Traditional Manufacturing Industry Research Articles

Application of Smart Polymer Materialsarts

With the wide application of polymer materials, the performance of ordinary polymer materials gradually cannot meet the shortcomings of use in special environments, such as limited strength, insufficient wear resistance, poor stability and so on. And can not adjust and repair according to changes in the environment. Therefore, the demand for intelligent polymer materials has gradually expanded, and has been applied to a variety of fields after research. This paper introduces the information and application of three kinds of intelligent polymer materials: targeting, shape memory and self-healing. Targeting is mainly used in cancer or other diseases that require precise drug delivery; The characteristics of shape memory can be applied to plaster and surgical sutures in the medical field, and can also be applied to the performance upgrade of traditional manufacturing industry. Self-healing can be used to improve the service life of composite materials by using different methods according to the requirements of different material use scenarios.

중국 4대 직할시 온라인 플랫폼 창업자의 창업동기, 개인역량, 창업환경요인이 창업행동에 미치는 영향 : 사업기회포착의 매개효과를 중심으로

With the technological advancements of the Fourth Industrial Revolution, traditional manufacturing industries are transitioning to platform entrepreneurship utilizing AI and robotics technology. This shift contributes to national economic growth and job creation, aligning with the “Mass Entrepreneurship and Innovation” initiative advocated by Chinese Premier Li Keqiang in 2015, which drives the development of the platform economy. This study empirically analyzes the impact of entrepreneurial motivation, individual competencies, and entrepreneurial environmental factors on entrepreneurial behavior, with a mediating effect of business opportunity recognition, based on data collected from 515 online platform entrepreneurs in China’s four major municipalities. Using SPSS V. 24.0 and Process Macro 4.0 for analysis, the results indicate that economic and non-economic motivations, interpersonal competencies, digital utilization competencies, entrepreneurial education, financial status evaluation, and entrepreneurial support programs positively influence entrepreneurial behavior, with business opportunity recognition playing a crucial mediating role. Among these factors, financial status evaluation emerged as the most significant variable affecting entrepreneurial behavior. This finding demonstrates that an entrepreneur's ability to identify and evaluate business opportunities significantly impacts their entrepreneurial preparation and execution. Conversely, problem-solving competencies showed no direct effect on entrepreneurial behavior nor a mediating effect through business opportunity recognition. The traditional Chinese culture emphasizing guanxi (relationships) highlights the greater importance of interpersonal competencies over problem-solving or digital utilization competencies in business success. Therefore, to promote online platform entrepreneurship, it is essential to provide systematic financial education and continuous entrepreneurial support, enable networking and interpersonal skill‘s development programs, and offer practical support measures considering the impact of entrepreneurial environmental factors such as financial status evaluation on entrepreneurial behavior.

Overcoming the pitfalls of economies-of-scale in shared accommodation: How can effective multi-listing management enhance the sustainability of homestay businesses?

Economies-of-scale have always been a significant concern, but previous theoretical studies have primarily focused on the traditional manufacturing industry. Consequently, their findings are not directly applicable to the tertiary sector, particularly the hospitality service industry. The hospitality service operates in a high-risk environment and is susceptible to market exits. Therefore, it becomes even more crucial to effectively leverage economies of scale for improving survival rates and long-term operations. This study aims to explore the effectiveness of economies of scale theory in the hospitality industry by examining how sharing accommodation’s economies-of-scale impact personalized and professionalized homestay businesses’ survival. To achieve this objective, we analyze a panel dataset comprising 551,605 properties located in New York City, Los Angeles, Chicago, and Austin using panel survival analysis techniques such as the Cox model and the exponential model. The findings indicate that economies-of-scale can confer short-term survival advantages to professional homestay businesses in comparison with personalized ones. However, in the long run, the social benefits of personalized homestay businesses surpass the impacts of economies-of-scale, thereby enabling them to achieve better sustainability than professional homestay businesses. The likelihood of survival for professional homestay businesses increases with a greater number of listings in operation, referred to as the advantage of economies-of-scale on professionalization; conversely, this is not true for personalized homestay businesses. Moreover, effective host–guest communication both facilitates the attainment of benefits for personalized businesses and amplifies its impact on professionalized enterprises; high-quality products offered by personalized hosts can derive more advantages from scales compared to low-quality products, while superhost-certified professionalized hosts experience less business benefit than uncertificated hosts. This study is one of the pioneering efforts to investigate how economies-of-scale can contribute to the professionalization and personalization of homestay properties, thereby enhancing their business survival. It significantly contributes to the existing literature on hospitality business survival and expands upon the economies-of-scale theory by elucidating its advantages and potential pitfalls in relation to homestay businesses.

