Christopher Gottlieb Klingaa, Sankhya Mohanty and Jesper Henri Hattel
Conformal cooling channels in additively manufactured molds are superior over conventional channels in terms of cooling control, part warpage and lead time. The heat transfer…
Abstract
Purpose
Conformal cooling channels in additively manufactured molds are superior over conventional channels in terms of cooling control, part warpage and lead time. The heat transfer ability of cooling channels is determined by their geometry and surface roughness. Laser powder bed fusion manufactured channels have an inherent process-induced dross formation that may significantly alter the actual shape of nominal channels. Therefore, it is crucial to be able to predict the expected surface roughness and changes in the geometry of metal additively manufactured conformal cooling channels. The purpose of this paper is to present a new methodology for predicting the realistic design of laser powder bed fusion channels.
Design/methodology/approach
This study proposes a methodology for making nominal channel design more realistic by the implementation of roughness prediction models. The models are used for altering the nominal shape of a channel to its predicted shape by point cloud analysis and manipulation.
Findings
A straight channel is investigated as a simple case study and validated against X-ray computed tomography measurements. The modified channel geometry is reconstructed and meshed, resulting in a predicted, more realistic version of the nominal geometry. The methodology is successfully tested on a torus shape and a simple conformal cooling channel design. Finally, the methodology is validated through a cooling test experiment and comparison with simulations.
Practical implications
Accurate prediction of channel surface roughness and geometry would lead toward more accurate modeling of cooling performance.
Originality/value
A robust start to finish method for realistic geometrical prediction of metal additive manufacturing cooling channels has yet to be proposed. The current study seeks to fill the gap.
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Bao Yong, Fan Yanqin, Su Liangjun and Zinde-Walsh Victoria
This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works…
Abstract
This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.
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Jack Allen, Housila P. Singh and Florentin Smarandache
This paper proposes a family of estimators of population mean using information on several auxiliary variables and analyzes its properties in the presence of measurement errors.
Abstract
This paper proposes a family of estimators of population mean using information on several auxiliary variables and analyzes its properties in the presence of measurement errors.
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Archana Goel, Rahul Dhiman, Sudhir Rana and Vimal Srivastava
This study aims to know whether board composition is effective in improving firm performance and particularly to determine whether this relationship varies across different levels…
Abstract
Purpose
This study aims to know whether board composition is effective in improving firm performance and particularly to determine whether this relationship varies across different levels of performance, that is, companies with very low performance, low performance, moderate performance, high performance and very high performance.
Design/methodology/approach
The authors use a data set covering 213 Indian companies registered on S&P Bombay Stock Exchange 500 Index over the period 2001 to 2019 by using Tobin's Q as a performance parameter. The study applies the quantile regression technique and compares the results with fixed effect generalized least squares (GLS) regression.
Findings
The findings reveal that board size positively affects the company's performance across all quantiles. Independent directors negatively impact the performance of companies across all quantiles. However, the strength of these relationships increases with increase in performance, thereby supporting agency theory and stewardship theory, respectively. The effect of executive directors on the performance of the companies varies across quantiles. The effect is adverse at moderate and high quantiles only.
Practical implications
The findings provide some grounds for regulators to exercise caution while designing board composition guidelines, keeping in mind the unique internal environment of each company which ultimately affects their performance levels. Similarly, Indian companies are also suggested to compose their boards keeping in mind their performance levels.
Originality/value
The study contributes towards the debate on the board composition and firm performance relationship by adding to the agency theory and stewardship theory that all the companies cannot have the similar board composition. Rather its composition depends upon the performance levels of the companies.