Search results

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

This paper aims to anticipate the possible development direction of WAAM. For large-scale and complex components, the material loss and cycle time of wire arc additive manufacturing (WAAM) are lower than those of conventional manufacturing. However, the high-precision WAAM currently requires longer cycle times for correcting dimensional errors. Therefore, new technologies need to be developed to achieve high-precision and high-efficiency WAAM.

This paper analyses the innovations in high-precision WAAM in the past five years from a mechanistic point of view.

Controlling heat to improve precision is an effective method. Methods of heat control include reducing the amount of heat entering the deposited interlayer or transferring the accumulated heat out of the interlayer in time. Based on this, an effective and highly precise WAAM is achievable in combination with multi-scale sensors and a complete expert system.

Therefore, a development direction for intelligent WAAM is proposed. Using the optimised process parameters based on machine learning, adjusting the parameters according to the sensors’ in-process feedback, achieving heat control and high precision manufacturing.

This study aims to analyze the moderating effect of managerial discretion on the relationship between cross-border knowledge search and the high-tech firms’ innovation quality in a global health emergency and addresses the following issues: the influence mechanism of different types of cross-border knowledge search on the high-tech firms’ innovation quality in a global health emergency; and the moderating role of different dimensions of manager discretion on the above relationship.

Based on the firms’ strategy selection methods, the authors divided cross-border knowledge search into three aspects, namely, breadth, depth and balance, and analyzed the impact of cross-border knowledge search on the innovation quality of high-tech firms in a global health emergency, taking managerial discretion as the moderating variable, and divided it into position rights, pay rights and operation rights according to the key rights of firms’ strategic management. Furthermore, the authors constructed a theoretical model, and for an analysis sample, the authors collected data from Chinese high-tech firms from 2013 to 2021.

The empirical results show that there is an inverted U-shaped relationship between cross-border knowledge search breadth, depth and balance and the innovation quality of high-tech firms. The position rights, pay rights and operation rights of managerial discretion have partially significant moderating effects on the relationship between cross-border knowledge search breadth, depth and balance and the high-tech firms’ innovation quality.

Considerable literature has grown around the theme of the impact of knowledge search on the firms’ innovation quality. Nevertheless, only a few studies draw on the combination of global health emergency, cross-border knowledge search and the innovation quality; in particular, no literature has analyzed the relationship between the three from the managerial discretion perspective. Exploring the above relationships has great theoretical value for enriching and improving knowledge management and innovation management theories and provides a theoretical basis and practical support for high-tech firms to face challenges of a global health emergency and to break through the innovation dilemma.

This paper aims to investigate the following issues: the mechanisms through which different types of top management team’s social capital influence the innovation quality of high-tech firms, and the moderating effect of organizational knowledge utilization on the relationship between top management team’s social capital and innovation quality in high-tech firms.

This study categorizes top management team’s social capital into political, business and academic dimensions, investigating their impact on innovation quality in high-tech firms. Furthermore, a research model is developed with organizational knowledge utilization as the moderating variable. Data from Chinese high-tech firms between 2010 and 2019 are collected as samples for analysis.

The innovation quality of high-tech firms shows an inverted U-shaped trend as the top management team’s political capital and business capital increase. The top management team’s academic capital has a significantly positive correlation with the innovation quality of high-tech firms. Moreover, organizational knowledge utilization plays a significant moderating role in the relationship between the top management team’s social capital and innovation quality in high-tech firms.

This study explores the relationship among different dimensions of top management team’s social capital, innovation quality and organizational knowledge utilization. It holds significant theoretical value in enriching and refining the interactions between top management team’s social capital, knowledge management theory and innovation management theory. In addition, it offers important practical implications for firms to rationally approach top management team’s social capital, emphasize top management team configuration management and establish a comprehensive and efficient organizational knowledge utilization mechanism.

This study aims to fill the research gap on the moderating effect of leadership empowerment on the relationship between relational capital and firms' innovation performance in the entrepreneurial ecosystem by addressing the following research questions: (1) How do different types of relational capital positively or negatively affect firms' innovation performance in China? (2) Does leadership empowerment play a moderating role in the above relationship?

Using data derived from the firms distributed in eastern, central and western China, the authors study the impact of relational capital, one of the dimensions of intellectual capital, on firms' innovation performance in the entrepreneurial ecosystem. Based on firms' operation process regarding the relationships with their external stakeholders, the authors divided relational capital into three aspects: trust, reciprocity and transparency. Furthermore, leadership empowerment is taken as the moderating variable in the above theoretical relationship.

There is significant evidence that trust, reciprocity and transparency have positive impact on firms' innovation performance. Leadership empowerment positively moderates the impact of trust and reciprocity on innovation performance. However, there is no significant moderating effect of leadership empowerment on the relationship between transparency and innovation performance.

In the era of the knowledge economy, the entrepreneurial ecosystem is a critical foundation for firms to improve their innovation capacity and performance, and intellectual capital is one of the most imperative drivers in terms of firms' innovation performance. Nevertheless, few studies have investigated thoroughly concerning the relationships among the entrepreneurial ecosystem, intellectual capital and innovation performance. As this study explores the relationships among the above three factors, it may have profound theoretical and practical significance for firms to extent external relationship networks, improve their innovation performance and strengthen their core competencies, which is of great significance to facilitate the construction of entrepreneurial ecosystem.

