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21 – 30 of 132M. Kathiresan and T. Sornakumar
Metal matrix composites (MMCs) are engineered materials formed by the combination of metal matrix and reinforcement materials. They have a stiff and hard reinforcing phase in…
Abstract
Purpose
Metal matrix composites (MMCs) are engineered materials formed by the combination of metal matrix and reinforcement materials. They have a stiff and hard reinforcing phase in metallic matrix. The matrix includes metals such as aluminum, magnesium, copper and their alloys. The purpose of this paper is to describe the development of an aluminum alloy‐aluminum oxide composite using a new combination of vortex method and pressure die casting technique and the subsequent tribological studies.
Design/methodology/approach
An aluminum alloy‐aluminum oxide composite was developed using vortex method and pressure die casting technique. The aluminum alloy‐1 wt% aluminum oxide was die cast using LM24 aluminum alloy as the matrix material and aluminum oxide particles of average particle size of 16 μm as a reinforcement material. The friction and wear characteristics of the composite were assessed using a pin‐on‐disc set‐up; the test specimen, 8‐mm diameter cylindrical specimens of the composite, was mated against hardened En 36 steel disc of 65 HRC. The tests were conducted with normal loads of 9.8, 29.4 and 49 N and sliding speeds of 3, 4 and 5 m/s for a sliding distance of 5,000 m. The frictional load and the wear were measured at regular intervals of sliding distance.
Findings
The effects of normal load and sliding speed on tribological properties of the MMC pin on sliding with En 36 steel disc were evaluated. The wear rate increases with normal load and sliding speed. The specific wear rate marginally decreases with normal load. The coefficient of friction decreases with normal load and sliding speed. The wear and friction coefficient of the aluminum alloy‐aluminum oxide MMC are lower than the plain aluminum alloy. The wear and coefficient of friction of the entire specimens are lower.
Practical implications
The development of aluminum alloy‐aluminum oxide composite using vortex method and pressure die casting technique will revolutionize the automobile and other industries, since a near net shape at low cost and very good mechanical properties are obtained.
Originality/value
There are few papers available on the development of (or tribological studies of) MMCs including aluminium/aluminium alloy‐ceramic composites developed by combination of vortex method and pressure die casting technique.
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Jane M. Binner, Graham Kendall and Alicia Gazely
This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices…
Abstract
This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis-à-vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.
Jane M. Binner, Thomas Elger, Birger Nilsson and Jonathan A. Tepper
The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural…
Abstract
The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is U.K. inflation and we utilize monthly data from 1969 to 2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast horizons. Both non-linear models perform significantly better than the VAR model.
Asli Ogunc and Randall C. Campbell
Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series…
Abstract
Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.
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Vincent A. Schmidt and Jane M. Binner
This chapter introduces a mechanism for generating a series of rules that characterize the money-price relationship for the United States, defined as the relationship between the…
Abstract
This chapter introduces a mechanism for generating a series of rules that characterize the money-price relationship for the United States, defined as the relationship between the rate of growth of the money supply and inflation. Monetary Services Indicator (MSI) component data is used to train a selection of candidate feedforward neural networks. The selected network is mined for rules, expressed in human-readable and machine-executable form. The rule and network accuracy are compared, and expert commentary is made on the readability and reliability of the extracted rule set. The ultimate goal of this research is to produce rules that meaningfully and accurately describe inflation in terms of the MSI component dataset.11Paper cleared for public release AFRL/WS–07–0848.
Vincent A. Schmidt and Jane M. Binner
Divisia component data is used in the training of an Aggregate Feedforward Neural Network (AFFNN), a general-purpose connectionist system designed to assist with data mining…
Abstract
Divisia component data is used in the training of an Aggregate Feedforward Neural Network (AFFNN), a general-purpose connectionist system designed to assist with data mining activities. The neural network is able to learn the money-price relationship, defined as the relationships between the rate of growth of the money supply and inflation. Learned relationships are expressed in terms of an automatically generated series of human-readable and machine-executable rules, shown to meaningfully and accurately describe inflation in terms of the original values of the Divisia component dataset.