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The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

This paper investigates the role of nexus supplier transparency, which involves the collective information disclosure to the public by second-tier nexus suppliers, as an alternative mechanism for mitigating buyer environmental, social and governance (ESG) risk exposure. We also examine buyer supply network accessibility as a moderating factor that facilitates collecting detailed information and undertaking corrective actions accordingly.

We collected a sample of 428 focal buyer firms and their supply networks up to third-tier suppliers. Data were obtained from Bloomberg and RepRisk databases. We identified critical nexus suppliers using data envelopment analysis (DEA) and tested hypotheses using regression analysis.

The results show that the benefits of nexus supplier transparency, such as reducing buyer ESG risk exposure, differ depending on the type of nexus supplier disclosing information and buyer supply network accessibility. Informational nexus supplier transparency was found to be beneficial. However, the results revealed the double-edged sword of monopolistic nexus supplier transparency, which benefits buyers with higher levels of accessibility but increases risk exposure for buyers with lower accessibility.

This study demonstrates that the transparency of critical second-tier suppliers mitigates buyer ESG risk exposure by providing information about lower tiers in the supply network. Challenging the notion of the focal buyer as the main orchestrator of supply chain initiatives, our alternative perspective opens a new avenue for risk management in multi-tier supply chains.

This study investigates the behavior of viscous hybrid ferromagnetic fluids flowing through plain elastic sheets with the magnetic polarization effect. It examines flow in a porous medium using Stefan blowing and utilizes a versatile hybrid ferrofluid containing MnZnFe₂O₄ and Fe₃O₄ nanoparticles in the C2H2F4 base fluid, offering potential real-world applications. The study focuses on steady, laminar and viscous incompressible flow, analyzing heat and mass transfer aspects, including thermal radiation, Brownian motion, thermophoresis and viscous dissipation with convective boundary condition.

The governing expression of the flow model is addressed with pertinent non-dimensional transformations, and the finite element method solves the obtained system of ordinary differential equations.

The variations in fluid velocity, temperature and concentration profiles against all the physical parameters are analyzed through their graphical view. The association of these parameters with local surface friction coefficient, Nusselt number and Sherwood number is examined with the numerical data in a table.

This work extends previous research on ferrofluid flow, investigating unexplored parameters and offering valuable insights with potential engineering, industrial and medical implications. It introduces a novel approach that uses mathematical simplification techniques and the finite element method for the solution.

This paper aims to focuses on by investigate the heat transmission and free convective flow of a suspension of nano encapsulated phase change materials (NEPCMs) within an enclosure. Particles of NEPCM have a core-shell structure, with phase change material (PCM) serving as the core.

The enclosure consists of a square chamber with an insulated wall on top and bottom and vertical walls that are differently heated. The governing equations are investigated using the finite element technique. A grid inspection and validation test are done to confirm the precision of the results.

The effects of fusion temperature (varying from 0.1 to 0.9), Stefan number (changing from 0.2 to 0.7), Rayleigh number (varying from 10³ to 10⁶) and volume fraction of NEPCM nanoparticles (changing from 0 to 0.05) on the streamlines, isotherms, heat capacity ratio and average Nusselt number are investigated using graphs and tables. From this investigation, it is found that using a NEPCM nano suspension results in a significant enhancement in heat transfer compared to pure fluid. This augmentation becomes more important for the low Stefan number, which is around 16.57% approximately at 0.2. Secondary recirculation is formed near the upper left corner as a result of non-uniform heating of the left vertical border. This eddy expands notably as the Rayleigh number rises. The study findings indicate that the NEPCM nanosuspension has the potential to act as a smart working fluid, significantly enhancing average Nusselt numbers in enclosed chambers.

The NEPCM particle consists of a core (n-octadecane, a phase-change material) and a shell (PMMA, an encapsulation material). The host fluid water and the NEPCM particles are considered to form a dilute suspension.

Using NEPCMs in energy storage thermal systems show potential for improving heat transfer efficiency in several engineering applications. NEPCMs merge the beneficial characteristics of PCMs with the enhanced thermal conductivity of nanoparticles, providing a flexible alternative for effective thermal energy storage and control.

This paper aims to explore the free convective flow and heat transmission of NEPCM water-type nanofluid in a square chamber with an insulated top boundary, a uniformly heated bottom boundary, a cooled right boundary and a non-uniformly heated left boundary.

