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The large size of models and long computing time prevent the creation of full‐scale, three‐dimensional models of end region of turbogenerators. Only exact three‐dimensional model can illustrate complex phenomena of end region losses. Also some methods of decreasing such losses cannot be simulated in two‐dimensional models. The purpose of this paper is to focus on a method of creating three‐dimensional models of turbogenerators' end regions for calculations of eddy current losses.

Time‐stepping is the most expensive part of computation. A harmonic model would be free from that disadvantage and it can provide a tool to make an accurate, fully three‐dimensional model of a steady state for different loads and provide results in a reasonable time.

The research focuses on the method of creating three‐dimensional models of turbogenerators end region for calculations of eddy current losses. By using two‐dimensional, time‐stepping models and empirical loss functions for a main flux and three‐dimensional models for eddy current losses from a perpendicular flux of an end connections, it is found that fast analysis of that complex part of a machine can be achieved.

The approach proposed in the paper is a universal and novel method of calculation losses of turbogenerators' end regions. Combining two‐dimensional and three‐dimensional models provides advantages of both known methods: fast computation time from simplified models and good representation of complex geometry of a machine.

The purpose of this paper is twofold: first, to revise the Kano model with a focus on one-dimensional attributes; and second, to use the revised model for categorizing and prioritizing various employee compensation strategies.

The Kano evaluation table has been revised and the one-dimensional attribute has been further extended to three categories of O_O, O_M and O_A. In the next step, the literature review-based identified strategies have been categorized and prioritized according to the developed Kano model. Consequently, an employee compensation system has been proposed to a process-based manufacturing company as a case study.

Findings indicated that out of the 44 employee compensation strategies, typically 6 were must-be, 13 were one-dimensional, 18 were attractive and 7 were indifferent. Also, the results of the revised Kano model indicated that typically out of the 13 one-dimensional strategies, 7 were one-dimensional tending toward must-be (O_M); and 6 were one-dimensional tending toward attractive (O_A).

The case study was limited to one company. The validity of the proposed model can be further studied in a larger population. This study provides managers with a more accurate instrument of decision making in selecting more differentiated employee compensation strategies, which, in turn, might lead to more employee satisfaction.

Theoretically, this study is different from existing studies, since almost none of the previous studies extended the Kano evaluation table for one-dimensional attributes. Practically, this study is another evidence of the application of the Kano model in the field of human resource management and in particular contributes to the design of employee compensation systems.

Due to the inability to directly apply an intra-oral image with esthetic restoration to restore tooth shape in the computer-aided design system, this paper aims to propose a method that can use two-dimensional contours obtained from the image for the three-dimensional dental mesh model restoration.

First, intra-oral image and smiling image are taken from the patient, then teeth shapes of the images are designed based on esthetic restoration concepts and the pixel coordinates of the teeth’s contours are converted into the vertex coordinates in the three-dimensional space. Second, the dental mesh model is divided into three parts – active part, passive part and fixed part – based on the teeth’s contours of the mesh model. Third, the vertices from the teeth’s contours of the dental model are matched with ones from the intra-oral image and with the help of matching operation, the target coordinates of each vertex in the active part can be calculated. Finally, the Laplacian-based deformation algorithm and mesh smoothing algorithm are performed.

Benefitting from the proposed method, the dental mesh model with esthetic restoration can be quickly obtained based on the intra-oral image that is the result of doctor-patient communication. Experimental results show that the quality of restoration meets clinical needs, and the typical time cost of the method is approximately one second. So the method is both time-saving and user-friendly.

The method provides the possibility to design personalized dental esthetic restoration solutions rapidly.

Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure.

The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process.

The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well.

The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.

