Intelligent Manufacturing Industry Research Articles

Fostering career crafting by developmental HR practices: the mediating role of future work self and moderating role of AI awareness

PurposeThe deep integration of emerging technologies such as artificial intelligence (AI) has triggered a transformation in career patterns, requiring employees to promptly adapt to changes in the vocational environment through career crafting. While human resource (HR) practices play a role in guiding employees' career self-management, further exploration is needed to determine the specific HR practices that organizations should adopt to facilitate employees' career crafting. This research takes developmental HR practices as a new premise for career crafting and examines the underlying mechanisms influencing the relationship between the two.Design/methodology/approachUsing a three-stage time lag method with a two-month lag in each stage, we collected sample data from 386 employees in the intelligent manufacturing industry and employed multiple regression analysis to test the research hypotheses.FindingsThe results of this paper show that developmental HR practices positively influence employees' career crafting behaviors through their future work self and that AI awareness moderates this relationship.Originality/valueDrawing upon career construction theory, we propose that developmental HR practices cultivate employees' future work self, thereby eliciting career crafting behaviors. Moreover, the mediating role of future work self is more pronounced when employees have a high level of AI awareness, as this awareness stimulates self-cognition-driven behaviors. These findings enrich the understanding of career crafting by shedding light on the career construction process in the relationship between developmental HR practices and career crafting and by identifying the boundary conditions for the relationship.

Application of metal additive testing and thermal conductivity technology in optimizing product appearance design in intelligent manufacturing industry

Metal additive manufacturing technology has the advantages of rapid manufacturing and personalized production, but due to the complexity of the manufacturing process and differences in material characteristics, products made with metal additives often have appearance defects and process performance issues. Therefore, the purpose of this study is to explore the application of metal additive detection technology in product appearance design and process performance optimization. This study proposes a comprehensive detection and optimization method that utilizes non-destructive testing techniques to comprehensively inspect metal components. By identifying and locating these issues, the quality status of the components can be accurately understood. Based on the test results, a series of appropriate optimization measures have been taken to improve the appearance and process performance of the product, adjust process parameters, improve the forming quality and surface smoothness of the components, thereby achieving better appearance effects. Metal additive testing technology can effectively detect and locate appearance defects in products, and by optimizing process parameters and material selection, it can significantly improve the appearance quality and process performance of products, providing support for improving the reliability and production efficiency of industrial manufacturing.

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

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

Exploration and Research on the Diversified Collaborative Education Model of Intelligent Manufacturing Professional Groups under the Background of the Integration of Suzhou Wuxi Changzhou Urban Vocational Education Circle

With the rapid development of global economic integration and intelligent manufacturing, vocational education plays an important role in cultivating high-quality technical and skilled talents. As an important base for China’s economic development and intelligent manufacturing industry, the development of vocational education in the Suzhou Wuxi Changzhou metropolitan area is particularly crucial. This article analyzes the current development status and existing problems of the Suzhou Wuxi Changzhou Urban Vocational Education Circle, constructs a diversified collaborative education model for intelligent manufacturing professional groups, and verifies the effectiveness of this model in improving education quality and promoting industrial development through empirical research and case analysis. Finally, the possible challenges and response strategies in the implementation process are discussed.

Evaluation and Selection of Manufacturing Suppliers under the Perspective of Smart Manufacturing Combined with Bilevel Programming

To address the issue of supplier evaluation and selection in the current intelligent manufacturing industry, a supplier intelligent evaluation and selection model combining bilevel programming is proposed. Firstly, based on existing research results and the current situation of the manufacturing market, a manufacturing supplier evaluation index system is constructed, and the principal component analysis method is used to simplify it; Then, the simplified indicator system is input into the back propagation neural network to build a supplier intelligent evaluation model and achieve supplier intelligent evaluation; A mathematical model for supplier selection problem is constructed based on bilevel programming, and an optimized sparrow search algorithm is put forward to solve it. A supplier intelligent selection model is constructed to achieve intelligent supplier selection. The lab findings denote that the F1 value of the supplier intelligent evaluation model is 0.953, the accuracy is 0.985, the area under the curve is 0.962, and the error of the output comprehensive evaluation value is 0.003; The output accuracy of the supplier intelligent selection model is 0.8. The above results indicate that the model raised in the article has good application effects in supplier evaluation and selection, and has a certain promoting effect on the development of the intelligent manufacturing industry.