The current study has a two-fold purpose. Firstly, it aims to analyze the extent to which knowledge management (KM) affects the performance of individuals (task and contextual) on the one hand and that of organizations (product or service, perceived and financial) on the other hand. Secondly, it proposes to investigate the mediating effect of motivation and innovation in the relationship between KM and individual and organizational performance.

Partial least squares structural equation modeling (PLS-SEM) was employed in this study, with mediation analysis performed using advanced PLS-SEM techniques. A total of 1,284 respondents from organizations in both the public and private sectors were included in the sample.

The findings emphasize that KM has a more significant direct effect on individual performance compared to organizational performance. Concurrently, in terms of indirect influence, it is found that KM, through motivation and innovation, has a positive and significant effect on both individual and organizational performances, with a higher influence on the organizational one.

The originality of the work can be noted in designing two different structural models to represent the proposed relationships at the individual and organizational levels. These findings could provide organizational decision makers with empirical evidence, helping them (1) internalize the significance of the KM process in organizations as well as its subsequent effects on individual and organizational performance and (2) identify factors that mediate variable relationships.

This study aims to comprehensively investigate the electron beam powder bed fusion (EB-PBF) process for copper, offering validated estimations of melt pool temperature and morphology through numerical and analytical approaches. This work also assesses how process parameters influence the temperature fluctuations and the morphological changes of the melt pool.

Two distinct methods, an analytical model and a numerical simulation, were used to assess temperature profiles, melt pool morphology and associated heat transfer mechanisms, including conduction and keyhole mode. The analytical model considers conduction as the dominant heat transfer mechanism; the numerical model also includes convection and radiation, incorporating specific parameters such as beam power, scan speed, thermophysical material properties and powder interactions.

Both the analytical model and numerical simulations are highly correlated. Results indicated that the analytical model, emphasising material conduction, exhibited exceptional precision, although at substantially reduced cost. Statistical analysis of numerical outcomes underscored the substantial impact of beam power and scan speed on melt pool morphology and temperature in EB-PBF of copper.

This numerical simulation of copper in EB-PBF is the first high-fidelity model to consider the interaction between powder and substrate comprehensively. It accurately captures material properties, powder size distribution, thermal dynamics (including heat transfer between powder and substrate), phase changes and fluid dynamics. The model also integrates advanced computational methods such as computational fluid dynamics and discrete element method. The proposed model and simulation offer a valuable predictive tool for melt pool temperature, heat transfer processes and morphology. These insights are critical for ensuring the bonding quality of subsequent layers and, consequently, influencing the overall quality of the printed parts.

The ability to measure cognitive load in the workplace provides several opportunities to improve workplace learning. In recent years, virtual reality (VR) has seen an increase in use for training and learning applications due to improvements in technology and reduced costs. This study aims to focus on the use of simulation task load index (SIM-TLX), a recently developed self-reported measure of cognitive load for virtual environments to measure cognitive load while undertaking tasks in different environments.

The authors conducted a within-subject design experiment involving 14 participants engaged in digit-recall n-back tasks (1-back and 2-back) in two VR environments: a neutral grey environment and a realistic industrial ozone facility. Cognitive load was then assessed using the SIM-TLX.

The findings revealed higher task difficulty for the 2-back task due to higher mental demand. Furthermore, a notable interaction emerged between cognitive load and different virtual environments.

This study relied solely on an n-back task and SIM-TLX self-report measure to assess cognitive load. Future studies should consider including ecologically valid tasks and physiological measurement tools such as eye-tracking to measure cognitive load.

Identifying cognitive workload sources during VR tasks, especially in complex work environments, is considered beneficial to the application of VR training aimed at improving workplace learning.

This study provides unique insights into measuring cognitive load from various sources as defined by the SIM-TLX sub-scales to investigate the impact of simulated workplace environments.

This paper explains why some firms manage to capture disproportionate value from their network of relationships, leading to superior performance. The paper examines how a firm's dependencies affect its value appropriation potential (VAP) in economic networks.

The paper follows the axiomatic method and the embeddedness perspective of firms to develop an index called nodal power, which captures the power that accrues to a firm in exchange-based economic networks. Thereafter, using the formal method and simulation, it shows nodal power reflects a firm's VAP in economic networks.

