Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

Analysis on Technical Structure and Competitiveness of ICT Manufacturing Industries

In order to analyze the technical structure and international comparative advantage of the information and communication technology(ICT)manufacturing industry,a complete set of ICT manufacturing product categories has been constructed by matching National Economical Industry Classification(GB/T4754-2017)with Harmonized System(HS)Codes,based on the relevant definitions in International Standard Industrial Classification(ISIC).The proposed definition overcomes inherent defects such as inaccurate scopes,lagging data and rough categories,which are characterized by commonly utilized product-level based classification approaches.Within the given framework,this paper has designed the technology content related indicators from the perspective of production distribution,and divided ICT product categories into high-end,medium-end and lowend manufacturing classifications according to respective global shares.Then,we have calculated international market shares(IMS),revealed comparative advantages(RCA),and market penetration rates(MPR)of ICT manufacturing exports for major economies from 2010 to 2021.Finally,development characterizations of ICT manufacturing industries for China’s Mainland are analyzed,and several practical suggestions are provided.

Research on the Digital Economy,Technological Innovation,and Transformation and Upgrading of the Manufacturing Industry in Shandong Province

Based on panel data from 16 cities in Shandong Province from 2013 to 2022,and grounded in theoretical analysis,this paper constructs a benchmark regression model,a mediating effect model,and a heterogeneity test for empirical analysis.The results show that:(1)the digital economy has a significant positive effect on promoting the transformation and upgrading of the manufacturing industry in Shandong Province;(2)the digital economy can drive the transformation and upgrading of the manufacturing industry through technological innovation;and(3)the impact of the digital economy on the transformation and upgrading of the manufacturing industry in Shandong Province is evidently heterogeneous.

The Application of Knowledge Management(KM)by Small and Medium-Sized Enterprises(SME)in Manufacturing Industry

To accelerate the digital transformation of small and medium-sized manufacturing enterprises(SMEs),this study delves into the primary challenges encountered in adopting knowledge management(KM)within these organizations and identifies the essential methods for successful implementation.The objective is to provide practical recommendations for the effective adoption of KM.This research suggests that enterprises should promote knowledge management through three key approaches:enhancing employees’cognitive understanding,standardizing knowledge systems,and tailoring business scenarios to meet diverse needs.These findings offer valuable insights into the digital transformation of SMEs in the manufacturing sector,ultimately helping these businesses to remain competitive and innovative in a rapidly changing market.By addressing the specific needs and challenges faced by SMEs,this study aims to contribute to a more comprehensive understanding of how knowledge management can be leveraged to drive digital transformation and improve overall business performance.

Research on the Industrial Transfer and Restructuring Path of the Energy-Intensive Manufacturing Industry in Western China

The orderly transfer of the manufacturing industry is a major action in China’s industrial restructuring.From the perspective of industrial transfer,we used the concentration ratio to depict the trend of the industrial transfer of energy-intensive manufacturing in the eastern,central,and western regions since the policy of large-scale development of western China was implemented.We measured the total factor productivity(TFP)of western China using the DEAMalmquist index method.We conducted a regression analysis to measure the effect of western China’s undertaking of the transfer of the energy-intensive manufacturing industry.The findings of this study show that during 2000–2019,eleven provinces(as well as autonomous regions and municipalities)in western China undertook the transfer of the energy-intensive manufacturing industry from the eastern and central regions to varying degrees,exhibiting significant phase features regarding the rate and scale of transfers.Further investigation also demonstrated that the transfer of energy-intensive manufacturing industries has a U-shaped enabling effect on TFP in western China with the scale effect greater than the technology effect.Therefore,it is necessary to transition from“extensive industrial transfer”at the cost of the labor force,land,and resources to“modern industrial transfer”featured by technology and efficiency improvements to contribute to industrial restructuring in western China effectively.

The Effects of Industrial Park Development on Manufacturing Firms’Performance in Ethiopia

This study examines the effects of industrial parks on export earnings,employment creation,and FDI attraction in Ethiopia.Despite varying degrees of successes and failures,several countries have utilized industrial park and other forms of special economic zones as a policy instrument for fostering economic transformation.China is at the forefront of using special economic zones as a policy tool for economic transformation.Ethiopia is one of the African countries that has adopted industrial park development as a policy tool to enhance its economic transformation.However,the issue is not well researched and this study aims to contribute to fill the research gap.The analysis of the hypotheses test reveals that industrial parks in Ethiopia have statistically significant effects on export earnings,employment creation,and FDI attraction with significant levels of p≤0.001.Low labor productivity,domestic raw material supply constraints,weak forward and backward linkage,transport cost and logistic constraints,and government institutions’capacity constraints are identified as the major constraints that affect the effectiveness of industrial parks and manufacturing firms.On the other hand,the availability of a trainable labor force,raw material potential,preferential policies and incentives,the economic growth of the country,and the labor wage rise in China and other emerging countries are identified as the main five potentials and opportunities for sustained and dynamic industrial parks development.Based on the findings,three policy implications are suggested.First,formulating and implementing manufacturing labor force development and utilization policies and strategies are vital.Second,the forward-backward linkage along the value chain needs to be enhanced through proper policies.Finally,institutional capacity building through learning by doing and public-private partnership has to be strengthened.

