Traditional Manufacturing Industry Research Articles

Reinforcement Learning Optimized Intelligent Electricity Dispatching System

With the rapid development of artificial intelligence, new changes are coming to all walks of life including traditional manufacturing industry, bio-pharmaceutical industry, electric-power industry, etc.. For electric-power industry, many machine learning algorithms have been used to achieve intelligent dispatch of electricity, intelligent electricity equipment fault diagnosis and more optimized customer management, among which the automation of power network is one of the most important issue ensuring safe operation of electricity grid. In the recent years, intelligent dispatch method based on big data analysis has been used in intellectual scheduling of electricity and has got significant promotion. However, the intelligent dispatch on big data analysis needs tremendous history data that is sometime not that easy to get. Moreover, once a small probability event happens, the big data based intelligent dispatch method may lose efficacy, thus in this paper we proposed a new intelligent dispatch model based on the reinforcement learning that is more robust, safer and with higher efficiency. Finally, according to the empirical study result, our model outperforms the traditional methods, where the average economic benefit in the test area increases more than 25%, fluctuation of distribution is more stable than before and the carbon emission decreases about 30%.

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.

Preliminary optimization of shape memory polymers geometric parameters to enhance the thermal loads' activation range

Over the last decades, 3D printing has found applications in all areas of engineering, ranging from electronics to biomedical industries, due to its efficiency in the use of the material in terms of mass customization and waste reduction and its high resolution and precision during the prototyping stage. Nevertheless, while the traditional additive manufacturing industry is developing new applications or proposing some improvement of the static and inanimate 3D printed components, a novel technology, i.e., 4D printing, is arising. 4D printing adds a temporal dimension to 3D by providing vitality to the design of shape-memory materials using an external stimulus to trigger the object transformation into another designed structure. This contribution aims to improve the available information on the Polylactic Acid (PLA) shape memory polymer (SMP) behaviour under the effect of homogeneous thermal stimuli. Consequently, this study deals with a first analysis to understand the proper methodology to activate the shape memory effect (SME) and achieve a programmed deformation. The preliminary experimental results are compared with a simplified Finite Element Analysis (FEA) model to analyze and better understand the SME. This work lays the foundation for later research to understand which optimal specimens' geometric parameters may enlarge the activation range in PLA specimens, enhancing their shape capabilities of detecting temperature modification close to the glass transition temperature (Tg).

Analysis of the Spatial Characteristics and Influencing Factors of e‐Commerce Industrial Chains from the Perspective of Embeddedness: Taking Xiong’an New Area in China as an Example

e‐commerce has promoted the application of the Internet by traditional enterprises. While this phenomenon has deepened industrial embeddedness, it has also profoundly affected the development of traditional manufacturing and retail industries. Based on the embeddedness theory, this paper takes the e‐commerce industrial chain in Xiong’an New Area of China as an example, uses location entropy method and local spatial autocorrelation to analyze its spatial characteristics and influencing factors, and also discusses the features of embeddedness from each dimension of industrial chains. The results show the following: ① The supply chain of industry‐dependent e‐commerce is dominated by production wholesalers, and suppliers with cost advantages and commercial traditions are embedded in local characteristic industries; the e‐commerce value chain is dominated by production and processing activities, and the spatial orientation of the business model is affected by regional industrial functions; the e‐commerce enterprise chain is dominated by B2B enterprises, the high‐level agglomeration of which reflects the absolute scale advantage provided by the e‐commercialization of traditional industry. ② The distribution of the e‐commerce industrial chains of Xiong’an New Area is characterized by spatial heterogeneity and has obvious spatial agglomeration. The industrial chains are concentrated in Xiong County, followed by Rongcheng County, and Anxin County has the weakest agglomeration. ③ The main factors that cause the uneven distribution of the e‐commerce industrial chains in Xiong’an New Area include three aspects: transportation location advantages, traditional industrial foundation, and government policy regulation. Research can provide a scientific reference for accurately identifying the spatial development characteristics of the industrial chains from various perspectives and promoting the sustainable development and e‐commercialization of traditional industries.