In the context of severe environmental pollution and resource shortage, this study aims to examine how knowledge flows affect the green activities of firms. Specifically, this paper explored whether the firms’ knowledge flows, namely, knowledge inflow (KIF) and knowledge outflow (KOF), play a moderating role in relationship between corporate environmental responsibility (CER) and green innovation in Chinese high-polluting firms.

The analysis was carried out based on a panel data set of 305 heavy-polluting Chinese listed firms from 2010 to 2020. Meanwhile, this paper adopted the fixed model to empirically attest the proposed hypotheses regarding the relationships among CER, knowledge flows and green innovation.

The results indicate that there is a U-shaped relationship between CER and green innovation, while the two dimensions of knowledge flows exert opposing effects on the nonlinear link between CER and green innovation. Specifically, KIF positively moderates the effect of CER on green innovation, whereas KOF negatively moderates the effect of CER on green innovation.

This study demonstrates how green innovation can be influenced by CER and, moreover, provides a more nuanced understanding of the value of knowledge management (KM) in firms’ green activities. In this way, this paper answers the call for understanding the importance of green transformation in the context of KM.

This paper aims to provide a comprehensive review of the state-of–the-art design methods for additive manufacturing (AM) technologies to improve functional performance.

In this survey, design methods for AM to improve functional performance are divided into two main groups. They are design methods for a specific objective and general design methods. Design methods in the first group primarily focus on the improvement of functional performance, while the second group also takes other important factors such as manufacturability and cost into consideration with a more general framework. Design methods in each groups are carefully reviewed with discussion and comparison.

The advantages and disadvantages of different design methods for AM are discussed in this paper. Some general issues of existing methods are summarized below: most existing design methods only focus on a single design scale with a single function; few product-level design methods are available for both products’ functionality and assembly; and some existing design methods are hard to implement for the lack of suitable computer-aided design software.

This study is a useful source for designers to select an appropriate design method to take full advantage of AM.

In this survey, a novel classification method is used to categorize existing design methods for AM. Based on this classification method, a comprehensive review is provided in this paper as an informative source for designers and researchers working in this field.

The purpose of this paper is to evaluate the forecasting performance of grey prediction models on educational attainment vis-à-vis that of exponential smoothing combined with multiple linear regression employed by the National Center for Education Statistics (NCES).

An out-of-sample forecasting experiment was carried out to compare the forecasting performances on educational attainments among GM(1,1), GM(1,1) rolling, FGM(1,1) derived from the grey system theory and exponential smoothing prediction combined with multivariate regression. The predictive power of each model was measured based on MAD, MAPE, RMSE and simple F-test of equal variance.

The forecasting efficiency evaluated by MAD, MAPE, RMSE and simple F-test of equal variance revealed that the GM(1,1) rolling model displays promise for use in forecasting educational attainment.

Since the possible inadequacy of MAD, MAPE, RMSE and F-type test of equal variance was documented in the literature, further large-scale forecasting comparison studies may be done to test the prediction powers of grey prediction and its competing out-of-sample forecasts by other alternative measures of accuracy.

The findings of this study would be useful for NCES and professional forecasters who are expected to provide government authorities and education policy makers with accurate information for planning future policy directions and optimizing decision-making.

As a continuing effort to evaluate the forecasting efficiency of grey prediction models, the present study provided accumulated evidence for the predictive power of grey prediction on short-term forecasts of educational statistics.

The key to the success of multinational corporations’ (MNCs) business models is the improvement of their innovation quality. From the cross-border knowledge management perspective, this paper aims to analyze the improvement path of innovation quality of MNCs and construct the functional path of the relationships among the knowledge creation, knowledge application and innovation quality of MNCs in the cross-border knowledge management process, so as to achieve the success of their business models. Based on this, this paper introduces cultural distance to further analyze how such relationships will change with the cultural distance level.

Using data from Chinese A-share listed MNCs with production operations located in the Asia-Pacific region from 2014 to 2018, this paper constructs a panel data model to test the mediating effect of knowledge application and the moderating effect of cultural distance on such relationships.

This paper obtains the following research findings: knowledge creation and knowledge application each have a significant, inverted U-shaped relationship with innovation quality; knowledge creation has a significantly positive correlation with knowledge application and knowledge application has a partial mediating effect on the relationship between knowledge creation and innovation quality; cultural distance has a moderating effect on such relationships. The specific moderating direction depends on the extent of the knowledge creation and knowledge application.

The findings are helpful to MNCs’ managers, providing guidance and serve as a reference for them to make strategic decisions on cross-board knowledge management and business models innovation.

The theoretical contributions are summarized as follows: First, it further enriches and expands the theoretical of knowledge management and innovation quality relationship. Second, it further enriches and expands the theoretical framework of knowledge management. Third, it further enriches the theoretical framework of cross-cultural management.

Assembly accuracy is the guarantee of mechanical product performance, and the characterization of the part with geometrical deviations is the basis of assembly accuracy analysis.

The existed small displacement torsors (SDT) model cannot fully describe the part with multiple mating surfaces, which increases the difficulty of accuracy analysis. This paper proposed an integrated characterization method for accuracy analysis. By analyzing the internal coupling relationship of the different geometrical deviations in a single part, the Monomer Model was established.

The effectiveness of the Monomer Model is verified through an analysis of a simulated rotor assembly analysis, and the corresponding accuracy analysis method based on the model reasonably predicts the assembly deviation of the rotor.

The Monomer Model realizes the reverse calculation of assembly deformation for the first time, which can be used to identify the weak links that affect the assembly accuracy, thus support the accuracy improvement in the re-assembly stage.