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Nikhilesh Chawla

Bio: Nikhilesh Chawla is an academic researcher from Purdue University. The author has contributed to research in topics: Microstructure & Ultimate tensile strength. The author has an hindex of 57, co-authored 326 publications receiving 11083 citations. Previous affiliations of Nikhilesh Chawla include University of Michigan & Arizona State University.


Papers
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TL;DR: In this article, the main focus is on wrought particulate reinforced light alloy matrix systems, with a particular emphasis on tensile, creep, and fatigue behavior, and a review captures the salient features of experimental as well as analytical and computational characterization of the mechanical behavior of MMCs.
Abstract: Metal matrix composites provide significantly enhanced properties — like higher strength, stiffness and weight savings — in comparison to conventional monolithic materials. Particle reinforced MMCs are attractive due to their cost-effectiveness, isotropic properties, and their ability to be processed using similar technology used for monolithic materials. This review captures the salient features of experimental as well as analytical and computational characterization of the mechanical behavior of MMCs. The main focus is on wrought particulate reinforced light alloy matrix systems, with a particular emphasis on tensile, creep, and fatigue behavior.

641 citations

Journal ArticleDOI
TL;DR: In this paper, the failure mechanisms of sisal fiber failure were described and discussed in terms of the fiber microstructure as well as defects in the fibers, and the cross-sectional area of the fibers was measured using scanning electron microscope (SEM) micrographs and image analysis.

345 citations

Journal ArticleDOI
TL;DR: In this article, the microstructure and mechanical properties of sintered Fe 0.85Mo-Ni steels were investigated as a function of their density and pore size, shape, and distribution.
Abstract: The microstructure and mechanical properties of sintered Fe–0.85Mo–Ni steels were investigated as a function of sintered density. A quantitative analysis of microstructure was correlated with tensile and fatigue behavior to understand the influence of pore size, shape, and distribution on mechanical behavior. Tensile strength, Young's modulus, strain-to-failure, and fatigue strength all increased with a decrease in porosity. The decrease in Young's modulus with increasing porosity was predicted by analytical modeling. Two-dimensional microstructure-based finite element modeling showed that the enhanced tensile and fatigue behavior of the denser steels could be attributed to smaller, more homogeneous, and more spherical porosity which resulted in more homogeneous deformation and decreased strain localization in the material. The implications of pore size, morphology, and distribution on the mechanical behavior and fracture of P/M steels are discussed.

296 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used nanoindentation to extract the elastic and plastic properties of Cu, Sn-3.5Ag solder, Ag3Sn, Cu6Sn5, and Cu3Sn.

286 citations

Book
02 Aug 1972
TL;DR: In this article, important techniques to process metal matrix composites are described, then the interface region and its characteristics, properties of different metal matrix composite composites, and finally, the authors summarize different applications of metal matrices composites.
Abstract: Metal matrix composites consist of a metal or an alloy as the continuous matrix and a reinforcement that can be particle, short fiber or whisker, or continuous fiber. In this chapter, we first describe important techniques to process metal matrix composites, then we describe the interface region and its characteristics, properties of different metal matrix composites, and finally, we summarize different applications of metal matrix composites.

264 citations


Cited by
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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: In this paper, the influence of alloy chemistry, thermomechanical processing and surface condition on these properties is discussed and various surface modification techniques to achieve superior biocompatibility, higher wear and corrosion resistance.

4,113 citations

Journal ArticleDOI
TL;DR: The metal matrix composites (MMCs) have been transformed from a topic of scientific and intellectual interest to a material of broad technological and commercial significance over the past two decades as mentioned in this paper.

1,512 citations