Theoretical analysis and technical application of mechanical intelligent manufacturing based on system digital-driven technology

With the accelerating process of global industrialization, the automated fabrication industry, as an important part of the industry, is also developing rapidly. Due to the low efficiency and high resource consumption of traditional manufacturing, it is more important to improve the production efficiency of conventional manufacturing and realize the resource-saving development mode. Due to the characteristics of system digital-driven technology, it can promote the intelligent development of the manufacturing industry. Therefore, the deep integration of system digital-driven technology and manufacturing is conducive to the intelligent promotion of manufacturing. Comparing the difference between the traditional manufacturing industry and the intelligent manufacturing industry, the application potential of system digitalization technology in the manufacturing field is analyzed. Based on the analysis of the theoretical basis of the system digital-driven technology and the application of the system digital technology in the main fields, the importance of the technology for the improvement of the accuracy index in the manufacturing process is emphatically analyzed. The maximum increase can be 5.3%, and the minimum increase can be 3.3%. It can be seen that the deep integration of digital-driven technology and manufacturing can not only improve the intelligent level of manufacturing, realize the intensive development of the industry, but also realize the data sharing of the entire industrial chain.

Enhancing SMEs digital transformation through machine learning: A framework for adaptive quality prediction

As smart manufacturing expands, businesses see the importance of digital transformation, especially for small and medium-sized enterprises (SMEs). Unlike larger companies, SMEs face greater challenges when undergoing digital transformation due to technological questions. However, recent advancements in high-performance computing and reduced hardware costs have made deep learning-based digital transformation more financially feasible for SMEs. While previous research utilized machine learning for product quality prediction, there remains a lack of comprehensive in adaptive quality prediction specifically designed for SMEs. This study presents a systematic framework utilizing various machine learning methods and validates research cases using CRISP-DM (Cross-Industry Standard Process for Data Mining). The first step involves applying XGBoost(eXtreme Gradient Boosting)for feature selection, the second step utilizes GRU for parameter prediction. Finally, in the third step, SVM (Support Vector Machine) is employed for quality classification. The integrated framework achieves high accuracy, with R2reaching 90 % for predicted parameters and nearly 95 % for classification indicators. Moreover, this research addresses the research gap in quality prediction and adaptability and provide an effective digital transformation solution for SMEs without substantial investment. The proposed research framework can be applied SMEs of other domains, such as the machining and traditional manufacturing industry.

Monocular visual anti-collision method based on residual mixed attention for storage and retrieval machines

In the traditional manufacturing industry, the safe operation of storage and retrieval (S/R) machines is vital for efficient automated warehouse management. Recently, deep learning-based monocular vision approaches have emerged as a promising solution for collision avoidance in automated storage and retrieval systems (AS/RS). However, the scarcity of specific datasets and the need for high-performance, lightweight neural network models pose challenges in the AS/RS domain. To address these challenges, this paper introduces a novel workflow for implementing a monocular anti-collision system for S/R machines. A semi-supervised annotation method leveraging a pretrained clustering model is proposed to expedite image collection and classification, resulting in a tailored dataset. Furthermore, the NightMix technique is introduced, combining Grayscale Enhancement and Floating Grid Mix method to enhance dataset diversity and capacity. Notably, a residual attention awareness module is integrated into MobileNet V3 to enhance its feature processing capabilities. Finally, the proposed approaches are validated through extensive experiments involving various design choices. The experimental results demonstrate that our methods are not only computationally efficient but also highly competitive in terms of learning performance.

