Traditional Manufacturing Industry Research Articles

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.

Transformation Mechanism of Supply Chain Micro and Small Enterprises in the Context of the Internet

The development of intelligent manufacturing has significantly impacted the traditional manufacturing industry, so it is urgent to change the traditional business model and production mode. As the world economy is in a downturn, small and micro enterprises in the subordinate position of the supply chain are facing a considerable threat. On the one hand, the substitutability is substantial; there is no bargaining power, which makes the enterprise's anti-risk ability 0. On the other hand, it is subject to significant restrictions, and the core enterprise thunder will protect all of the enterprises in the chain. This paper focuses on the risk identification and transformation mechanism of micro and small enterprises and whether micro and small enterprises have the conditions for transformation and upgrading in the context of the Internet. Based on the Bayesian formula, the AHP method model is the theoretical basis to help enterprises make decisions.

Analysis of the Impact of Environmental Regulation on the Green Innovation R&D Mechanism of Equipment

As a critical part of promoting the transformation of China's traditional manufacturing industry to intelligent manufacturing, the equipment manufacturing sector has the problems of high consumption and high pollution, which brings enormous environmental pressure and intensifies the contradiction between economic growth and environmental protection while rapidly boosting China's economic growth. To effectively deal with the relationship between economic development and environmental protection and achieve sustainable economic development, the Chinese government has introduced a series of environmental protection policies to promote the transformation of heavily polluting enterprises to green development with technological innovation as the intrinsic driving force. This paper analyzes the annual report data of 508 listed equipment manufacturing enterprises on the main board of Shanghai and Shenzhen A-shares from 2010 to 2019, explores the current situation of R&D investment of relevant enterprises, and analyzes the influence of different factors on R&D investment rate according to the model. The study concludes that there is a significant positive correlation between the equity financing rate and environmental regulation and the R&D investment rate of enterprises. Environmental regulation can significantly enhance the correlation between the equity financing rate and the R&D investment rate of enterprises.

Industrial hydrocarbon production and the new opportunities for green energy

Subject. This article discusses the issues of development of the petrochemical and oil refining industry of the Republic of Bashkortostan. Objectives. The article aims to assess the prospects for the use of renewable energy sources and the modernization of traditional manufacturing industries. Methods. For the study, we used the methods of graphic and statistical analyses. Results. The article finds that with the depletion of proven oil reserves in the Republic of Bashkortostan, the fuel balance can be restored through oil supplies from the Arctic areas and that the development of green energy in the region is primarily associated with the production of composite fuel. Relevance. The results of the study can be used when designing plans for the production of innovative products by enterprises of the fuel and energy complex.

3D printing: A catalyst for innovation in modern industries

3D printing, also known as additive manufacturing, has the potential to revolutionize various areas of society. This technology uses a layer-by-layer approach to transform digital designs into physical objects, allowing individuals and small businesses to easily produce prototypes, custom products, and replacement parts. This significantly reduces the barriers to entry that previously existed in the manufacturing industry. The democratization of product creation through 3D printing has significant implications for intellectual property and manufacturing. There are concerns about the ease with which 3D printing can be used to reproduce copyrighted designs, leading to debates on how to protect intellectual property rights in the digital age. Additionally, the technology may disrupt established manufacturing processes and threaten jobs in traditional manufacturing industries. Despite these concerns, the benefits of 3D printing cannot be ignored. The technology allows for efficient and cost-effective production of complex geometries that would be difficult or impossible to create using traditional manufacturing techniques. It also enables the creation of customizable and personalized products, reducing waste and increasing sustainability. To maximize the potential benefits of 3D printing while addressing its challenges, policymakers, stakeholders, and researchers are working together to develop solutions. These include developing new intellectual property laws and regulations, designing new manufacturing processes that combine traditional techniques with 3D printing, and promoting education and training programs to help ensure that individuals and businesses have the necessary skills to use 3D printing technology effectively.

Industrial Chain Map and Linkage Network Characteristics of Digital Economy

The development of the digital economy in the modern era significantly embodies a country’s comprehensive strength. This paper uses the data from China’s 2012 and 2017 input-output tables to analyze the linkage structure between the digital economy industry and other industries using the average propagation length model and social network analysis. The Weaver–Thomas model was used to obtain the industrial linkage matrix and network characteristics. An industrial chain map of the digital economy was depicted as the core. This paper is of practical significance in depicting the industrial chain structure and network characteristics, understanding the position of digital economy industry in the economic system and promoting the integrated development of the digital economy and traditional manufacturing industries, which allows them to incorporate the new generation of technology and industrial transformation with the digital technology as the core to promote their transformation and upgrading.