An Application of Non–Linear Cobb-Douglas Production Function to Selected Manufacturing Industries in Bangladesh

Recently, businessmen as well as industrialists are very much concerned about the theory of firm in order to make correct decisions regarding what items, how much and how to produce them. All these decisions are directly related with the cost considerations and market situations where the firm is to be operated. In this regard, this paper should be helpful in suggesting the most suitable functional form of production process for the major manufacturing industries in Bangladesh. This paper considers Cobb-Douglas (C-D) production function with additive error and multiplicative error term. The main purpose of this paper is to select the appropriate Cobb-Douglas production model for measuring the production process of some selected manufacturing industries in Bangladesh. We use different model selection criteria to compare the Cobb-Douglas production function with additive error term to Cobb-Douglas production function with multiplicative error term. Finally, we estimate the parameters of the production function by using optimization subroutine.

Geographical Agglomeration of Chinese Manufacturing Industries

Based on the plant level data from the First National Census of Basic Units, the Second National Census of Basic Units, and the First National Economic Census of China, this paper ex- amines the geographical agglomeration of Chinese manufacturing industries at a more disaggregated level both by industrial classi-fication and spatial unit analysis. Applying the agglomeration index proposed by Maurel and Sedillot, studies show that the most agglomerated industries are resource-intensive, and capital and technology intensive while industries demanding localized inputs or serving localized markets or favored by local governments are fairly dispersed. The more disaggregated industries are more spa- tially agglomerated. At the finer spatial scale, industries are more dispersed. Results indicate that within-county spillovers are stronger than nearby-county spillovers, and within-prefecture spillovers are stronger than nearby prefecture spillovers. Local- ized spillovers are still quite substantial at a range beyond that of counties. Comparing the agglomeration index of Chinese manu- facturing industries in 1996, 2001 and 2004, agglomeration seems to be a general tendency. Results also indicate that some industries have experienced remarkable changes in their levels of agglom- eration in the period 1996–2004.

Evaluation of Role of Home Market Effects in China′s Manufacturing Industries

Under the theoretical framework of the New Trade Theory and the New Economic Geography, Home Market Effects (HMEs) is considered to be important sources of comparative advantage and significant reasons for industry agglomeration. Through the analysis on the input-output tables in China, the paper confirms the existence of HMEs for the manufacturing industries and their export trades on the national and provincial level in China. Several conclusions have been drawn in this study. Firstly, there exist prominent HMEs for manufacturing industries related with nondurable goods and materials in China. Secondly, 10 of the 15 kinds of manufacturing industries considered in this study display the existence of HMEs. Thirdly, the comparative advantage of factor endowment for the export trade of manufacturing industries is declining, while HMEs for them are increasing. Fourthly, among the 30 provincial regions studied in this paper, 11 regions show the existence of HMEs, most of which are located in the eastern coast region. This paper illustrates the simultaneous existence of the comparative advantage of factor endowment and scale economies of HMEs for China's manufacturing industries. HMEs will not only be the new dynamic for the increase of manufacturing industries and their export trade, but also be the primary power for urban economic growth, industry improvement, and urban space expansion for China.

Application of Non-Linear Cobb-Douglas Production Function with Autocorrelation Problem to Selected Manufacturing Industries in Bangladesh

In developing counties, efficiency of economic development has been determined by the analysis of industrial production. An examination of the characteristic of industrial sector is an essential aspect of growth studies. The growth of a country can be measured by Gross Domestic Product (GDP). GDP is substantially affected by the industrial output. Industrial gross output is mainly a function of capital and labor input. If the effect of labor and capital input to output is at a satisfactory level in an industry or in a group of industries, then industrial investment will increase. As a result, the number of industries will increase, which will directly affect GDP and also will decrease the unemployment rate. This is why, industrial input-output relationship is so important for any industry as well as for the overall industrial sector of a country. To forecast the production of a firm is necessary to identify the appropriate model. MD. M. Hossain et al. [1] have shown that Cobb-Douglas production function with additive errors was more suitable for some selected manufacturing industries in Bangladesh. The main purpose of this paper is to detect the autocorrelation problem of Cobb-Douglas production model with additive errors. The result shows that autocorrelation is presented in some manufacturing industries. Finally, this paper removes the autocorrelation problem and re-estimates the parameters of the Cobb- Douglas production function with additive errors.