The Impact of the Digital Economy on Supply Chain Security: Evidence from China’s Wooden Furniture Industry

Supply chain security is a major prerequisite for China’s successful industrial modernization, while the digital economy has significantly contributed to industrial transformation and upgrading. This study considers China’s wooden furniture industry as its research object, constructing an evaluation index system of the digital economy and supply chain security of the wooden furniture industry. Then, it studies the impact of the digital economy on supply chain security through theoretical analysis and empirical methods using the two-way fixed model of provinces and time. The findings demonstrate that the digital economy effectively enhances the level of supply chain security in China’s wooden furniture industry, further validating the digital economy’s positive externality impact on the traditional real economy. The impact mechanism test shows that inventory turnover capacity is the focal point for the digital economy to improve the supply chain security of the wood furniture industry, specifying the starting point for that industry’s digital transformation. The heterogeneity findings show that the role of the digital economy in improving the wood furniture industry’s level of supply chain security is more significant in inland areas than in coastal areas. Additional analyses found a threshold effect of the digital economy’s impact on supply chain security, indicating its limitations. This study explores the impact of the digital economy on the real economy from a traditional manufacturing industry, enriching research on the positive externalities of the digital economy as well as providing a reference for traditional manufacturing industries, such as that of wooden furniture, to probe the embedding points of the digital economy and appropriate digital transformation.

Application of process analysis technology in traditional Chinese medicine manufacturing industry

In the traditional Chinese medicine(TCM) manufacturing industry, quality control determines the safety, effectiveness, and quality stability of the final product. The traditional quality control method generally carries out sampling off-line testing of drugs after the end of the batch production, which is incomprehensive, and it fails to find the problems in the production process in time. Process analysis technology(PAT) uses process testing, mathematical modeling, data analysis, and other technologies to collect, analyze, feedback, control, and continuously improve the critical quality attributes(CQA) in all aspects of the production of TCM preparations in real time. The application of PAT in the TCM manufacturing industry is one of the research hotspots in recent years, which has the advantages of real-time, systematic, non-destructive, green, and rapid detection for the production quality control of TCM preparations. It can effectively ensure the stability of the quality of TCM preparations, improve production efficiency, and play a key role in the study of the quantity and quality transfer law of TCM. Commonly used PAT includes near-infrared spectroscopy, Raman spectroscopy, online microwave, etc. In addition, the establishment of an online detection model by PAT is the key basic work to realize intelligent manufacturing in TCM production. Obtaining real-time online detection data through PAT and establishing a closed-loop control model on this basis are a key common technical difficulty in the industry. This paper adopted systematic literature analysis to summarize the relevant Chinese and foreign literature, policies and regulations, and production applications, and it introduced the development trend and practical application of PAT, so as to provide references for accelerating the application of PAT in the TCM manufacturing industry, the intelligent transformation and upgrading, and high-quality development of the TCM industry.

A tripartite evolutionary game study of low-carbon innovation system from the perspective of dynamic subsidies and taxes

Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises.

Application of Machine Learning and Data Mining in Manufacturing Industry

With the rise of machine learning in various industries, the traditional manufacturing industry is facing a new disruption, which requires the use of different technologies and tools to achieve its production targets; In this regard, machine learning (ML) and data mining (DM) play a key role. This paper provides a statistical understanding of the main methods and algorithms used to improve manufacturing processes over the past 20 years by dividing them into four main themes: Scheduling, Monitoring, Quality and Failure, presents previous ML research and the latest advances in manufacturing, followed by a comprehensive discussion of existing problem solutions in manufacturing from multiple aspects, It includes tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machines, neural networks), learning types (i.e., ensemble learning, deep learning), and performance indicators (i.e., accuracy, mean absolute error). In addition, the main steps of database knowledge discovery (KDD) process that should be followed in manufacturing applications are described in detail, and the methods to overcome some problems and the advantages of machine learning applied to manufacturing industry are briefly described. Finally, the paper summarizes and further looks forward to the future development direction.