MANUFACTURING ASPECTS OF OFFSHORE FABRICATION AND INSTALLATION

The research presented in this paper aim at identifying research commonalities between shipbuilding, offshore fabrication practices and manufacturing. As part of an exploratory effort a literature review and a case study of two offshore structures projects were performed. Research concerning shipbuilding and offshore fabrication, together with literature from other industries in construction, larger engineering projects and traditional manufacturing was reviewed. The two offshore structures projects were analyzed by means of interviews and complemented by direct observations and document reviews. The study concludes that there are gaps in the research concerned with holistic perspectives on the fabrication and installation phases of shipbuilding and offshore projects. The number of actors involved in any project of this magnitude increase barriers and communication interfaces. The dynamic nature of these types of projects was also observed and the changeability should always be a accounted factor when dealing with projects of this sort. The interviews held as part of the verification of observed phenomena in literature was limited to two projects and a single company and actors perceptions. However the collected data served well in being complementary to the literature review. It could be the task of academia to patch the gaps for overall project success, in the cases where single industry actors simply cannot see the benefit or do not have the recourses to fill them themselves. This study combines findings from traditional manufacturing industries, shipbuilding, offshore structures fabrication and large engineering projects in general.

Application of Artificial Intelligence and Collaborative Knowledge for Manufacturing Design

With the rapid development of science and technology, digital technology has brought the world economy and management into a new stage. Collaborative design can realize product design process by cross-regional and cross-industry designers and share and exchange product information through network. With the rapid development of big data and artificial intelligence, knowledge services have gradually developed into multirole collaborative design activities based on artificial intelligence decision support. Traditional manufacturing industry has gradually transformed into modern manufacturing service industry after integrating information technology means such as Internet, communication, computer, and modern management methods. This article focuses on artificial intelligence decision support systems and the complex product manufacturing industry. We present a detailed analysis of how to integrate the knowledge generated by the product life cycle in the era of big data. We calculate the influence coefficient and sensitivity index of four different industries and propose a metadata architecture to improve the value of products as a whole. The findings of the research study imply that a knowledge-based collaborative platform should be designed by the enterprises and industries and a platform-based construction approach for economical, practical, and reliable production. We also present a detailed discussion about other factors such as the network cost of symmetric services, raw data and forecast data, and the number of nodes and the processing complexity.

Knowledge Integration and Disruptive Innovation Product Development: Case Studies of Company A

Because of the energy crisis, companies started developing electric vehicles. Company A, as a new entrant, successfully developed a disruptive innovation of the world’s first high-performance, zero emissions, and two-wheeled electric scooter. As disruptive innovation is frequently discovered in the combination of different industrial technologies, company A is comprised of high-tech and traditional industry resources and knowledge. This study aims to explore the process of heterogeneous industry knowledge integration in the traditional motorcycle manufacturing industry by a case study of company A. Following qualitative research method, this study concludes four main findings in product development: (1) organizational knowledge integration is identified to mitigate the impact of functional conflict; (2) design validation knowledge integration is identified to set the standard for pioneer product strategy; (3) internal and external engineering knowledge integration is identified to realizes the goal of pioneer product strategy; (4) product validation knowledge integration is identified to coordinate the flexible planning process.

Evolution Process and Simulation of Advanced Equipment Manufacturing Innovation Network Based on Intelligent System.

With the widespread application of intelligent systems in major industries, the traditional equipment manufacturing industry has gradually shifted to advanced equipment manufacturing. The innovation network of an industry directly affects the innovation capability of the industry, so the research of innovation network has always been the focus of attention. However, there are few researches on the innovation network of advanced equipment manufacturing industry, so this article has launched an in-depth study on it. First, it analyzes the basic geometric characteristics of the innovation network. Then, based on the small-world network model, an evolution model of the advanced equipment manufacturing innovation network was created, and simulation experiments were performed on the evolution model. The experimental results show that the clustering coefficient and the average distance showed different changes in different stages, but in the end they all gradually stabilized. The clustering coefficient was 0.484, the average distance was 1.854, and the degree of small cosmos was 0.497. This shows that the innovation network evolution model established in this study can reveal the evolution process of the innovation network of advanced equipment manufacturing through simulation experiments.

Application of Intelligent Manufacturing Technology in the Field of Ship Design and Manufacturing

With the rapid development of information technology and its gradual penetration into the traditional manufacturing industry, modern industry has entered a period of intelligent manufacturing. In order to firmly grasp this brand-new development opportunity, various industries in our country are accelerating innovation and development, forming a number of brand-new industries and models. At this stage, the global shipping volume is increasing slowly and the endogenous power is lacking. The shipbuilding industry occupies a large proportion of my country’s traditional industries. It not only provides technical equipment for the development of marine resources and national defense construction, but is also an important channel for world trade, driving the development of many key industries, and it is a modern comprehensive industry with a huge industrial chain. Sex industry. The shipbuilding industry can drive the rapid development of the local economy and play an important role in promoting labor employment and ensuring the safety of coastal defense. In recent years, my country’s foreign trade has been growing at an alarming rate, which has provided better development opportunities to a certain extent. The industry has high international competitiveness.