Productivity Growth, Technological Progress, and Efficiency Change in Vietnamese Manufacturing Industries: A Stochastic Frontier Approach

This study applies a stochastic frontier production approach to decompose the sources of total productivity (TFP) growth into technical progress and changes in technical efficiency of 8057 firms in Vietnamese manufacturing industries during 2003-2007. Using both total manufacturing industry and sub-manufacturing industrial regressions, the analysis focuses on the trend of technological progress (TP) and technical efficiency change (TEC), and the role of productivity change in economic growth. According to the estimated results, the annual technical progress for the manufacturing industry and sub-manufacturing industries are calculated directly from the estimated parameters of the translog stochastic frontier production function by taking a partial derivative of output with respect to time t. The average technical changes in manufacturing industry and sub-manufacturing industries are positive, with an average technical change about 5.2%, 5.8%, 5.4%, 11.8%, 4.6%, 4.1%, 7.3%, 4.8%, 4.8% and 4.8% for total sample, food products & beverages, textile & wearing apparel, footwear, paper & products, industrial chemicals, rubber & plastic products, non- metallic mineral, basic & fabricated metal and other sub-industries, respectively. Total TFP in the manufacturing sector has grown at the annual rate of 0.052, although the rate of growth decreased continuously during the sample period. For the sub-industry estimates during the sample period, TFP grew fastest in the footwear sub-industry, with annual average growth rate of 11.8%, followed by the rubber & plastic products with a rate of 7.3%, and the food products & beverages with a rate of 5.8% per annum.

Software Selection in Manufacturing Industries Using a Fuzzy Multiple Criteria Decision Making Method, PROMETHEE

This paper presents an effective decision making framework for software selection in manufacturing industries using a multiple criteria decision making method, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The method is improved in the present work by integrating with analytic hierarchy process (AHP) and the fuzzy logic. Fuzzy logic is introduced to handle the imprecision of the human decision making process. The proposed decision making framework is practical for ranking competing software products in terms of their overall performance with respect to multiple criteria. An example is included to illustrate the approach.

Controversy of International Carbon-motivated Border Tax Adjustment and Its Impact on China's Manufacturing Industries

Carbon-motivated border tax adjustment （BTA） aims to compensate for the loss of competitiveness of carbon intensive products due to carbon dioxide abatement actions. Based on the analysis of the international background of carbon-motivated BTAs, this paper discusses the fundamental motivation leading to US policy transformation, the potential impacts of the policy on China＇s manufacturing industries, and the compatibility of the policy to WTO rules. Carbon-motivated BTAs violate the fundamental principle of the UNFCCC, and potentially conflict with the core WTO principle of non-discrimination reflected in the GATT Articles 1 and III. However, Article XX of the GATT may be applicable. Thus, the author suggests several measures to alleviate the impacts of carbon-motivated BTAs, and puts forward countermeasures based on carbon consumption per capita.

The possibilities of entering new markets for Romanian companies from wood manufacturing industries

Romania has the potential of resources, labor and real capacity to adapt to the gait of a global economic competition. But it required a long term policy and environment which could support this potential value. If not, we will risk turning into a consumer economy, consumption will not be funded on internal production and become supplier of labor services for peripheral. In the context of economic crisis, Romanian furniture retains its significant competitive advantages, including the price in export markets. The most important markets for exporters of furniture from Romania are Ukraine and Russia, markets with great potential, but affected by problems such as devaluing local currencies, and recently, imposing new duties.

Reflection and Study on the Transfer of International Manufacturing Industries and Chinese Sustainable Development

Ever since the 1960s, especially 1980s, under the background of quickening pace of economic globalization and the regional division of labor around the world, the transfer of manufacturing industries among countries and areas gets great momentum and impetus. During 20-plus years in which China has adopted the policy of opening-up and reforming, our country has played an active role in taking part in the transfer of manufacturing industries. It advances the structural change of national manufacturing industries, accelerates national industrialism and promotes the comprehensive development of regional economics. In this paper, the author makes some initial study on the regularity of transfer of International manufacturing industries, discusses the profound impact of such shift on our national resources, environment, economics and society, and also proposes corresponding suggestions on how to implement the strategy of sustainable development in the process of the shift.

Spatial Evolution and Locational Determinants of High-tech Industries in Beijing

Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.

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.

Parallel Manufacturing for Industrial Metaverses:A New Paradigm in Smart Manufacturing

To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems(CPSSs) and the Internet of Minds(Io M) are regarded as its infrastructures and the "Artificial systems", "Computational experiments"and "Parallel execution"(ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R&D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge Automation(KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations(DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case,especially the unmanned production line named Flex Vega.

Fog-IBDIS:Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.