Application of digital-intelligence technology in the processing of Chinese materia medica.

Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.

The dynamic impact of digital economy on the green development of traditional manufacturing industry: Evidence from China

The traditional manufacturing industry is China’s second-largest source of carbon emissions, and the successful completion of dual carbon target depend on the traditional manufacturing industry’s ability to reduce emissions. This paper constructs the evaluation index system to analyze the driving factors and green coupling level of the digital economy enabling the green development of traditional manufacturing industry from three aspects: industry digitalization capacity, socio-economic benefits capacity and ecological resources carrying capacity based on the theory of social shaping of technology. From the perspective of regional economic development, this paper constructs the spatial Durbin model and threshold regression model to analyze the mediation effect and spatial spillover mechanism of the digital economy empowering the green development of traditional manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2020, the empirical analysis show that digital economy has the positive impact on the green development of traditional manufacturing industry through the digital innovation, industrial upgrading and human capital. At same time, the digital economy has a positive spatial autocorrelation pattern of the green development of traditional manufacturing industries with “high in the eastern and low in the western of China” from the global Moran index. Through the policy test of carbon-trading pilot programs by the difference-in-differences model, the digital economy has a non-linear impact on the intensive production of traditional manufacturing industry, which green finance activate the new impetus of market and the data element resources enhance industrial coordination development.

Research on Identifying Robot Collision Points in Human–Robot Collaboration Based on Force Method Principle Solving

After years of more rigid and conventional production processes, the traditional manufacturing industry has been moving toward flexible manufacturing and intelligent manufacturing in recent years. After more than half a century of development, robotics has penetrated into all aspects of human production and life, bringing significant changes to the development of human society. At the same time, the key technology of human–machine cooperative operation has become a research hotspot, and how to realize a human–machine integrated safety system has attracted attention. Human–machine integration means that humans and robots can work in the same natural space, coordinating closely and interacting naturally. To realize real human–robot integration under human–robot cooperative operation, the good judgment of intentional interaction and accidental collision and the detection of collision joints and collision points when accidental collision occurs are the key points. In this paper, we propose a method to identify the collision joints by detecting real-time current changes in each joint of the robot and solve the collision point location information of the collision joints through the principle of virtual displacement and the principle of force method using the force sensor data installed at the base of the robot as the known condition. The results show that the proposed method of identifying the collision joints using changes in joint current and then establishing a collision detection algorithm to solve the collision point location is correct and reliable.

Distributed Data Flow Scheduling Optimization in Industrial Internet of Things Based on Optimal Transport Theory

The development of industrial internet of things (IIoT) has completely changed the traditional manufacturing industry. The data exchange between controllers and actuators needs to achieve extremely low delay in IIoT. Due to the limited communication resources, it is necessary to reasonably schedule data flow to reduce delay. Although the studies of data flow scheduling exist in IIoT, they have not considered the impact of time-varying environmental factors and most of them adopted centralized scheduling schemes, which increase computation and communication cost rapidly in large-scale network scenarios. In this paper, the consensus-based distributed optimal transport (OT) algorithm is proposed to optimize data flow scheduling for IIoT networks. Specifically, a data flow scheduling optimization mechanism based on time-varying environmental factors is proposed and an online distributed data flow scheduling optimization algorithm is designed. Compared with the random data flow scheduling algorithm, numerical results show that the proposed algorithm can maximally reduce the average delay by 87%, increase the transmission rate and the spectral efficiency by 157% and 98%, respectively.

Supply chain finance and innovation efficiency: An empirical analysis based on manufacturing SMEs.

