Intelligent Manufacturing Industry Research Articles

Reform and Practice of Project-Based Teaching Mode Based on Online Open Course Platform

Project-based practical teaching is an important part of the teaching reform of fashion design and intelligent manufacturing industry course in vocational colleges, and also an important measure to enhance the effectiveness of teaching. A good practical teaching base is a strong guarantee for the smooth development of project-based teaching. This paper explores the reform and innovation of project-based teaching of fashion design and intelligent manufacturing specialty, and further studies the construction of project-based base focusing on base practice teaching and online courses, to further integrate project-based teaching resources, give play to the role of network-based virtual platform, build perfect project processes, improve project-based teaching course resources of base, and realize co-building and sharing of teaching resources between colleges and enterprises, so as to further improve the teaching quality of project-based teaching of fashion design and intelligent manufacturing specialty, and develop more qualified technical talents.

Research on the Training of Interdisciplinary-Based Intelligent Manufacturing Talents

As a significant field of research under the 14th Five Year Plan, intelligent manufacturing holds a special position in industrial upgrading and core technology independence. Intelligent manufacturing is an important support for building a “scientific and technological power” in the future. Cultivating creative interdisciplinary talents who can adapt to the development of intelligent manufacturing industry in the new era is of great significance for China to realize the modernization and autonomy of its industrial system. In view of the existing gap between the interdisciplinary talent training of intelligent manufacturing and the actual needs of the industry, this paper focuses on the concept of interdisciplinary construction. Based on the cycle improvement of professional construction, a curriculum system that integrates the interaction of interconnected projects is established. Through four specific measures, namely establishing the collaborative development system of intelligent manufacturing specialty, building a cycle diagnosis and reform system of intelligent manufacturing specialty, improving the curriculum system of integrated modular specialty group, and setting up an application scenario project-based curriculum, we hope to provide reference for the cross-training of compound intelligent manufacturing talents.

Research on Enterprise Management in Intelligent Manufacturing Industry

Research on Enterprise Management in Intelligent Manufacturing Industry

A Combined Fuzzy Optimization Model for the Location of an Intelligent Energy-Efficient Manufacturing Industrial Park

With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.

Does It Help Carbon Reduction in China? A Research Paper about the Mediating Role of Production Automation Based on the Carbon Kuznets Curve

As China puts forward its “carbon emissions peak and carbon neutrality” goals, how to achieve carbon reductions has become a key for China’s goal. The manufacturing industry is a significant source of carbon dioxide emissions. For a manufacturing country such as China, adjustments in various aspects of the industry would have a huge impact on its carbon emissions. As an important reform of the contemporary production mode, the process of production automation in China will inevitably affect China’s carbon emissions; therefore, the analysis of the impact of that production automation on the carbon dioxide emissions is an important basis for judging the future carbon reductions in China. Referring to the traditional study of the carbon Kuznets curve, this paper analyzes the impact of an average wage on production automation and the role of production automation in the carbon Kuznets curve (CKC). This paper proposes that production automation plays a mediating role in the process of carbon emissions, and gives a verification model of that mediating role. By analyzing the relationship between average wages and the production automation process, the U-shaped curve relationship between them was verified. By examining the relationship between carbon dioxide emissions data and the production automation industry in China, we verified that production automation plays a partial mediating role in the change of the carbon Kuznets curve. Combined with the analysis of the two parts, this paper believes that with the continuous development of China’s intelligent manufacturing industry, China’s carbon reduction prospects are more optimistic, and that there is a good industrial foundation to achieve the “carbon peaking and carbon neutrality” goals. Finally, this paper proposes policy suggestions so as to increase research investment in production automation, to help promote the application of production automation, encourage the research and application development of low-carbon technology, especially encouraging modular design, and to give full play to the role of production automation in the process of carbon neutrality in China.

The Application of Machine Vision in Intelligent Manufacturing

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

Research on capital allocation efficiencies with four-dimensional factor capitals from China's intelligent manufacturing enterprises.

