach with cutting strategies, integrating a sample average approximation framework and a backward labeling algorithm to handle stochastic elements. A real-world technology implementation case study highlights the advantages of considering MEEs, uncovers myopic biases in technology selection, and identifies implementation patterns centred around core Industry 4.0 technologies. This research enhances our understanding of intelligent manufacturing decision-making, offering valuable insights and practical implications for technology-driven transformations.

Intelligent manufacturing technology, as the core driving force of the fourth industrial revolution, is profoundly changing the production mode and industrial pattern in the field of mechanical processing. This paper starts from the application background of intelligent manufacturing technology in the field of machining, combined with the limitations of traditional machinery manufacturing technology, systematically analyzes the application status of intelligent manufacturing technology in CNC production, equipment fault diagnosis, sensing technology and industrial robots, and provides theoretical support and practical guidance for the transformation and upgrading of machining industry. The exploration of the application path of intelligent manufacturing technology in the field of machining not only helps to enhance the core competitiveness of the industry but also provides important support for the realization of high-quality development and sustainable development goals of the manufacturing industry.

We take the Made in China 2025 policy as a quasi-natural experiment to empirically test the effect of Intelligent manufacturing (IM) on green technological innovation (GTI). The results show that: (1) IM policy promotes the quality and quantity of enterprise GTI; (2) IM guides enterprise GTI by increasing enterprise research investment and improving the level of green finance; (3) Enterprises with varied technology intensity and location, firm scale and property rights will respond to IM policies in diverse ways; (4) IM policy, China’s broadband and national big data pilot policies have synergistic effects on GTI.