Search results

Selective laser melting (SLM) is a major additive manufacturing (AM) process in which laser beams are used as the heat source to melt and deposit metals in a layerwise fashion to enable the construction of components of arbitrary complexity. The purpose of this paper is to develop a framework for accurate and fast prediction of the temperature distribution during the SLM process.

A fast computation tool is proposed for thermal analysis of the SLM process. It is based on the finite volume method (FVM) and the quiet element method to allow the development of customized functionalities at the source level. The results obtained from the proposed FVM approach are compared against those obtained from the finite element method (FEM) using a well-established commercial software, in terms of accuracy and efficiency.

The results show that for simulating the SLM deposition of a cubic block with 81,000, 189,000 and 297,000 cells, the computation takes about 767, 3,041 and 7,054 min, respectively, with the FEM approach; while 174, 679 and 1,630 min with the FVM code. This represents a speedup of around 4.4x. Meanwhile, the average temperature difference between the two is below 6%, indicating good agreement between them.

The thermal field for the multi-track and multi-layer SLM process is for the first time computed by the FVM approach. This pioneering work on comparing FVM and FEM for SLM applications implies that a fast and simple computing tool for thermal analysis of the SLM process is within the reach, and it delivers comparable accuracy with significantly higher computational efficiency. The research results lay the foundation for a potentially cost-effective tool for investigating the fundamental microstructure evolution, and also optimizing the process parameters in the SLM process.

The quality and properties of Inconel 718 (IN718) from selective laser melting (SLM), a major additive manufacturing (AM) process, have been studied extensively. Among all aspects of quality, relative density (RD) is most widely investigated, and it significantly affects the mechanical properties of SLM-ed materials. This study aims to develop robust RD prediction models based on the data accumulated in literature using machining learning approaches.

By mining the literature of SLM-ed IN718, a comprehensive data set is created, which consists of the four major process parameters of laser power, scan speed, hatch spacing, layer thickness and RD data. A back propagation neural network (BPNN) model, along with its two optimized models: genetic algorithm (GA) optimized BPNN (GA-BPNN) and adaptive GA optimized BPNN (AGA-BPNN) models are created for predicting the RD of SLM-ed IN718, and their prediction performances are compared.

Overall, satisfactory prediction accuracies are obtained – the R² values of the built BPNN, GA-BPNN and AGA-BPNN models are 73.5%, 75.3% and 79.9%, respectively. This also shows that by incorporating the optimization technique, the prediction accuracy of BPNN is improved and AGA-BPNN has the highest accuracy. Moreover, SLM experiments are conducted to test the model predictability. It is found that the predictions generally agree well with the experiment data, and the order of the model prediction accuracies is consistent with that based on the literature data.

This research highlights that by mining literature data, prediction models of RD of SLM-ed IN718 can be obtained with satisfactory performance, which consider more process parameters and cover wider parameter ranges than any individual studies, in a cost-effective manner.

Internal audit executives instruct the internal audit department to supervise corporate business management activities, evaluate internal controls and risks and provide recommendations for operating. Therefore, this paper aims to confirm whether and how the supervisory ability of the chief internal audit executive enhances the internal audit department’s function to prevent corporate fraud. Based on the results, this paper further researches the role of the supervisory board position in this relationship.

This paper examines 922 small and medium-sized listed enterprises in China from 2010 to 2017 and empirically investigates the influence of the internal audit executive’s supervisory ability (IAESA) on the occurrence of corporate fraud.

The results reveal that the IAESA is significantly negatively correlated with the occurrence of corporate fraud. This suppression effect is more pronounced when the internal audit executive is also the company’s supervisor. However, if the internal audit executive is the chairman of the board of supervisors, the suppression effect no longer exists. This paper therefore confirms that the IAESA curbs corporate fraud via the improvement of the internal corporate control level.

Because the sample data was limited by the information disclosure level of the included companies, the sample size was relatively small as compared with those of other studies.

This study not only expands the research perspective in the field of internal audit functions but also provides a decision-making reference for the prevention of corporate fraud.

This paper extends an approach that might be able to curb corporate fraud.

A comprehensive index was developed using data envelope analysis to quantify the supervisory ability of internal audit executives. Based on this, this research confirms that the internal audit department performs a “firewall function” to prevent corporate fraud.

The purpose of this paper is to present the irregular deviation examination of flight control surfaces and the potential problem diagnosis of irregular deviations for the jet transport aircraft. A four-jet transport aircraft at transonic flight in cruise phase is the study case of the present article.

The standard lift-to-drag ratio (L/D) and flight dynamic models are established through flight data mining and the fuzzy logic modeling technique based on the flight data of quick access recorder available in the Flight Operations Quality Assurance (FOQA) program of the airlines. The irregular deviations of flight control surfaces are examined by the standard L/D model-predicted results through sensitivity analysis. The contribution values in L/D deficiency are predicted by the deviations and the L/D derivatives of all influencing variables in Taylor series expansion. The potential problems due to irregular deviations can be excavated by the flight dynamic models through the analysis of in-flight stability and controllability.

The magnitude of stabilizer angle to the deficiency of L/D is the largest among the four control surfaces and elevator is the second one through the judgment of contribution values in L/D deficiency. The stabilizer has irregular deviations with obvious endplay problems of jackscrew, as found in the present study. The stabilizer is suggested to have the unscheduled maintenance for the flight control rigging.

The specific transport aircraft of the standard L/D model should be the best one in L/D performance among all transport aircraft in the fleet of the airlines. The present method is a new concept to monitor the irregular deviation of flight control surface. The study case of the four-jet transport aircraft at transonic flight in cruise phase is illustrated as the standard L/D mode. The required flight data of monitored flight is requested to eliminate the biases through compatibility checks. The flight data of study case in the present study is also illustrated as monitored flight data.