Research on influencing factors and multiple driving paths of intelligent transformation in China’s manufacturing industry

As the internal requirement of the development of China’s traditional manufacturing industry, intelligent transformation has gradually become the main goal of China’s regional manufacturing transformation. From the perspective of manufacturing value chain, this paper proposes a conceptual model of intelligent transformation of manufacturing industry. Taking 22 sample cities as cases, the paper uses fuzzy set qualitative comparative analysis method to identify the necessary factors of intelligent transformation of manufacturing industry and the configuration analysis of each factor combination, and then obtains three realization paths to promote intelligent transformation. The results show that intelligent technology innovation and intelligent industry investment are the necessary conditions for the intelligent transformation of manufacturing industry, and there are three configuration paths: collaborative R&D and processing, external factor driven and value chain climbing, which promote the intelligent transformation of manufacturing industry. This paper enriches the research on intelligent transformation of manufacturing industry from the perspective of value chain, and uses the qualitative comparative analysis method of fuzzy set to provide countermeasures and suggestions for the direction and path of intelligent transformation of manufacturing industry.

Application of industrial engineering in blazer making

This paper is being developed in Blazer on industrial engineering. In the last few decades, the global economy, in particular in international commerce and manufacturing organizations, has evolved substantially. The technical framework of today is thus distinguished by productivity-oriented, which can be accomplished by industrial engineering. This paper presents the different principles & approaches used in industrial engineering, such as time analysis, work studies, line balancing and the outcome of organizational disintegration tests in Blazer. This research has focused on several kinds of machinery to produce a Blazer. The traditional manufacturing industry has issues such as low efficiency, longer lead time, high rework & re-work, poor line balance, low style shift flexibility, etc. The implementation of Industrial Engineering correctly resolved these issues. This study looks at how to develop Blazer's processes by using Industrial Engineering, wasting less resources, time, raw materials, human capital and electricity.

New Craft Production in Europe - between Creative Class and Industrial Manufacturing

Purpose: This article presents research regarding “new craft”, an emerging and developing part of the local economy in many European cities. The term refers to types of work between traditional manufacturing and creative industries, more innovative and individual than the former and more tangible than most of the latter. Approach: The study was based on a literature review, interviews and analysis of a case study - the post-industrial Praga district of Warsaw, which is an original work of the authors from Warsaw School of Economics and OpenHeritage, a Horizon 2020 project. Findings: The findings encompass specific aspects and characteristics of jobs located on the edges of two groups – traditional manufacturing and creative industries. This research helps to fill a previous gap in the European statistics regarding small-scale production and manufacturing. This part of locality is gaining importance due to (1) a recent stimulus in new forms of production and the testing of innovative, large-scale manufacturing (2) the re-introduction of urban manufacturing which now raises less concern about possible negative environmental impacts (3) the role of local networks during the Covid-19 pandemic. Practical Implications: New craft production is a significant and growing trend in numerous European cities. It should be based on two elements: (1) traditional high quality European craft and manufacturing (2) innovation and creative industries in which the EU wants to be a global leader. Therefore, there is a need for policies, financial mechanisms and statistical recognition that will bring benefits to craftsmen and municipal decision makers.. Originality/value: According to the authors, new craft production to some extent replaces traditional craftspeople but more importantly stimulates innovation. Therefore, its role and characteristics should be discussed further – especially in new post-Covid 19 reality – as a factor in stimulating the development of local economies, including districts and cities.