Analysis of the Connotation and Practice of the Integration of Production and Education under the Background of Intelligent Manufacturing

Intelligent manufacturing is the focus and trend of China’s industrial development. In the context of intelligent manufacturing, the amalgamation of industry and education is the only way of vocational education. The integration of production, education and research is no longer a superficial and single school—enterprise cooperation. It’s a pattern, a thought and an idea. It is only through the whole process of the reform and development of vocational education that the concept of the integration of industry and education can be carried out. In order to provide more and better professional talents for the fast iterative intelligent manufacturing industry, the essence of the integration of industry and education is under the background of intelligent manufacturing. Relying on the new concept and model of vocational education integration, vocational school students can realize individual freedom and all-round development, and everyone has the opportunity to be brilliant in life.

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

Big Data Enables the Intelligent Manufacturing Industry

With the rapid development of big data technology, the intelligent manufacturing industry is ushering in unprecedented opportunities. Big data enables intelligent manufacturing, helping to achieve more accurate demand forecasting, production planning and supply chain management, and improve production efficiency and product quality. At the same time, big data analysis can provide real-time monitoring and early warning system, timely find and solve problems in the production process, and reduce operating costs. Through big data technology, intelligent manufacturing enterprises can better grasp consumer needs, optimize product design, provide personalized customized services, and enhance market competitiveness. In the future, big data will continue to play an important role in intelligent manufacturing, helping enterprises to realize digital transformation and upgrade the industrial value chain.

Exploration and Reflection on the Construction of Intelligent Manufacturing Professional Clusters for Industrial Internet

In the trend of the transformation of the manufacturing industry towards intelligence promoted by industrial internet technology, the traditional curriculum system related to the manufacturing industry cannot meet the requirements of society and industry for talent knowledge structure and skill training, which puts forward new requirements for undergraduate major construction and development. This article relies on the advantages of the industrial internet location in the Yangtze River Delta region, combined with the needs of regional industrial development, and the problems in the current process of intelligent manufacturing professional construction. Taking the construction of the intelligent manufacturing major cluster at the Nanjing University of Science and Technology ZiJin College as an example, in response to the needs of the development of the intelligent manufacturing industry and the problems in the current process of cultivating intelligent manufacturing talents, this article focuses on the construction of the teaching staff, revision of the curriculum system, construction of scientific research and virtual simulation teaching platforms, multi-disciplinary and multi-angle collaborative education models, providing reference for the reform of applied engineering and technology talent cultivation models in the manufacturing industry in the era of industrial internet.

Analysis and Research on Integration Issues of Intelligent Manufacturing and Service Industry

Analysis and Research on Integration Issues of Intelligent Manufacturing and Service Industry

In-mold condition-centered and explainable artificial intelligence-based (IMC-XAI) process optimization for injection molding

This paper proposes a novel injection molding process optimization method based on the in-mold condition (IMC) and interpreted influence of in-mold condition features on part quality. In-mold condition is crucial for process optimization because it represents the actual process condition in the cavity where the polymer material is formed into the final part shape. Traditionally, the analysis of in-mold condition heavily depends on domain knowledge and insight, which introduces bias and inconsistency in in-mold condition-based process optimization. This study aims at developing an intelligent, objective, and robust process optimization method that concentrates on the highly influential in-mold condition features concerning part-quality, as interpreted by explainable artificial intelligence (XAI). In this in-mold condition-centered modeling approach, the input process parameters and final part quality were associated with the in-mold condition, with their corresponding relationship modeled by two machine learning (ML) models, respectively. The effect of in-mold condition on part quality was interpreted by applying XAI on the ML model that describes the relationship between in-mold condition and part quality. Features in the in-mold condition profiles that have high influence on part quality are given more weight in the search for diverse in-mold conditions that satisfy multiple part-quality objectives. A feasibility check has been implemented to identify, among those potential in-mold conditions, the optimal one that is physically feasible based on the ML model that governs the relationship between the process parameters and in-mold condition. The proposed method not only pinpoints better optimized process parameters than the conventional approach that omits the in-mold condition and only considers the direct relationship between the process parameters and part quality, but also reveals the possibility of further quality improvement. The optimization tool of the proposed method can be found on an online interactive platform https://imc-xai-injmold-optimization-4e26a3e3ef41.herokuapp.com/, which was created to facilitate further research based on the proposed approach. In addition to process optimization, this approach can effectively contribute to intelligent manufacturing management and Industry 4.0.