The study analysis and findings prove that a firm's dyadic level exchange relations and the embedded network structure determine its VAP by affecting the nodal power. A firm with lesser nodal power is likely to appropriate less value from its relations even if it equally contributes to the value creation. This finding explains how the structural and relational characteristics of a firm's network enable disproportionate value appropriation.

Nodal power furthers the scope of analyzing firms' economic relationships and changing power equations in dynamic networks. It can help firms build optimal strategic networks and manage the portfolio of relationships by predicting the impact of changing relations on firms' VAP.

The paper's original contribution is to explain, through formal analysis, why and how the structure and nature of relations of firms affect their VAP. The paper also formalizes the power-dependence principle through a dependency-based index called nodal power and uses it to show how interfirm dependencies are key to value appropriation.

The hybrid nanofluid flow due to a rotating disk has numerous applications, including centrifugal pumps, paper production, polymers dying, air filtration systems, automobile cooling and solar collectors. This study aims to investigate the convective heat transport and magnetohydrodynamics (MHD) hybrid nanofluid flow past a stretchable rotating surface using the Yamada-Ota and Xue models with the impacts of heat generation and thermal radiation.

The carbon nanotubes such as single-wall carbon nanotubes and multi-wall carbon nanotubes are suspended in a base fluid like water to make the hybrid nanofluid. The problem’s governing partial differential equations are transformed into a system of ordinary differential equations using similarity transformations. Then, the numerical solutions are found with a bvp4c function in MATLAB software. The impacts of pertinent parameters on the flow and temperature fields are depicted in tables and graphs.

Two solution branches are discovered in a certain range of unsteadiness parameters. The fluid temperature and the rate of heat transport are enhanced when the thermal radiation and heat generation effects are increased. The Yamada-Ota model has a higher temperature than the Xue model. Furthermore, it is observed that only the first solution remains stable when the stability analysis is implemented.

To the best of the authors’ knowledge, the results stated are original and new with the investigation of MHD hybrid nanofluid flow with convective heat transfer using the extended version of Yamada-Ota and Xue models. Moreover, the novelty of the present study is improved by taking the impacts of heat generation and thermal radiation.

In recent times, ternary hybrid nanofluid has garnered attention from scientist and researchers due to its improved thermal efficiency. This study aims to delve into the examination of ternary hybrid nanofluid (Al₂O₃–Cu–TiO₂/water), particularly concerning axisymmetric flow over a nonlinearly permeable stretching/shrinking disk. In addition, the investigation of convective boundary conditions and thermal radiation effects is also considered within the context of the described flow problem.

Mathematical formulations representing this problem are reduced into a set of ordinary differential equations (ODEs) using similarity transformation. The MATLAB boundary value problem solver is then used to solve the obtained set of ODEs. The impact of considered physical parameters such as suction parameter, radiation parameter, nonlinear parameter, nanoparticle volumetric concentration and Biot number on the flow profiles as well as the physical quantities is illustrated in graphical form.

The findings revealed the thermal flux for the nonlinearly shrinking disk is approximately 1.33%, significantly higher when compared to the linearly shrinking disk. Moreover, the existence of dual solutions attributed to the nonlinear stretching/shrinking disk is unveiled, with the first solution being identified as the stable and reliable solution through temporal stability analysis.

Understanding ternary hybrid nanofluid behavior and flow has applications in engineering, energy systems and materials research. This study may help develop and optimize nanofluid systems like heat exchangers and cooling systems.

The study of flow dynamics across nonlinear stretching/shrinking disk gains less attention compared to linear stretching/shrinking geometries. Many natural and industrial processes involve nonlinear changes in boundary shapes or sizes. Understanding flow dynamics over nonlinear shrinking/stretching disks is therefore essential for applications in various fields such as materials processing, biomedical engineering and environmental sciences. Hence, this study highlights the axisymmetric flow over a nonlinear stretching/shrinking disk using ternary hybrid nanofluid composed of alumina (Al₂O₃), copper (Cu) and titania (TiO₂). Besides, this study tackles a complex problem involving multiple factors such as suction, radiation and convective boundary conditions. Analyzing such complex systems can provide valuable insights into real-world phenomena where multiple factors interact.