Compared with traditional manufacturing enterprises, intelligent manufacturing enterprises pay more attention to the investment of knowledge capital and technological capital. Taking 258 intelligent manufacturing listed companies in China from 2015 to 2020 as research samples, the paper selects the material capital, human capital, knowledge capital and technological capital of enterprises as the input variables of Cobb-Douglas production function. Considering that enterprises are often affected by spatial correlation, stochastic frontier panel model, spatial lag stochastic frontier panel model and dynamic spatial lag stochastic frontier panel model are constructed to measure capital allocation efficiencies of enterprises. The results show that all the factor capitals in the three models have a significant positive impact on enterprises’ performance, and the dual lag effect of time and space is significant. Moreover, it is more reasonable to use the dynamic spatial lag stochastic frontier panel model to estimate the parameters and measure capital allocation efficiencies. The development of intelligent manufacturing industry has significant space-time spillover effect among provinces. About 52.98% of intelligent manufacturing enterprises have high capital allocation efficiencies, but 12.04% still need to further optimize capital allocation. The gap between the actual performance of the sample enterprises and efficiency frontier is mainly due to technical ineffectiveness. From a regional perspective, the top ten enterprises with high capital allocation efficiencies are all in the eastern region, but the average of capital allocation efficiency is the highest in the western region, followed by the eastern and central regions.

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network.

Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices.

Enhancing Digital Innovation for the Sustainable Transformation of Manufacturing Industry: A Pressure-State-Response System Framework to Perceptions of Digital Green Innovation and Its Performance for Green and Intelligent Manufacturing

Low carbon and digitalization are the general trends of manufacturing upgrading and transformation. Digital technology enables the whole process of green manufacturing and breaks down the spatial barrier. To achieve the dual carbon goals, the pressure-state-response (PSR) model, in which digital technology enables the green innovation of the manufacturing industry, was theoretically analyzed in this study. The measurement system of the digital green innovation (DGI) in the manufacturing industry was constructed according to the PSR framework. An evaluation model based on the analytic hierarchy process and the deviation maximization technique for order preference by similarity to an ideal solution method was constructed to measure the level of DGI. The results of this study from Chinese manufacturing are as follows. (i) The measurement system of the level of DGI in manufacturing industry includes a pressure system, state system and response system. (ii) In the past five years, the comprehensive index of the DGI in manufacturing industry has generally shown a trend of fluctuating rise. There are overall low and unbalanced phenomena in all regions. The gap decreased from 0.1320 to 0.1187, showing a gradually narrowing trend. (iii) Compared with other regions, the composite index of DGI is generally higher in the regions with a better ecological environment in the east and a more developed economy in the north. State parameters are higher than pressure and response parameters in most areas. (iv) Compared with other regions, the composite index of DGI in western and southern regions is lower, and the parameters of pressure, status and response are basically coordinated. (v) The application degree of digital technology, the emission intensity of waste water/exhaust gas of output value of one hundred million yuan and the expenditure intensity of digital technology adopted by enterprises are the key influencing factors of DGI in the manufacturing industry. This study not only proposed an evaluation index system of the digital green innovation level, but also puts forward policy guidance and practical guidance of digital technology to accelerate the green and intelligent manufacturing industry.

Research on National Costume Design Based on Virtual Reality Technology

Cheongsam has the unique costume culture characteristics of the Chinese nation and is a classic style of traditional Chinese costumes. At the same time, with the rapid development of science and technology, 3D virtual technology plays an increasingly important role in the garment intelligent manufacturing industry. Therefore, how to combine 3D virtual technology with the development of women’s cheongsam clothing products is of great significance, which can overcome the limitations of time and space and effectively improve the efficiency of clothing pattern design. To solve this problem, a design method for national costume based on virtual reality technology is proposed. First of all, the modeling and structural characteristics of cheongsam are analyzed. Secondly, different curve fitting methods are applied to human body feature recognition, and cubic polynomial fitting is selected to complete the feature recognition of the human body model in a virtual environment. Then, in order to prevent the penetration between clothing and the human body, the collision detection function based on the AABB bounding box is added, and a method based on linear sensitivity is used to map 2D to 3D. Finally, the model of a cheongsam costume is created by using the clothing simulation Marvelous Designer software, and the effectiveness of the proposed virtual cheongsam costume simulation design method is verified by subjective evaluation indexes.

Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM

With the in-depth reform of intelligent manufacturing, selecting high-quality intelligent manufacturing system solution suppliers has become a key force to promote the intelligent transformation of manufacturing enterprises. However, manufacturing enterprises have hidden risks in the selection process of many intelligent manufacturing system solution suppliers, so it is urgent to carry out the research on the risk evaluation of intelligent manufacturing system solution suppliers. Based on the current situation in China’s intelligent manufacturing industry, this paper constructs the evaluation index system of intelligent manufacturing system solution suppliers, uses the PLS-SEM method to establish the risk evaluation model of intelligent manufacturing system solution suppliers, collects data through a questionnaire survey, uses a PLS algorithm to fit the index and test the model, and uses power BI software to visualize the risky impact. The conclusions are as follows: (1) The primary indicators have hidden risks for the system solution suppliers. (2) The higher the achievement of secondary indicators, the lower the implied risk, and the more conducive to the intelligent upgrading of manufacturing enterprises. According to the visualization results, management suggestions are given to provide useful reference for manufacturing enterprises to select high-quality intelligent manufacturing system solution suppliers and promote the transformation and upgrading of manufacturing enterprises, from digitization and networking to intelligent stage.

Retracted] Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network

With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry. In order to adapt to market demand, enterprises need to constantly integrate resources to improve the competitiveness of enterprise supply chain. Based on the background of suppliers in intelligent manufacturing enterprises, the evaluation method of supplier efficiency was studied by using machine learning. In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO‐BP neural network is constructed. Through the collected sample data, the network is trained and simulated, and the results are analyzed. Finally, the designed model is applied to a large battery manufacturing enterprise, and the supplier efficiency evaluation method based on DPMPSO‐BP neural network is validated and analyzed. Compared with the traditional BP neural network method, the supplier efficiency evaluation method is effective and feasible.

Exploration on Automatic Management of GIS Using TL-CNN and IoT

The research aims to effectively monitor the state of complex industrial equipment in real time, diagnose the internal Partial Discharge (PD) pattern of Gas Insulated Metal Enclosed Switchgear (GIS), implement effective health management, and change the traditional model optimization route of increasing training time in exchange for performance improvement. This paper studies the complex equipment-oriented Health Management System (HMS) based on Internet of Things (IoT) technology and Transfer Learning. Firstly, the principles of Transfer Learning and Deep Learning (DL) technology are introduced. Secondly, the requirements of GIS internal status recognition and management are studied. Furthermore, a GIS-oriented HMS based on Transfer Learning-optimized Convolutional Neural Networks (CNN) is proposed, and the training dataset is constructed. Finally, the proposed model is tested. The results show that the complex equipment-oriented HMS based on IoT technology, CNN, and Transfer Learning can detect the internal status of GIS in real time. Compared with the traditional DL algorithm and expert system, the proposed model has a shorter training time of only 16min, faster convergence speed, high testing recognition rate, and over 96% recognition rate. Compared with other mainstream algorithms, it has higher identification, the storage parameter volume is 408, and the storage space is 12.8MB. Moreover, the proposed Transfer Learning-optimized CNN model can accurately detect the status of GIS, identify abnormal statuses, and help prolong the service life of GIS. The proposed complex equipment-oriented HMS contributes to the intelligent manufacturing industry and provides a new direction for applying emerging Computer Technology in the intelligent industry.

Artificial intelligence techniques for implementation of intelligent machining

For the past few years, the rapid progress and development of artificial intelligence (AI) based technologies have been analyzed for the applications of the intelligent manufacturing industry, i.e., industry 4.0. this has triggered a valuable transformation in means, models, and ecosystems within the manufacturing industry and AI development. With the advancement in manufacturing technology, there is a need to execute these technologies and AI more efficiently and cost-effectively. It can be possible by combining traditional manufacturing and machining technologies with recently developed intelligent manufacturing technologies comprising hardware and software techniques. This review paper discusses various AI implementation-based intelligent manufacturing industries with their architecture and technology systems based on the integration of AI with manufacturing and information communication. Furthermore, AI-based manufacturing application, their implementation, and current development in intelligent manufacturing have also been discussed.