To diagnose the irregular deviations of flight control surface deflected angles with contributing to the L/D deficiency estimation is an innovation to improve the flight data analysis of FOQA program for airlines. If the irregular deviation problems of control surfaces can be fixed after rigging in maintenance, the goal of flight safety and aviation fuel saving will be achieved.

The flight control surface rigging of unscheduled maintenance is not expected to coincide with an airline’s peak season or unavailable space in hangar. The optimal time of unscheduled maintenance for the flight control rigging will be easily decided through the correlations between excessive fuel cost and flight safety.

This method can be used to assist airlines to monitor irregular angular positions of flight control surfaces as a complementary tool for management to improve aviation safety, operation and operational efficiency.

In this study, the authors explore the association between employee education level and the efficiency of corporate investment using data from a sample of Chinese listed firms during the period from 2011 to 2018. By examining the impact of education on investment efficiency, the authors' study provides valuable insights that contribute to a deeper understanding of the underlying economic mechanisms related to education.

The authors conduct multivariate regression analyses to examine the relationship between investment efficiency (following Richardson, 2006) and the level of employee education, along with a series of control variables. To ensure the reliability of the authors' findings, the authors subject the their results to a comprehensive set of robustness tests, such as a staggered difference-in-difference (DiD) regression approach, an instrumental variable (IV) method and the use of alternative employee education level and investment efficiency measurements.

The findings offer compelling evidence that higher levels of education have a positive impact on firms' investment efficiency, and this effect remains robust across various model specifications and endogeneity considerations. Moreover, the influence of education is more pronounced in firms that prioritize employee training, maintain effective internal communication and offer attractive financial rewards. Furthermore, the results suggest that the relationship between education and investment efficiency is influenced by the firms' business nature and competitive environment. Factors such as business complexity, labor intensity and business location also play a role in shaping the impact of education on investment outcomes.

The study emphasizes the crucial role of education in influencing investment decisions and performance within firms. By delving into this previously unexplored area, the authors' research contributes to the existing literature, establishing that the level of employee education is a significant determinant of corporate investment efficiency. This valuable insight has substantial implications for firms aiming to enhance their investment decision-making processes and overall performance. Understanding the positive impact of education on investment efficiency can empower organizations to leverage their human capital effectively and achieve better investment outcomes, ultimately contributing to long-term success and competitiveness in the market.

This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities.

This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks.

The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People’s Daily corpus compared to existing models.

This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Ethical decision making, business ethics.

This case is applicable to MBA, EDP and EMBA courses.

TOREAD, a professional provider of outdoor equipment in China, started in business by producing and selling tents. To meet market demand, TOREAD expanded its product line which ranges from outdoor durable tent products to “pan-outdoor” products including footwear and clothing. During the critical expansion phase, TOREAD was challenged by a quality problem in a batch of outsourced sandals that had been manufactured by a contracted supplier. By researching different options and going through an ethical decision making process, TOREAD made the choice of destroying all “problem sandals”. Since then, TOREAD has focused development on product quality improvement and product innovation to establish a sustainable brand image and generate social benefits. TOREAD's decision making in the critical development phase helped it to become the leader in the outdoor product industry in China.

This case may be used for courses such as business ethics and strategy. By learning this case, students can understand the process of making ethical decisions when facing moral dilemmas among corporate decision makers, employees and relevant interested parties, and learn how to make strategic decisions to balance company profit growth and social benefits in critical development phases.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Purpose: This research investigates the effects of the coronavirus pandemic on Portuguese and Brazilian consumers’ vulnerability in contrasting age groups. It seeks to establish the relationship between fear of COVID-19 and the pandemic’s impact on customer’s vulnerability to help companies design strategies to cope with this new market context and be prepared to address these vulnerabilities in a future international health crisis.

Design/Methodology/Approach: This study employs a quantitative research method to assess the different impacts of the COVID-19 pandemic on consumer vulnerability. Based on Portuguese and Brazilian residents’ surveys, an age/country-segmented cross-cultural multi-group analysis was performed to understand the differences in vulnerability.

Findings: Outcomes proved how the pandemic aggravates distinctively the vulnerability dimensions of consumers living in Portugal and Brazil. Besides, results highlight significant differences in consumers’ vulnerability during the pandemic given their age group. A positive correlation between age and fear of COVID-19 was also verified.

Value: Results were obtained based on consumers’ perceptions and scores rather than postulated behaviors. The findings highlight the need for health prevention measures to avoid neglecting existing vulnerable groups, whilst verifying how COVID-19 has managed to proliferate consumers’ vulnerability. Suggestions are drawn for both firms and governments based on obtained results and existing literature. Exemplar business strategies to avoid these vulnerabilities are put forward and discussed. The potential business advantages of firms shaping their activity according to their customers’ current vulnerabilities, during international pandemics, are also pointed.

We argue that the influence of public stakeholders (the state) and private stakeholders (nonstate social or economic stakeholders) on corporate philanthropy is interdependent, in that satisfying the state may increase the degree of scrutiny and pressure exerted by private stakeholders on the firm, particularly in institutional environments that place few checks and balances on the power of the state – thus creating suspicion that political patronage shelters firms’ social and moral wrongdoing. To test this theory, we examine the circumstances under which politically patronized firms engage more (or less) in corporate philanthropy. Utilizing a dataset that encompasses both publically traded and unlisted private firms in China, we find that corporate philanthropy is negatively associated with political patronage among unlisted firms but positively associated with political patronage among listed firms. These results are consistent with the predictions made based on our theoretical arguments. This chapter aims to foster further discussion regarding the interdependence of the influences exerted by different stakeholders on firms.