The objective of the present work was to perform a detailed numerical study of laminar forced convection in a three‐dimensional square duct packed with an isotropic granular material and saturated with a Newtonian fluid. Hydrodynamic and heat transfer results are reported for three different thermal boundary conditions. The flow in the porous medium was modeled using the semi‐empirical Brinkman‐Forchheimer‐extended Darcy model which also included the effects of variable porosity and thermal dispersion. Empirical models for variable porosity and thermal dispersion were determined based on existing three‐dimensional experimental measurements. Parametric studies were then conducted to investigate the effects of particle diameter, Reynolds number, Prandtl number and thermal conductivity ratio. The results showed that channeling phenomena and thermal dispersion effects are reduced considerably in a three‐dimensional duct compared with previously reported results for a two‐dimensional channel. It was found that the Reynolds number affects mainly the velocity gradient in the flow channeling region, while the particle diameter affects the width of the flow channeling region. As the Reynolds number increases or as the particle diameter decreases (i.e., when the inertia and thermal dispersion effects are enhanced), the Nusselt number increases. The effects of varing the Prandtl number on the magnitude of the Nusselt number were found to be more significant than those of the thermal conductivity ratio. Finally, the effects of varing the duct aspect ratio on the friction factor can be neglected for small particle diameter (D_p ≤ 0.01) or for high particle Reynolds number (Re_d ≥ 1000) due to the dominant bulk damping resistance from the porous matrix (Darcy term) or strong inertia effects (Forchheimer term), respectively.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

The purpose of this paper is to obtain evidence concerning the basic dimensions included in cognitive prototypes pertaining to opportunity recognition and decision to launch a new venture; identifying the underlying dimensions of both prototypes – the cognitive frameworks current or nascent entrepreneurs employ in performing these important tasks.

The bi-dimensional models were tested in a sample of 284 founder entrepreneurs, using a 48-item questionnaire. It was used as structural equation confirmatory factor analysis to compare fit indices of uni-dimensional second-order and third-order bi-dimensional models of business opportunity and decision to launch a venture.

Results support the bi-dimensional models and offer support that both prototypes include two basic dimensions. For the business opportunity prototype these are viability and distinctiveness while for the decision to launch a new venture, the basic dimensions are feasibility and motivational aspects.

These results help to further clarify the nature of the cognitive frameworks individuals use to identify potential opportunities and reach an initial decision about whether to pursue their development. Uncovering the cognitive functioning of opportunity recognition and decision to exploit it, allow individuals to recognize opportunities easier and successfully; and to make more accurate and effective decisions.

Knowing the basic dimensions of opportunity and decision-making prototypes contributes to develop effective skills with respect to business opportunity recognition among students enrolled in entrepreneurship programs. These surveys can be used for self-assessment and also for investors, tutors, and entrepreneurship agents in order to help evaluate features of business opportunities and decision to launch a venture.

This study embraces a conceptual contribution, proposing a different model of the business opportunity and decision to exploit prototypes, and it extends Baron and Ensley (2006) previous work, to another important step in the entrepreneurial process – the decision to develop an identified opportunity through the launch of a new venture.

The aim of this study is to optimize the process parameters of area-forming rapid prototyping system to improve the model dimensional repeatability and to minimize the process time as well.

Model dimensional repeatability is based on the dimensional standard deviation of the test sample. The significant factors that affect the model dimensional repeatability and process time are established by the fractional factorial design. Response surface methodology, based on the central composite design, is applied to evaluate the regression models of the response variables including prototype’s dimensional repeatability and processing time. Finally, a desirability function for each individual response variables is constructed to obtain the optimal process parameters.

The significant factors that have an impact on the main effects of response variables model dimensional repeatability and process time found by the fractional factorial design are curing time, light flux and platform moving velocity.

All previous studies were concerned with product accuracy in area-forming rapid prototyping system. In this work, we focus on optimization of model dimensional repeatability.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

Kriging surrogate model has demonstrated a powerful ability to be applied to a variety of engineering challenges by emulating time-consuming simulations. However, when it comes to problems with high-dimensional input variables, it may be difficult to obtain a model with high accuracy and efficiency due to the curse of dimensionality. To meet this challenge, an improved high-dimensional Kriging modeling method based on maximal information coefficient (MIC) is developed in this work.

The hyperparameter domain is first derived and the dataset of hyperparameter and likelihood function is collected by Latin Hypercube Sampling. MIC values are innovatively calculated from the dataset and used as prior knowledge for optimizing hyperparameters. Then, an auxiliary parameter is introduced to establish the relationship between MIC values and hyperparameters. Next, the hyperparameters are obtained by transforming the optimized auxiliary parameter. Finally, to further improve the modeling accuracy, a novel local optimization step is performed to discover more suitable hyperparameters.

The proposed method is then applied to five representative mathematical functions with dimensions ranging from 20 to 100 and an engineering case with 30 design variables.

The results show that the proposed high-dimensional Kriging modeling method can obtain more accurate results than the other three methods, and it has an acceptable modeling efficiency. Moreover, the proposed method is also suitable for high-dimensional problems with limited sample points.