A mechanism for upgrading the global value chain of China's wood industries based on sustainable green growth

With climate change and increasingly fierce international trade competition, upgrading the global value chains (GVCs) under the sustainable development mode is becoming a key transformation for China's wood industry. This study applies system dynamics theory to build a model of upgrading and evolution of the GVC of the wood industry based on sustainable green growth and uses Vensim PLE software for system simulation. The results demonstrate that (1) under the framework of sustainable green growth, the optimization of industry structure and security of resource supply play an important role in promoting the position of the GVC. (2) improving technological innovation capacity, plantation forest stock, carbon emission reduction intensity, and wage growth is essential to upgrade the wood industry's position in the GVC. (3) different policy implementations have different impacts on the coupled and coordinated development of the system, and implementing innovation-driven policies is the optimal realization path to promote the synergistic upgrading of the GVC. The findings enrich the holistic and systematic study of the evolution of GVC upgrading in traditional manufacturing industries and provide a fresh perspective for assisting the wood industry in realizing GVC upgrading under a sustainable green growth model.

Literature Review on Carbon Emission Reduction in China's Traditional Manufacturing Industry and Research on The Driving Mechanism of Its Green Transformation

At present, "double carbon" has become one of the most popular social topics. Realizing carbon neutrality and carbon peak is a solemn commitment made by our country. As an extremely important part of China's industrial system, the success of the green transformation of the traditional manufacturing industry also determines whether China's "double-carbon" goal can be successfully implemented. This paper focuses on the Chinese traditional manufacturing carbon emission reduction related literature research, through the relevant literature integration of the modern traditional manufacturing carbon reduction research direction and focus, based on the PSR model to Chinese traditional manufacturing green transformation power analysis, from the perspective of system driven, to establish green transformation power analysis framework, from the system level to explain the dynamic mechanism of traditional manufacturing green transformation.

Product quality recognition and its industrial application based on lightweight machine learning

Quality management plays a crucial role in manufacturing industry as it directly impacts product quality and customer satisfaction. Traditional manufacturing industries have begun incorporating deep learning models to manage production quality. However, existing studies often focus on algorithmic models that require high computing power, while practical industrial applications often suffer from a lack of labelled data. This article proposes a novel and lightweight machine-learning-based quality detection model to address this issue. The proposed model tackles quality detection by efficiently detecting the local maxima of the scale space in input images using a Hessian matrix detection method, eliminating the need for continuous computation of multilayer Gaussian difference images. Additionally, image processing techniques are employed to augment the training data, enabling the model to achieve high accuracy even with limited datasets. Experimental results demonstrate that the proposed model outperforms alternative methods in terms of both accuracy and efficiency.

Quality control of manufacturing enterprises based on computer and big data technology

AbstractThe traditional manufacturing industry is facing the impact of Big Data, and all aspects of product research and development, process design, quality management, production and operation are urgently looking forward to the birth of innovative methods to cope with the challenges of big data in the industrial background. Although traditional manufacturing enterprises have initially established quality control information system, there are still many problems and limitations in the existing quality control system, which cannot meet the operation needs of manufacturing enterprises. This paper chooses the quality management of manufacturing enterprises as the research object, and uses advanced technologies such as computer, internet, big data and cloud computing to build a data system model of quality control of manufacturing enterprises. this paper optimizes the quality data processing process by establishing the quality monitoring model, and builds an intelligent quality supervision platform, and sets up the quality alarm rules for manufacturing enterprises. If the deviation between the actual value and the predicted value of the quality monitoring index is mapped between [0,1], the quality monitoring system can generate an outlier probability score. In this study, the traditional manual quality management is transformed into the management mode of the “internet + quality data” in order to realize the information, digital and intelligent quality management of manufacturing enterprises. This comprehensive research method combines modern digital technology and relevant theories of quality management to explore the optimization scheme of quality management of manufacturing enterprises, and also provides reference experience for the information construction of other similar enterprises.

