Search results

This study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.

A convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.

This study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.

The “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

This study aims to enhance merging of additive manufacturing (AM) techniques with powder injection molding (PIM). In this way, the prototypes could be 3D-printed and mass production implemented using PIM. Thus, the surface properties and mechanical performance of parts produced using powder/polymer binder feedstocks [material extrusion (MEX) and PIM] were investigated and compared with powder manufacturing based on direct metal laser sintering (DMLS).

PIM parts were manufactured from 17-4PH stainless steel PIM-quality powder and powder intended for powder bed fusion compounded with a recently developed environmentally benign binder. Rheological data obtained at the relevant temperatures were used to set up the process parameters of injection molding. The tensile and yield strengths as well as the strain at break were determined for PIM sintered parts and compared to those produced using MEX and DMLS. Surface properties were evaluated through a 3D scanner and analyzed with advanced statistical tools.

Advanced statistical analyses of the surface properties showed the proximity between the surfaces created via PIM and MEX. The tensile and yield strengths, as well as the strain at break, suggested that DMLS provides sintered samples with the highest strength and ductility; however, PIM parts made from environmentally benign feedstock may successfully compete with this manufacturing route.

This study addresses the issues connected to the merging of two environmentally efficient processing routes. The literature survey included has shown that there is so far no study comparing AM and PIM techniques systematically on the fixed part shape and dimensions using advanced statistical tools to derive the proximity of the investigated processing routes.

This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

Unhealthy diets are associated with an increased risk of non-communicable diseases and present a significant public health challenge. When developing effective interventions and policies, consideration must be given to the unique social culture in which food choice is embedded. Health vulnerabilities to poor nutrition exist throughout life but may be influenced by socio-cultural factors such as age. This study aimed to assess the attitudes of older or younger Chinese consumers towards healthy eating and explore the factors influencing their food choices.

Semi-structured interviews were conducted in Wuhan, China, with 20 consumers aged 18–25 (Group A) and 20 consumers aged 65 and over (Group B).

Thematic analysis revealed that the two groups had positive attitudes towards healthy eating, although Group A participants were more knowledgeable. Time pressure, food prices and social networks differentially influence healthy eating practices across age groups.

Given China’s economic and cultural context, healthy eating interventions should consider the individual characteristics and food preferences of the different age groups. This approach can optimize targeted healthy eating interventions, and media communications related to healthy eating.

This study aims to investigate international students’ experiences through a lens of knowledge management perspective, emphasizing their strategies for knowledge management in tandem with cultural adaptation. The primary objective is to elucidate how international students navigate cultural differences and use knowledge management strategies to augment their learning and integration, thereby supporting their academic progress in a new academic environment.

An in-depth qualitative research strategy was used, using semistructured interviews with Pakistani doctoral students who were studying in Australia. A thematic analysis was conducted to identify recurring themes and patterns in the data.

The findings reveal that international students adeptly adopt various knowledge management strategies to facilitate cultural adaptation. These strategies encompass embracing otherness through a sense of belonging, engaging in both personal and shared learning experiences, achieving individual success, and using critical inquiry as a guiding framework for observations. Notably, this study underscores the pivotal role played by cultural competence in conjunction with social networks, influencing cultural intelligence and, subsequently, impacting knowledge sharing and integration for academic progress.

This study’s findings provide practical insights for higher education institutions and policymakers, emphasizing the importance of supporting international students in their cultural adaptation and knowledge management endeavors. These practical implications encompass fostering a welcoming and inclusive environment, supporting intercultural engagement, using technology for enhanced learning and communication and promoting the development of cultural intelligence among international students.

This study contributes to the literature on international student experiences and knowledge management by providing insights into the strategies used by international students to navigate knowledge of cultural differences to enhance their learning experiences and advance academic progress. These findings contribute to a deeper understanding of the intersection between cultural adaptation and core knowledge management concepts of knowledge sharing and integration in the context of higher education.