This paper firstly demonstrates the positive and negative effects of supply chain finance on the innovation efficiency of China's small and medium-sized enterprises (SMEs) in the manufacturing industry from the theoretical point of view. Based on the data of 267 manufacturing companies in China Growth Enterprise Market from 2015 to 2019, the DEA-SBM method was used to measure the comprehensive innovation efficiency of different companies, and it was further decomposed into technological innovation efficiency and organizational innovation efficiency. Afterwards, it conducts an empirical analysis through the double fixed effect model, and explores the difference in the impact of supply chain finance on innovation efficiency in enterprises with different industries and different property rights. The results show that supply chain financial services have a strong positive impact on the comprehensive innovation efficiency, technological innovation efficiency and organizational innovation efficiency of manufacturing SMEs. Further, supply chain finance has the most significant improvement on the technological innovation efficiency of the sample of private traditional enterprises, but it has a significant inhibitory effect on the organizational innovation efficiency of the sample of state-owned high-tech enterprises. Therefore, this paper suggests that the development of supply chain financial services should increase support for traditional manufacturing industries; appropriately tilt resources to private enterprises; improve relevant supply chain financial laws and regulations, establish and improve corresponding institutional arrangements, and encourage state-owned enterprises to participate in market competition.

Процеси цифровізації економіки та їх вплив на конкурентне середовище

In recent decades, the world has been experiencing the modernization of traditional manufacturing and service indus-tries in the global penetration of new information technologies into all spheres of social life. Digitization (or the economy dependent on digital technologies) gives a new impetus to creating new markets, new digital skills, and opportunities in society, business, and the state. Digitization of the economy is a particular stage of the modern development of scientific and technical progress, which is associated with the comprehensive implementation of Internet networks, computer and infor-mation technologies, electronic trade and commerce, cloud services in industrial, social, public, and other spheres of activity, as well as affect all sectors of the economy. The development of information technologies and systems today facilitates the work and management of the company. It improves the enterprise's activity in general, creating new opportunities and in-creasing the efficiency of the creation of each employee and staff in general. The impact of information technologies is at all stages of the company's activity – from business planning to changing the direction of action. Currently, completing indus-trialization and transitioning to Industry 4.0, the country is digitizing all economic activity, creating and developing new innovative products and technologies at an accelerated pace, dominated by digital platforms, artificial intelligence, in-formatization, and automation. The main goal of the economic transformation processes of digitization is the transformation of production into a flexible one adapted to existing realities, which increases the country's competitiveness in the "digital space". Digitization is a means of obtaining the necessary results that meet modern society's and businesses' needs for addi-tional profits. The digital economy requires knowledge about advanced goods and services and increasing the importance of innovation in the sustainable development of the world economy. The amount of information inherent in the digital economy fundamentally changes the functioning of markets, creating new development opportunities. Management processes are also changing, which in the digital economy are based on the transparency of management and decision-making processes and on the completeness and accuracy of source information. Digital technologies make it possible to process large amounts of in-formation to make optimal economic decisions, as well as improve the quality of the processed data. Keywords: digital economy, digital technologies, digitalization, economic processes, competitive environment.

Cultivating Regional Innovation Ecosystems in the Digital Age: Case Studies of Four Cites in China

This paper explores the cultivation paths of innovation ecosystems in the digital age by constructing a theoretical framework consisting of innovators, elements of innovation, platforms and digital governance. Four cities in China, Shenzhen, Hangzhou, Xuzhou and Weifang, were selected for in-depth case studies. Findings revealed that: (1) regions with ‘head enterprises’ and a strong industrial base achieve a competitive advantage via the synergy of digital industrialisation and industrial digitalisation; (2) digital technology pioneer regions can adopt digital industrialisation; (3) regions with a strong industrial base and traditional manufacturing industries can prioritise industrial digitalisation and (4) manufacturing regions with a large number of small and mid-size enterprises (SMEs) who can combine industrial digitalisation and digital value. This study provides theoretical and practical value for the cultivation of regional innovation ecosystems.

Digital Economy Empowers High-quality Development of Traditional Manufacturing Industry: Evidence from Nanjing Metropolitan Area

The integration and development of the digital economy and the real economy, especially the traditional manufacturing industry, is the focus of China's development at this stage. With its high efficiency and high value, the digital economy has rapidly exerted its powerful traction in the field of manufacturing, giving the traditional manufacturing industry a chance to usher in a "spring of development" and gradually align itself with the direction of high-quality development. In order to find micro evidence that the digital economy enables the high-quality development of traditional manufacturing industry, this article uses factor analysis methods to measure the development level of its digital economy based on the perspective of Nanjing Metropolitan Area, and conducts an empirical analysis of the effect of the digital economy on the high-quality development of traditional manufacturing industry.

