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This paper is mainly concerned with studying the e‐manufacturing scheduling issue based on the bills of materials (BOM) flows of products. On the basis of e‐manufacturing philosophy, a genetic algorithm (GA)‐based scheduling model for physical e‐manufacturing cells is put forward. By means of building the mapping relationship and dynamic task association among physical e‐manufacturing cells, logic e‐manufacturing cells, e‐manufacturing systems and e‐manufacturing environment, high‐level scheduling issues can be transformed as an inductive procedure concerning the scheduling results to corresponding physical e‐manufacturing cells. As verification, finally, a case study is given to demonstrate the method mentioned above.

Extends the concept of e‐service to the whole phase of manufacturing. Develops an e‐service platform prototype based on this concept with Java Web solution including the mobile agent broking technologies and application service provider (ASP) principle. The key point to implement the platform is to enable an open Web information service infrastructure for the whole product manufacturing chain. Inside this infrastructure, product‐specific online manufacturing system can be created by means of using a kind of bidding model. All users participating in the manufacturing process are able to cooperatively finish manufacturing tasks in real time through sharing the same platform. With the help of BOM flow, the global information service flow can be controlled easily. In addition, the legacy hardware/software can also be encapsulated with aglets that are Java mobile agents. As to new ASP software packages, they can be configured simply via the plug and play mode to the e‐service platform. In this way, the on‐line networked manufacturing can be tested.

The purpose of this paper is to solve the asynchrony problem between the logistics stream and the information stream in the complex product assembly executive process.

States of assembly and logistics are described by transitions, and implemented via logistics agents and assembly agents. Events in the assembly executive process are described by places, and mapped to radio frequency identification (RFID) tags' states. An agent‐based complex product assembly framework is proposed. Mobile agents are used to encapsulate task and data, and RFID tags' states are able to trigger dispatching of assembly agents and executing of assembly tasks. Assisted by mobile agents' retraction mechanism, on‐site data are carried back when assembly tasks are finished.

An assembly executive process Petri net and a mobile agent‐based complex product assembly framework are proposed.

Dynamic matching mechanism between assembly tasks and materials is achieved, and controlling and monitoring methods of complex product assembly executive process are enhanced.

The purpose of this paper is to derive a new lubrication model of double involute gears drive and study the effect of the tooth waist order parameters of double involute gears on lubrication performance.

The new lubrication model of double involute gears drive was established according to the meshing characteristics of double involute gears drive and the finite length line contact elastohydrodynamic lubrication theory. Numerical calculation of the lubrication model of gear drive was conducted using the multigrid method.

The results show that the oil film necking phenomenon and the oil film pressure peak emerged at the tooth waist order area and the tooth profile ends, and when compared with involute gear, the lubrication performance at the tooth waist order area is better than that at the tooth profile ends. The effect of tooth waist order parameters on lubrication performance at the tooth waist order area was greater than that at other areas.

This research will promote the application of the double involute gear as soon as possible, and it has the reference value for other types of gears.

This study aims to examine the role played by brand equity orientation and failure type in service recovery. Specifically, through the lens of forgiveness, the way brand equity orientation and failure type influence consumer response to recovery is revealed.

This research tests a novel model using data collected from 1,589 consumers in two scenario-based experimental studies. The statistical product and service solutions (SPSS) program with the PROCESS tool was used to test the mediation and moderated mediation effects.

The research findings suggest that forgiveness plays a mediation role in the relationship between recovery and satisfaction. Brand equity orientation moderates the mediation effect of forgiveness on the relationship between recovery and recovery satisfaction. In addition, failure type also plays an important role and there is a significant three-way interaction effect (service recovery × brand equity orientation × failure type) on recovery satisfaction under certain circumstances.

Building on the extant literature which focuses on the cognitive process when investigating recovery and consumer reaction, this research advocates the significant role played by the psychological process, namely, the feeling of forgiveness, in explaining the effect of distinct recovery strategies on consumer satisfaction. This research also unveils the effects of brand equity orientation and failure type on recovery outcomes.

When addressing performance failure, brand equity orientation and failure type need to be identified. Businesses could develop recovery strategies to arouse consumer forgiveness, which would lead to increased recovery satisfaction. When designing recovery strategies, managers need to be mindful of the effects of brand equity orientation and failure type.

This research is one of the few which reveals the mediating role played by forgiveness on the effect of recovery. Taking brand equity orientation and failure type into consideration, the findings of this research provide new insights into the recovery literature.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

The purpose of this paper is to present a numerical model to investigate the dynamic behavior and force coefficients of a compact squeeze film damper with dual film clearances adjusted by an elastic ring, known as elastic ring squeeze film damper (ERSFD).

The governing equations of ERSFD as well as the boundary conditions are obtained based on Reynolds equation. A simplified Greenwood–Williamson model is implemented to investigate the contact behavior between the elastic ring and the journal. The interactions between the films and the elastic ring are achieved by block iterative method.

The radial deformation as well as velocity of the elastic ring are captured to illustrate the pressure profiles of the inner and outer films. High-order frequency components related to the number of the boss N are observed on the frequency spectrum of the film force. The force coefficients of the ERSFD are constant for a wider range of non-dimensional whirling radius ε compared with conventional squeeze film damper.

The force coefficients of the ERSFD are obtained by assuming that the journal center moves in a circular centered orbit. High-order frequency components related to the number of bosses N are observed. These findings may provide helpful materials for the application of the ERSFD.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Overqualification is a growing concern for employers in many countries. The practice of mismatching employees and positions inevitably leads to an increase in organizational training and management costs. This study can not only further enrich the theoretical achievements in the field of human resource management, but can also provide a reference for enterprise managers on how to effectively stimulate employees' innovative behaviour.

503 questionnaire responses were analysed using SPSS 24.0 and Mplus 8.0.

It was concluded that, (1) perceived overqualification is negatively related to employees' innovation behaviour (β = −0.32, p < 0.001); (2) psychological contract breach and psychological distance partially mediate the relationship between perceived overqualification and employees' innovation behaviour. Among them, the mediating effect of psychological contract breach was −0.14 (p < 0.01), and that of psychological distance was −0.12 (p < 0.001); the chain-mediating effect of psychological contract breach and psychological distance was −0.09 (p < 0.01); (3) employment relationship atmosphere played a moderating role in the relationship between perceived overqualification and employees' innovation behaviour (β = 0.04, p < 0.01).

This study verifies the negative impact of perceived overqualification on employee innovation behaviour and reveals the mediating role of psychological factors between the two. It also discusses the moderating effect of employment relationship climate on the relationship between perceived overqualification and employees' innovation behaviour.

This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.