Path selection of lean digitalization for traditional manufacturing industry under heterogeneous competitive position

Lean digitization is a management method that integrates lean thinking into the process of enterprise production management by virtue of digital technology, and the competitive position has become an important situational factor affecting the implementation of lean digitalization. Most manufacturing enterprises are not clear about the path of lean digitalization, nor can they develop a set of tangible lean digitalization schemes combined with their own competitive position. In order to help enterprises to form proper implementation scheme of lean digitalization with limited resources, we conducted a survey on some traditional manufacturing enterprises carrying out lean digitalization. This study analyzes the influencing factors of lean digitalization and the multiple concurrent causal relationships among factors from four stages of “point, line, plane and cube” by using the fuzzy additive ratio assessment (ARAS-F) and fuzzy sets qualitative comparative analysis (fsQCA) methods. The results showing: (1) In the stages of “point, line, plane and cube”, all the internal and external influencing factors cannot be solely regarded as the necessary conditions for the efficient implementation of lean digitization, in other words, the explanatory power of a single influencing factor is not strong. Each factor must be combined to form the necessary conditions for lean digitization; (2) In the stages of “point, line, plane and cube”, the multi-combination paths of lean digitalization are got under different competitive position. This study points out the implementation path of lean digitalization for traditional manufacturing enterprises under different competitive positions, and provides the possibility of theoretical support for the practice of lean digitalization.

Cloud manufacturing architecture: a critical analysis of its development, characteristics and future agenda to support its adoption

PurposeIn the last decade, cloud manufacturing (CMfg) has attracted considerable attention from academia and industry worldwide. It is widely accepted that the design and analysis of cloud manufacturing architecture (CMfg-A) are the basis for developing and applying CMfg systems. However, in existing studies, analysis of the status, development process and internal characteristics of CMfg-A is lacking, hindering an understanding of the research hotspots and development trends of CMfg-A. Meanwhile, effective guidance is lacking on the construction of superior CMfg-As. The purpose of this paper is to review the relevant research on CMfg-A via identification of the main layers, elements, relationships, structure and functions of CMfg-A to provide valuable information to scholars and practitioners for further research on key CMfg-A technologies and the construction of CMfg systems with superior performance.Design/methodology/approachThis study systematically reviews the relevant research on CMfg-A across transformation process to internal characteristics by integrating quantitative and qualitative methods. First, the split and reorganization method is used to recognize the main layers of CMfg-A. Then, the transformation process of six main layers is analysed through retrospective analysis, and the similarities and differences in CMfg-A are obtained. Subsequently, based on systematic theory, the elements, relationships, structure and functions of CMfg-A are inductively studied. A 3D printing architecture design case is conducted to discuss the weakness of the previous architecture and demonstrate how to improve it. Finally, the primary current trends and future opportunities are presented.FindingsBy analyzing the transformation process of CMfg-A, this study finds that CMfg-A resources are developing from tangible resources into intangible resources and intelligent resources. CMfg-A technology is developing from traditional cloud computing-based technology towards advanced manufacturing technology, and CMfg-A application scope is gradually expanding from traditional manufacturing industry to emerging manufacturing industry. In addition, by analyzing the elements, relationships, structure and functions of CMfg-A, this study finds that CMfg-A is undergoing a new generation of transformation, with trends of integrated development, intelligent development, innovative development and green development. Case study shows that the analysis of the development trend and internal characteristics of the architecture facilitates the design of a more effective architecture.Research limitations/implicationsThis paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. The reason for considering Chinese articles is that CMfg was proposed by the Chinese and a lot of Chinese CMfg-A articles have been published in recent years. CMfg is suitable for the development of China’s manufacturing industry because of China’s intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg-A.Originality/valuePrior studies ignore the identification and analysis of development process and internal characteristics for the current development of CMfg-A, including the main layers identification of different CMfg-As and the transformation process analysis of these main layers, and in-depth analysis of the inner essence of CMfg-A (such as its elements, relationships, structure and functions). This study addresses these limitations and provides a comprehensive literature review.

Analytics-enabled escalation management: System development and business value assessment

Industry 4.0 initiatives can help traditional manufacturing industry cope with increasing global competition. Such solutions facilitate transparency, automation as well as business process transformation. This paper elaborates on a collaboration with a medium-sized manufacturing company. We highlight the design, evaluation and roll-out of an escalation management system with integrated data-driven decision support. We do so by applying an action design research process. Thereby, our study focuses on the system design concerning the creation of business value.The system leverages state-of-the-art machine learning algorithms for disruption type classification and escalation handling duration prediction. These predictions can be embedded in an integrated planning procedure leveraging diverse organizational data sources (e.g., personnel availability, production plans) to instantiate a prescriptive analytics solution. Combined with informative analytics insights, this allows the proposed system to generate significant business value by reducing escalation durations. In the long run, the transformational business value enabled by the system is likely to exceed the automational business value. This highlights the special importance of tight integration of industrial analytics applications within business processes.