The Impact Of The Digital Economy And New Infrastructure On China’s Economic Development

With the rapid growth of digital technology and the advent of the epidemic and post-epidemic era, digital economy has become a vital force driving China’s monetary growth and transformation and modernizing. The digital economy and new infrastructure are two important directions in current China’s economic development. Using the newly implemented infrastructure policies released by municipality governments in China from 2018 to 2022 as a data source, the concept of new progress, driven by novel technologies, and built on digitized networks of information, is used to provide a source of power for further economic development. This paper investigates the positive and negative effects of the mathematical economy on economic growth using the Cobb-Douglas production function, Keynes-Hicks social welfare function, and Differential pricing model. One way that the digital economy might advance is by lowering transaction costs, increasing economic growth rates and total factor productivity, creating new employment opportunities, raising individual income levels, improving social services and public administration, raising social welfare levels, and further promoting economic development. On the other hand, new infrastructure includes digital infrastructure, smart manufacturing, new energy, and transportation infrastructure. The construction of these infrastructures can promote industrial upgrading and the adjustment and optimization of industrial structures. The transformation of traditional service and manufacturing industries into digitized online industries will ultimately lead to a more transparent and efficient overall economy. Even if the new infrastructure and digital economy can cause problems such as unfair competition and privacy breaches, the combination of them can lead to a win-win situation for economic development in China.

Research on the Development Path of Industrial Tourism in Quanzhou City

The development of industrial tourism can not only promote industrial integration and enhance the added value of industrial industries, but also drive the development of related service industries, create more employment opportunities, and help local economic prosperity. Many enterprises in Quanzhou belong to the traditional manufacturing industry, so the development of industrial tourism demonstration bases can promote the transformation and upgrading of the traditional manufacturing industry and the full utilization of resources. At the same time, industrial tourism also contributes to the protection and inheritance of industrial heritage, promotes industrial civilization and enriches tourism content. The article evaluates industrial tourism in Quanzhou from the aspects of industrial tourism resources, tourism source market, and government support, and discusses the development of industrial tourism, product development, spatial layout of resources, and marketing of industrial products in Quanzhou, which provides a referable basis for Quanzhou to formulate the industrial heritage protection and development planning.

Enhancing green total factor productivity through manufacturing output servitization: A case study in China

In the context of the growing environmental pollution and resource depletion caused by traditional manufacturing industries, the need for sustainable and eco-friendly practices has become a critical issue for the upgrading and transformation of the manufacturing industry worldwide. Based on data from listed manufacturing companies in China, which is the world's largest manufacturing country and exhibits significant diversity regarding the ownership, scale and level of enterprises, the impact of manufacturing output servitization on green total factor productivity (GTFP), which is a measurement of economic efficiency that takes into account environmental impacts, is analyzed in this article. The results show that manufacturing output servitization can improve the GTFP of enterprises, and this can be achieved through mechanisms such as increased profitability and innovation capabilities. The positive effect on the GTFP of enterprises in less developed regions is greater than that in developed regions and is more significant for private and foreign-funded enterprises than for state-owned enterprises. The companies that adhere to the Global Reporting Initiative framework for environmental, social and governance reporting experience a more significant positive impact on GTFP as a result of their manufacturing output servitization efforts. This research offers valuable insights into the potential of servitization as a strategy for enhancing GTFP and provides actionable guidance for policy-makers and industry stakeholders seeking to align manufacturing practices with sustainability goals.