The investigation of pro-environmental behaviors (PEB) among college students is essential for future sustainability endeavors. Existing research seldomly concentrated on college students and their PEB. This study aims to address the gap in understanding PEB among college students.

This study constructed an integrated model combining the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory, with the novel addition of environmental risk perception. Through an empirical study involving 844 college students, this research analyzed the data with the structural model.

The authors identified that environmental values, attitudes, perceived behavioral control, subjective norms and risk perception play crucial roles in shaping PEB. This study also revealed age-related differences, highlighting that older students might be less influenced by attitudes and subjective norms due to more established habits. Findings underscore the importance of fostering PEB through environmental education, promotion of low-carbon lifestyle choices and incentives. This investigation not only enriches the theoretical framework for PEB but also offers practical insights for policymakers and educators to enhance sustainable practices among the youth.

Though the authors offer valuable findings, this research has two key limitations: the use of observational data for hypothesis testing, which weakens causal inference, and the collection of data through questionnaires, which may be biased by social desirability. Respondents of self-report tend to behave in the socially desired ways. Consequently, they usually exaggerate their pro-environmental intention or PEB. To comprehend the influencing aspects more thoroughly, future research should consider incorporating experimental methods and objective data, such as digitalized data.

The findings provide valuable evidence for guiding college students’ PEB, including strengthening environmental education, promoting of low-carbon fashion and providing incentives for PEBs.

First, the authors examine the internal factors influencing PEB among Chinese university students within the “dual-carbon” initiative framework. Second, this research pioneers the use of structural equation modeling to merge TPB and VBN theories, offering a predictive model for university students’ PEB. Third, the authors introduce “environmental risk perception” as a novel variable derived from both TPB and VBN, enhancing the model’s explanatory power.

This study aimed to examine consumers’ impulse purchases of luxury products in the metaverse, specifically by exploring how consumers’ motivational and emotional experiences affect virtual luxury product purchases in the metaverse.

An online survey was administered to a total of 230 users of various metaverse platforms in China. The data were analysed using partial least squares structural equation modelling (PLS-SEM) disjoint two-stage approach.

The findings indicated that motivational experience (goal importance and goal interest) and positive emotion (fantasy, feeling and fun) have positive effects on impulse buying of luxury products in the metaverse, but none for negative emotions (loneliness and isolation).

This study indicated that understanding and leveraging consumers’ motivational experiences and positive emotions can drive their impulse buying behaviour of luxury products in the metaverse, hence providing virtual and brand retailers with a testbed for their products before they launch in the physical market.

This study enriches our comprehension of consumers’ metaverse luxury purchases by delving into the impacts of motivational and emotional experiences on impulse buying behaviours.

Leakage serves as a core indicator of sealing performance degradation, particularly under high-speed and heavy-duty operational where increased leakage is common. Within heavy-duty vehicle transmissions, the leakage can lead to excessive pressure loss and eventual transmission failure. This study aims to introduce a predictive method for assessing sealing ring leakage in vehicle transmissions based on operating conditions.

Seal test was carried out using a specialized seal test rig. Various data points were collected during this test, including leakage, friction torque, oil temperature, oil pressure and rotating speed. The collected data underwent noise separation and reconstruction using the complete ensemble empirical mode decomposition with adaptive noise method. Subsequently, a leakage prediction model is developed using the random forest regression with parameter optimization. A quantitative evaluation for influencing factors in leakage prediction process is investigated.

The results achieve a mean accuracy index exceeding 95%, demonstrating close alignment between predicted and actual leakage values. Feature contribution results highlight that the trends of the oil temperature, friction torque and oil pressure significantly affect the leakage prediction, with the oil temperature trend exerting the most substantial influence.

This work sheds light on the interplay between operating conditions and sealing performance degradation, offering valuable insights for understanding and addressing sealing issues effectively.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0271/

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.