Design of Customized Intelligent Manufacturing Information Interaction System Based on Virtual Technology

With the continuous development of information technology, system intelligence is leading the next round of “industrial revolution”, especially the intelligent manufacturing industry has become the core of improving industrial productivity. Intelligent manufacturing involves each link in the manufacturing industry, which is the most critical part of intelligent manufacturing is intelligent production, through intelligent manufacturing related technology to optimize the production mode of manufacturing to promote the production state more flexible and integrated. Intelligent manufacturing is based on computer simulation technology and information and communication technology, optimize the production design of the factory and simplify the production process of the factory, the purpose is to reduce the waste of resources and improve the reasonable allocation of production resources.

An online detection system of energy efficiency for intelligent manufacturing

Online detection of energy efficiency automatically has important significance for evaluating new production lines and detecting the performance of existing production lines in modern intelligent manufacturing and smart industry. To solve the shortcomings of existing detection systems, a novel online detection system of energy efficiency is proposed in this paper. Within a time interval, the proposed system detects the production quantity and the energy consumption of the production line by a production quantity detection module and a power consumption sensing module, then calculates and obtains the production efficiency during the period and displays to users by output module. Experimental results show that the proposed system has high practical value.

Key Course Group Construction of Intelligent Manufacturing for Applied

This article mainly expounds the intelligent manufacturing theory basis and technology foundation course group construction in the important role of applied undergraduate professional curriculum system, combining the reform of the new round of intelligent industry, on theoretical knowledge taught content at the same time, the core courses and practical application of the teaching content to construct, and explore the construction conform to the concept of the new era of smart manufacturing key lessons Cheng Qun, the introduction of cognition and reconstruction of the teaching theory, make full use of various platforms to help students better meet the requirements of intelligent manufacturing industry talent.

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the existing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.

Factors Affecting the Manufacturing Industry Transformation and Upgrading: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area.

This study aims to analyze the development trend of the manufacturing industry transformation and upgrading in the Guangdong–Hong Kong–Macao Greater Bay Area (2008–2018). On the basis of synergetics, the order parameter method of factor analysis is used to study these factors. The results show that: (1) There are five slow variable factors, such as intelligent manufacturing industry, technological innovation, scale agglomeration, market demand, and fixed asset investment, which are important power sources of the transformation and upgrading of the manufacturing industry in Greater Bay Area. The development of these factors is relatively mature, and they cooperate with each other. (2) Similar to a fast variable of manufacturing development ecology, green development is an important coordinating factor in removing bottlenecks. Finally, suggestions for the development of the transformation and upgrading of the manufacturing industry are put forward.

Intelligent manufacturing in the context of industry 4.0: A case study of siemens industry

At present, the methods by which products are designed and manufactured are advancing at a rapid pace just before our eyes. The contemporary period known as Industry 4.0 is realizing an evolution from manual to digital operations and it has great importance to manufacturers throughout the world. In the context of Industry 4.0, intelligent manufacturing is a paradigm enabled for the integration of smart sensors and controls. Traditional manufacturers are exploring intelligent manufacturing approaches (Machine Learning/Artificial Intelligence, Internet of Things, Digital Twin, etc.) to improve their competitiveness, but lack in implementing them. This paper reviews the intelligent manufacturing approaches and extends a study on how a manufacturing production plant applies its intelligence in executing these approaches to transform from discrete manufacturing to intelligent manufacturing. The case study initiates the implementation of Siemens manufacturing production plant into a model based on intelligent manufacturing, thereby transforming the whole into an intelligent manufacturing industry. A broadened path about the implementation of intelligent manufacturing in this industry is outlined. In the operation of selecting these approaches, the manufacturing plant continuously mechanizes its products, informatizes its management, and digitizes its operation.