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Binder jetting is one of the essential additive manufacturing methods because it is cost-effective, has no thermal stress problems and has a wide range of different materials. Using binder jetting technology in the industry is becoming more common recently. However, it has disadvantages compared to traditional manufacturing methods regarding speed. This study aims to increase the manufacturing speed of binder jetting.

This study used adaptive slicing to increase the manufacturing speed of binder jetting. In addition, a variable binder amount algorithm has been developed to use adaptive slicing efficiently. Quarter-spherical shaped samples were manufactured using a variable binder amount algorithm and adaptive slicing method.

Samples were sintered at 1250°C for 2 h with 10°C/min heating and cooling ramp. Scanning electron microscope analysis, surface roughness tests, and density calculations were done. According to the results obtained from the analyzes, similar surface quality is achieved by using 38% fewer layers than uniform slicing.

More work is needed to implement adaptive slicing to binder jetting. Because the software of commercial printers is very difficult to modify, an open-source printer was used. For this reason, it can be challenging to produce perfect samples. However, a good start has been made in this area.

To the best of the authors’ knowledge, the actual use of adaptive slicing in binder jetting was applied for the first time in this study. A variable binder amount algorithm has been developed to implement adaptive slicing in binder jetting.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

The conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.

The multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.

Benchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.

Because of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

The purpose of this paper is to develop and test an integrated model to examine the relationship between work engagement and three facets of perceived job performance (PJP). The authors argue that work engagement might not optimally improve PJP unless it is channelized through information and communication technology orientation.

Data for the present research were collected from higher educational institutes in the northern region of India by using a convenient sampling technique. Results of structural equation modeling (SEM) through AMOS 20 revealed that work engagement facilitates all three facets i.e. task performance, contextual performance and adaptive performance of teaching professionals. Furthermore, SEM results established the partial mediating effect of information and communication technology orientation between work engagement, task performance, contextual performance and adaptive performance.

Findings from present research contribute theoretically as well as practically to job performance and work engagement literature by giving insights to administrators and practitioners on how to improve the overall job performance of teaching professionals by enhancing their engagement and addressing their need for digital know-how.

To the best of the authors’ knowledge, this study is one of the first to study the impact of work engagement and information and communication technology on the three facets of PJP using a diverse sample of 1030 teachers from universities in North India.

The purpose of this study is to establish the relationship between digital leadership and academic performance. It models the digitalization process, outlining why and how digital leadership is important for better academic performance. At the same time, this study examines the role of digital culture as a moderating variable in the direct relationship between main variables of the study. The study aims to expand the domain of academic performance at the university by including a much recent leadership-related aspect and organizational context of the digital culture.

The study opted for a descriptive study, using the survey instruments to collect the data. The sample population consisted of students currently enrolled at the Faculty of Business and Management, Universiti Teknologi MARA, Melaka, Malaysia. Based on the convenience sampling, 383 samples were drawn from the sample population. All items were adopted from previous literature, and expert feedback was obtained to examine the validity of the instruments. The data were analysed using SPSS and SmartPLS version 3.0.

This study provides empirical insights about how digital leadership is important for academic performance for the new millennials. Also, digital culture is found to provide significant moderation effect into the relationship. It suggests that universities must promote digitalization culture and embed the use of technology and digitalization into teaching and learning to cultivate a more effective learning process among university students. This is important as elements of digital leadership, including adaptive role, attitude, digital competency, digital skill and inspirational role, are found to significantly contribute to academic performance.

This study only focuses on samples taken from one of the faculties in one campus, thus limiting its scope. Future research is encouraged to replicate the same study setting to include larger sample size from different faculties, or perhaps from different universities. These propositions could help to better generalize the research findings on the practice of digital leadership on academic performance in the country. However, this study established a digital leadership model that can be applied to undergraduate students at the universities. Also, the inclusion of digital culture can strengthen the learning process.

This study includes implications for the development of digital leadership attributes and promoting digital culture within the university students and environment for engaging in a better academic performance. Digital leadership is found to be an important criterion of academic performance in this digital age society, and cultivating digital culture enhances students’ academic performance. These findings shall prompt the university to actively engage in fostering digitalization culture within the university. Also, the top management of the university should inform the students to be adaptive and cultivate the attributes of digital leaders, as their readiness to cope with the technological change has significant positive impact on their academic performance.

It is important to ensure that the future graduates that are being produced are ready to take on more challenges as digital leaders in the digital society. This might accelerate the country’s initiatives and efforts towards becoming a developed nation. Thus, investing in oneself to become digitally literate and competent might not only influence their academic performance, but they will also be equipped to fulfil one of the expectations of future employers of potential graduates, which is possessing digital leadership.

Digitalization is not only about the technology. It is about the people too. As the study on digital leadership is still in its infant stage, this study is unique as it is among the earliest to establish digital leadership constructs within the context of Malaysia. It informs the university that digital leadership provides significant contribution to academic performance. Thus, the university is encouraged to nurture digitalization, not only in the teaching and learning but also with the people within the university environment. Determining the right programs and plans for the curricular will help students to develop digital leadership attributes more effectively. Finally, improving digitalization among its students and culture is important, as these elements provide significant effect towards academic performance.

This study aims to investigate the multiple influence paths or underlying mechanisms of entrepreneurial leadership (EL) on adaptive innovation from the perspectives of organizational learning and resource management, drawing on complex adaptive system theory.

With a questionnaire survey of 317 senior and middle managers from different firms in China, structural equation modeling was used to test the hypothesized conceptual model, and bootstrapping method was employed to examine the multiple mediating effects.

Results indicate that EL has a significant and positive effect on adaptive innovation. This relationship is partially mediated through exploitative learning, exploratory learning, resource bricolage and boundary-spanning integration, respectively. The impact of EL on adaptive innovation is also sequentially transmitted through exploitative learning and resource bricolage or exploratory learning and boundary-spanning integration.

Adaptive innovation has become a firm competition strategy to cope with dynamic changes in current uncertain environment where EL can play its effectiveness to engage firms in such innovation activities. However, the question of why and how EL drives adaptive innovation has yet to be discussed. This study highlights the innovation effectiveness of EL and the triggering process of adaptive innovation, and contributes to several countermeasures for firms to implement leadership and innovation practices responding to uncertain environment.

Utilizing the social cognitive theory, this study aims to suggest that organizations that promote high-performance work practices (HPWPs) are instrumental in fostering an individual's affective commitment to change through the explanatory process of an individual's readiness to change. In addition, high adaptive leadership would also strengthen HPWPs and readiness to change relationships.

This study tested the hypotheses using a temporally segregated research design across three time waves (n = 337).

This study found support for the direct, mediating, moderating and mod-med hypotheses. The results of this study corroborate that a high adaptive leadership and an organization implementing HPWPs set the stage for creating an individual's affective commitment to change via their readiness to change.

The current study integrates the change management, leadership and HRM literature by suggesting a unique mechanism and boundary conditions that advance research and practice in an individual's willingness and acceptance to change. Based on the study's findings, the authors suggest theoretical and practical implications for research and practice.