The meaning of knowledge transfer in educating engineers for modern manufacturing technologies

The development of modern manufacturing technologies such as additive manufacturing and other laser-based manufacturing technologies have increased their usage, especially in engineering education. Education must correspond to different applications of these technologies to ensure the quality of their usage in the manufacturing industry. Laser-based technologies have an impact on society through companies and industry in the manufacturing sector as they are employing graduated students, who are more aware of the possibilities of the technologies. Traditional manufacturing industries are facing the era of digital transformation. There are vast differences between industries and different industry players as well as their ability to keep up with the transformation process. The pace of the process can depend on many different items that are related to the company organization and leadership, technology base and to the abilities to modify these. The pace can also depend on the processes of cooperation and development between other ecosystem players e.g., supply chain partners or development drivers such as customer industries. This study concentrates on one of the key issues, namely learning and adoption of new skills to tackle the change. Universities have a major role in the knowledge transfer from education to working life. To develop this, there is a need to identify the factors in university training or pedagogical choices that can speed up the process of transformation. The aim of this research is to identify the factors for knowledge transfer through arranging efficient education for laser-based manufacturing technologies, especially in engineering education. It was concluded that by educating engineers with sufficient knowledge about the technologies, the manufacturing industry can utilize the knowledge and implement laser-based technologies better in their operations.

Preliminary study of knowledge transfer aspect in the creation of a framework for education and training of digital manufacturing technologies

Digitalization is changing the industry. As this change accelerates its speed, it also requires a transformation process where knowledge transfer between industry and research institutes play a significant role. There is a need to be more fluent, flexible, and efficient in order to get the latest research results into industrial implementation as quickly as possible. The challenge in knowledge transfer is that its speed in the current stage is too slow compared to the speed of development and changes required by digitalization of traditional manufacturing industries. The motivation for this study is the gap in knowledge transfer. One emerging digital transformation is the establishment of modern digital manufacturing technologies, e.g., additive manufacturing (AM). There are different approaches to supporting the industry in this transformation. Knowledge transfer can happen, for example, through education (e.g., master students) and industrial training, but also the fluent transfer of the latest research results from research institutes to companies is needed. University education needs to support the requirements of the manufacturing industry by providing future experts with skills to smooth the transformation process and bring novel technology applications, such as AM, to industrial-scale use. The article discusses how university education can support future competence-building in the industry. Similar needs are also present in industrial training, which universities often provide. Both education and training need to be improved from fundamental approaches to explain how this new knowledge should be created, i.e., how knowledge transfer happens most efficiently. The outcome of this article is the basis of the framework for education and training of digital manufacturing technologies by using modern learning methods and tools. More detailed pedagogical and knowledge transfer models can be developed and applied when this framework is created.

Does digital transformation enhance the core competitiveness?-Quasi-natural experimental evidence from Chinese traditional manufacturing.

In the era of the digital economy, building an internationally competitive manufacturing industry with intelligent manufacturing as its main focus is the only way to promote the transformation of a country into a manufacturing power and achieve high-quality economic development. To explore whether Digital transformation can improve the core competitiveness of traditional manufacturing enterprises, and what factors affect this process, this study establishes the core competitiveness system of enterprises through the principal component analysis(PCA) method and discusses the above issues through the construction of a double difference model. The results of this research from China's traditional manufacturing industry are as follows. (i) The digital transformation of enterprises has significantly improved their core competitiveness and has a certain time lag effect. (ii) In the process of enterprise digital transformation, enterprise management capabilities, environmental uncertainty, and enterprise operational efficiency will positively enhance the results of enterprise digital transformation. (iii) The enhancement of core competitiveness caused by digital transformation is more significant for the market leaders and laggards. (iv) Compared with non-state-owned enterprises, the Digital Transformationn of state-owned enterprises has a more obvious effect on promoting their core competitiveness. (v) In comparison with enterprises with low government subsidies, the Digital Transformation of enterprises with high government subsidies plays a more significant role in promoting their core competitiveness. In addition, this study proposes policy guidance and practical guidance for digital transformation to accelerate the promotion of core competitiveness of traditional manufacturing industry.