Spatial–temporal distribution of air-pollution-intensive industries and its social-economic driving mechanism in Zhejiang Province, China: a framework of spatial econometric analysis

The distribution of pollution-intensive industries is a serious challenge to environmental governance of China during the transition period. However, existing studies lack the attention to the distribution and driving factors of air-pollution-intensive industries (APIIs). Therefore, based on geospatial analysis and spatial measurement models, this study explored the spatial distribution of APIIs and their socioeconomic driving factors in Zhejiang Province from 2006 to 2018. The results show that (1) the output value of APIIs in Zhejiang showed a trend of first increasing and then slowly decreasing, indicating the industry structure has a tendency toward improvement; (2) the spatial agglomeration of APIIs in Zhejiang Province has a positive spatial correlation (significant at least at the level of 10%), and the main agglomeration areas are located in cities around Hangzhou Bay, but the overall level of agglomeration is decreasing and shows a certain diffusion trend. (3) APIIs in Zhejiang Province have significant industry differences. Industries with a high degree of concentration are mainly petroleum processing, coking, nuclear fuel processing, chemical fiber manufacturing and textile, all with the Gini coefficient exceeding 0.5. (4) The improvement of labor force, capital investment, international trade level and infrastructure level will aggravate the distribution of APIIs, while technological innovation, environmental regulation and foreign investment can effectively suppress and reduce the output value of regional APIIs. In particular, for every 1% increase in technological innovation, the output value of APIIs will decrease by 0.113%, and it will also have a significant reduction effect on neighboring cities. In the future, Zhejiang needs to fully rely on technological innovation capabilities to drive the development of green industries and the upgrading of traditional manufacturing industries.

Implementation of MQTT protocol based network architecture for smart factory

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique

This paper aims to develop a prediction algorithm for better product quality of the diecasting process, one of the most essential aspects of quality management in traditional manufacturing industries. Shallow neural network (SNN) architecture has been applied as a regressive classifier engine in order to predict product quality of the manufacturing process to renovate the conventional technology adopted by the mainstream fundamental manufacturing industries. The results obtained have been contrasted with classical multilayer perceptron to establish the efficacy of the proposed strategy. In addition to the predictive classifier, various aspects of the research on the prediction algorithm and optimal input selection for successful yield have also been investigated for the aforementioned process using Fuzzy C Means clustering approach. As a case study for validation, the proposed algorithm applies for data set from an aluminum die-casting process. The results reflect that the proposed analytic model can prove to be an effective tool for the prediction and optimization of the product quality of various fundamental manufacturing processes.

Research on the application of big data in regional industrial supply chain

Taking Artemisia industry, the pillar industry of Qichun County as an example, this paper studies the role of big data in the construction of regional industrial supply chain, and uses big data technology to analyze the application of modern information technology in the industry, such as the system of county industrial supply chain, product structure and e-commerce. The main problems of the industrial supply chain in Qichun County are: insufficient application of big data technology in the whole supply chain, low application of big data technology in the traditional manufacturing industry, low application of big data in the tourism and health industry, and low intelligence of the logistics system. It is proposed to use big data and other information technology to optimise the supply chain upgrade of the county industry and establish a complete industrial chain of smart manufacturing, smart logistics, smart tourism and artificial intelligence + big health.

Development and Practice of Cross Border e-Commerce Platform System Based on School Enterprise Cooperation

With the maturity of 5g technology and the development of science and technology. Since then, the Internet has begun to merge with all walks of life, and has created a thriving “Internet plus” economy. The most representative of the Internet plus economy is the e-commerce economy. With the characteristics of “cross-border integration, innovation driven, open and free, connecting everything, fast and convenient”, e-commerce economy has widely penetrated into the traditional manufacturing industry, logistics industry, education industry, government affairs and other fields, and has become the focus of the world’s development. Cross border e-commerce, as a new type of business in e-commerce economy, closely connects the global economy, but also becomes one of the main factors to measure the economic development of various countries. After cross-border e-commerce has become the mainstream of the market, the problems faced are the lack of e-commerce talents, unreasonable talent structure and so on. Based on the cross-border e-commerce platform of school enterprise cooperation, using literature review method and social survey method, cross-border online shopping consumers show an obvious trend of youth, and the proportion of users aged 25-35 is as high as 60%, The proportion of 18-24 years old and 36-46 years old was 13% and 17%.