An improved MPGA-ACO-BP algorithm and comprehensive evaluation system for intelligence workshop multi-modal data fusion

The digital economy is a new economic form taking data as an important production factor and digital and intelligent technology as a driving force for transformation. The core idea is to extract and fuse the knowledge implicit in data and transform it into intelligence to drive the transformation of traditional manufacturing industries, and one of its key technologies is multi-modal data fusion. In this paper, an improved MPGA-ACO-BP algorithm is proposed, and combined with an improved entropy-weighted TOPSIS method comprehensive evaluation system, which effectively solves the problem of “data scale inconsistency” between modal data leading to difficult model fusion and fusion accuracy. Finally, the validity of the theory and methods of this paper are verified using the example of multi-modal data fusion tool wear prediction in an intelligence workshop. By distilling the corresponding evaluation metrics inductively, the improved comprehensive evaluation system in this paper can also be extended to different production control scenarios to provide them with the corresponding integration information, which has a certain practical value.

A Vulnerability Assessment Framework for Product-Service Systems Based on Variation Mode and Effect Analysis

In recent decades, the product-service system (PSS) has been spotlighted due to its innovation and sustainability. As a novel business system, PSS provides additional value for products through the addition of service, which effectively upgrades the traditional manufacturing industry. For realizing a successful PSS, a robust and reliable operation stage is extremely important for users to stay satisfied and loyal. Thus, designers need to ensure that this system is not sensitive to any influential perturbation. Namely, they must achieve the desensitization of PSS to vulnerability. However, the current PSS design field still does not provide an effective method to assess the vulnerability in the whole life stage of PSS. This would lead to less time for the PSS provider to respond to various events. Furthermore, the tremendous loss could be caused due to the immaturity of the system. Therefore, this research has developed a vulnerability assessment framework based on variation mode and effect analysis (VMEA) for PSS. This developed framework has the ability to identify the potential noise factors and assess their severity based on multiple steps of analysis. This method has proved its effectiveness through an application example, and it is also expected to enable PSS researchers to design a robust PSS.

Intelligent Delay-Aware Partial Computing Task Offloading for Multiuser Industrial Internet of Things Through Edge Computing

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln

The push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time applications due to the high computational cost and being time-consuming. The rapid development of artificial intelligence technology will empower the traditional manufacturing industry. In this paper, we propose a data-driven digital twin framework for real-time flow field prediction by combining the CFD modeling simulation, IoT, and deep learning technologies. The framework integrates geometric, rule, physical, and neural network models to achieve the real-time simulation of physical and twin objects. The proper orthogonal decomposition (POD) and multiscale convolutional neural network (MCNN) are innovatively embedded into the framework. The POD is used to map high-dimensional data to low-dimensional features, and the MCNN is used to construct models predicting low-dimensional features for fast flow field prediction. The effectiveness of the proposed model is verified by the push-plate kiln case. The results show that the digital twin can quickly predict multi-physics fields based on the perceptual data to achieve the real-time evaluation of the operating state of the push-plate kiln.

Research on the impact of digital transformation on green development of manufacturing enterprises

Digital development is changing with each passing day, and the traditional manufacturing industry is gradually shifting from extensive development to intensive development, the most prominent manifestation of which is the digital transformation and development of enterprises. This paper first discusses the internal mechanism of digital transformation promoting the green development of manufacturing enterprises from three aspects: product greening, technology greening and investment greening. Then, based on the panel data of China’s listed manufacturing enterprises from 2011 to 2019, the fixed effect model, two-stage least squares method, mediating effect model and moderating effect model were used to test the relationship between them. The results show that digital transformation can promote the green development of manufacturing enterprises, and this conclusion still holds after a series of robustness tests. The empirical results of the mediating effect model show that digital transformation promotes the green development of manufacturing enterprises through three mediating paths: increasing green product output, technological innovation level and green investment level. Moreover, the promoting effect of digital transformation on the green development of enterprises will be moderated by the heterogeneous effect of environmental uncertainty. Therefore, accelerating the digital development of enterprises and promoting the construction of digital China is conducive to the green development of enterprises, and finally realizing the common development of digitalization and greening.