BTIA-IME: A blockchain-based trusted interactive architecture for intelligent manufacturing equipment

With the continuous development of artificial intelligence, big data, and blockchain technology, the intelligent manufacturing industry will enter a new 5.0 era. In Industry 5.0, attackers can take advantage of the hidden dangers of information transmission among infrastructure, such as smart factories (SF) and intelligent manufacturing equipment (IME), to steal private data, endanger personal safety, and harm the interests of factories. In order to avoid the above phenomenon, this paper proposes a blockchain-based trusted interactive architecture for intelligent manufacturing equipment (BTIA-IME) based on the device layer of the Industrial Internet of Things (IIoT). First, we design the key management smart contract (KMSC) and equipment management smart contract (EMSC) based on Elgamal to implement the IME trusted authentication mechanism that does not require three-party verification to ensure the credibility of IME in SF. Then, we design the data management smart contract (DMSC) based on Elgamal to store the privacy data structure through the “Blockchain-(InterPlanetary File System) IPFS” on-chain and off-chain storage modes to ensure the distributed and trusted storage of private data. After that, an access control list is constructed with the shared private data as the main body to implement the trusted access method of private data to ensure trusted access to private data. Finally, we perform simulation experiments on the proposed architecture. Theoretical and simulation experiments have proved that our proposed architecture is feasible.

A Method for Identifying Energy Consumption of Machine Tools Based on Integrated Models

The uncertainty of the dynamic change process of expenditure of energy from machine tools seriously hinders the development of an intelligent and green manufacturing industry. In response to the multifaceted impact of identification methods in the process of energy consumption change, the difficulty of extracting high-quality features, and the complexity and nonlinearity of energy consumption curves, an identification method for predicting the energy consumption of machine tools based on integrated models was proposed. Taking CNC milling as an example, the model of energy consumption based on different cutting periods was established, which was then trained and predicted by using the preprocessing signal based on Random forest (RF). Meanwhile, the RF was used to identify the cutting stages and realize the energy consumption classification prediction. The advantage and availability of this method have been demonstrated through practical cases, and it has been verified that this recognition method can accurately predict different operating states and energy consumption of each stage on machine tools.

Examining the Effectiveness of Housing and House Registration Policies for Raising Inflow of Talents A Case Study in Zhangjiang Science City, Shanghai

With the national economic transformation, the advance of the intelligent manufacturing industry is gradually approaching, which has a direct positive correlation with innovations and the development of high-tech industries. Sufficient technical and entrepreneurial talent supply is the key to maintaining a high level of technological progress and sustaining economic development. Therefore, as one of the most developed cities in China, Shanghai adopts several house-related policies to attract the inflow of advanced talent. This paper examines the effectiveness and feasibility of the housing and household registration policies implemented in Zhangjiang Science City, including shortening the time of transferring temporary residence permit into the registered permanent residence (TR-to-PR) and offering talents apartments to reduce rental pressure of young talents. Four simple quantitative indicators used to analyze the effectuality are the number of talents introduced annually and its growth rate; the number of TR-to-PR groups annually and its growth, respectively. As a result, this paper supports that the policies are effective in promoting talent inflow. However, the future talent retention issue is still alarming and it deserves the attention of policymakers. More policies concerning solving house price bubbles and improving the living environment should be adopted to keep talents stay and contribute to economic development in the long run.

Innovation network, knowledge absorption ability, and technology innovation performance--An empirical analysis of China's intelligent manufacturing industry.

Based on the social network theory, this study utilizes knowledge absorption capacity as the mediating variable and technology turbulence as the moderating variable; furthermore, it focuses on China's intelligent manufacturing industry data to explore the effect of the intelligent manufacturing enterprise innovation network on technology innovation performance and the regulating mechanism of technology turbulence. Based on the patent data obtained from Derwent Database (survey period: 2016-2020), the empirical analysis indicates the following: (1) Network relationship, network location, and network density are significantly and positively correlated with technology innovation performance; however, network size exerts no significant effect on technology innovation performance. (2) Network relationship strength, network location, and network density exert significantly positive effects on the two dimensions of knowledge absorption capacity, namely the In-degree and the Out-degree. Network size exerts no significant effect on knowledge absorption capacity. (3) Knowledge absorption capacity exerts a partial mediating effect on the relationship between innovation network and technology innovation performance. (4) The three dimensions of innovation network that exert a significant effect on technology innovation performance are positively correlated with the interaction terms of technology turbulence, which indicates that the interaction terms, namely innovation network and technology turbulence, exert a positive impact on technology innovation performance through knowledge absorption capacity, and that the moderating effect of technology turbulence exerts a role through knowledge absorption capacity. Finally, this study postulates implementations and policy proposals for enhancing the innovation performance of intelligent manufacturing enterprises.

Precise geometry and pose measurement of in-hand objects with simple features using a multi-camera system

In precision pick-and-place tasks, robotic grasp errors can cause the failure of subsequent placement steps. Therefore, in-hand object pose measurement has attracted the attention of the intelligent manufacturing industry. However, existing algorithms have limited accuracy and insufficient practicality. Moreover, they often assume that the actual object geometry is identical to the CAD model. To alleviate the problems in terms of accuracy, practicality, and uncritical assumptions, this paper proposes an in-hand measurement solution based on multi-view reconstruction for objects with simple features. In this concept, multiple monocular cameras are deployed at the robot end to calculate the object’s actual geometry and 6D pose by reconstructing its key geometric features. An in-hand pose compensation strategy and a robot terminal system integrated with manipulation and measurement are developed. To verify the solution’s effectiveness, several experiments were carried out. First, grasp errors caused by multiple sources are intuitively demonstrated in a simulation environment. The in-hand measurement is validated to compensate for grasp errors through comparison experiments. Then, robots can rearrange various irregularly piled objects in real-world experiments based on the developed terminal system. Results demonstrate that maximum remaining position errors are within 2 mm with maximum angle errors of less than 1°. The maximum error compensation rate reaches 95%, which is superior to existing in-hand measurement algorithms. It can be observed that the proposed solution is valid for multiple objects. Geometry measurement can help robots calculate the in-hand object pose and identify the object’s category and availability. Therefore, the proposed measurement solution seems accurate and universal for robot applications in smart manufacturing industries, enhancing the accuracy and flexibility of robot manipulation.

Fault propagation analysis of computer numerically controlled machine tools

Computer numerically controlled machine tools are the key equipment of the intelligent manufacturing industry, unscheduled shutdowns can substantially cause equipment damage and production loss. Fault propagation analysis, as a prominent basis of reducing unscheduled downtime, can locate the harmful parts and reveal the fault propagation path. To improve reliability, this paper proposes a novel fault propagation analysis method that can accurately and timely evaluate fault propagation coefficients of components in interior computer numerically controlled machine tools. To depict the latent cause–effect relationships of components, a quantitative causal diagram is built with the aid of design information and topology. An inverse PageRank algorithm is proposed to assess the fault propagation risks of components. Unlike the traditional PageRank algorithm, the idea of inverse PageRank is that a node is crucial if it directs to other important nodes. Subsequently, the fault propagation coefficients of components in the quantitative causal diagram are derived and the fault propagation path is identified. The proposed method is validated on the computer numerically controlled machine tool, and the results are basically consistent with the typical cascading events and conform to expert judgment.

Reconfigurable intelligent surface with 6G for industrial revolution: Potential applications and research challenges

Abstract With proper tuning in the phase shift of reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), which consist of huge number of passive reflecting elements, are the most promising and transforming technology to reconfigure the wireless propagation ecosystems to improve the “spectral-and energy-efficiency” for the future generation wireless communication system. This article presents a systematic review of RIS/IRS technology and conceptualizes the prospective requirement, operations, and application. Further, we have presented its capability for execution in a forthcoming smart industrial ecosystem to assist the development of Industry 5.0 with a series of emerging applications. Finally, we discuss the potential, open research challenges, and prospects of RIS technology in the emerging intelligent manufacturing industry to encourage future research in this area.

Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions

Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.

Application and Prospect of Intelligent Manufacturing in Practice

Intelligent manufacturing is the core technology paradigm of the new round of scientific and technological revolution, and the main direction of strengthening the integration of digital and real technology and building a manufacturing power. This paper focuses on intelligent manufacturing. Take intelligent mechanical engineering, intelligent logistics, industrial robots and intelligent production workshops as examples. Exploring the phased achievements and applications of intelligent manufacturing technology. Research showed that the realization of intelligent manufacturing has adopted technologies such as the Internet of Things, big data, cloud computing, machine learning, intelligent sensing, interconnection and remote operation and maintenance, effectively solving many current engineering and scientific research problems. Indicating that the intelligent manufacturing industry supported by information technology and advanced digital technology has broad application prospects. It also provides reference for the application and high-quality development of intelligent manufacturing.