This paper presents a review of the principal developments in functionally graded materials (FGMs) with an emphasis on the recent work published since 2000. Diverse areas relevant to various aspects of theory and applications of FGM are reflected in this paper. They include homogenization of particulate FGM, heat transfer issues, stress, stability and dynamic analyses, testing, manufacturing and design, applications, and fracture. The critical areas where further research is needed for a successful implementation of FGM in design are outlined in the conclusions. DOI: 10.1115/1.2777164

https://web.mst.edu/~vbirman/papers/Modeling%20and%20Analysis%20of%20FGM_Review_2007.pdf

Modeling and Analysis of Functionally Graded Materials and Structures

THE appearance of a treatise in English upon the mathematical theory of elasticity is an event the potential importance of which may be judged by the that the author, in his frequent suggestions for collateral reading, refers to only three such, those of Southwell, Timoshenko, and Love. In spirit and content Sokolnikoff}s book differs greatly from each and all of these. It may be described by a possible sub-title: “A pure mathematician surveys topics related to certain problems in the mathematical theory of elasticity”. It is symptomatic of the change in outlook of American mathematics over the past few decades. Mathematical Theory Of Elasticity Prof. I. S. Sokolnikoff with the collaboration of Asst. Prof. R. D. Speche. Pp. xi + 373. (New York and London: McGraw-Hill Book Co., Inc., 1946.) 22s. 6d.

Mathematical Theory Of Elasticity

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

An improved grey wolf optimizer for solving engineering problems

A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.

/pdf/binary-optimization-using-hybrid-grey-wolf-optimization-for-15yg1nd05e.pdf

Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection

In this experiment, seven software teams developed versions of the same small-size (2000-4000 source instruction) application software product. Four teams used the Specifying approach. Three teams used the Prototyping approach. The main results of the experiment were the following. 1) Prototyping yielded products with roughly equivalent performance, but with about 40 percent less code and 45 percent less effort. 2) The prototyped products rated somewhat lower on functionality and robustness, but higher on ease of use and ease of learning. 3) Specifying produced more coherent designs and software that was easier to integrate. The paper presents the experimental data supporting these and a number of additional conclusions.

Prototyping versus specifying: A multiproject experiment.

A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

/pdf/hybrid-algorithm-of-particle-swarm-optimization-and-grey-3twk97n6tu.pdf

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

A novel hybrid GWO-SCA approach for optimization problems

Modeling the anisotropy and creep in orthotropic aluminum–silicon carbide composite rotating disc

The creep behavior of a rotating disc made of isotropic composite containing varying amounts of silicon carbide in the radial direction has been investigated in the presence of a thermal gradient, also in the radial direction. The variation of silicon carbide content has been so tailored as to contain larger amounts of particles in a highly stressed region. This type of inhomogeneous material is known as Functionally Graded Material (FGM). The thermal gradient experienced by the disc is the result of braking action as estimated by FEM analysis. The creep behavior of the disc under stresses developing due to rotation has been determined following Sherby's law and compared with that of a similar disc following Norton's law. The difference in the distribution of stresses and strain rates in the discs does not follow any definite trend but the values are somewhat different. The presence of thermal gradient and a linear particle gradient separately or their simultaneous presence result in a significant decrease in steady state creep rates as compared to that in a composite disc with the same average particle content (20 vol %) distributed uniformly and operating under isothermal condition. Further, the study revealed that the creep behavior of a FGM disc could be significantly improved by increasing the gradient of particle distribution while keeping the same average particle content of 20 vol % silicon carbide in the disc.

Modeling of Creep Behavior of a Rotating Disc in the Presence of Both Composition and Thermal Gradients

The steady state creep in a rotating disc made of isotropic aluminium–silicon carbide particulate composite has been investigated in the present study. The creep behaviour of the composite has been described by Sherby's constitutive model. The creep parameters in the law have been determined using regression equations developed on the basis of available experimental results. The radial and tangential stresses and steady state creep rates in the disc have been calculated and presented for various combinations of material parameters (like particle size and particle content) and temperatures. The study revealed that for given operating conditions, the strain rates in the disc can be controlled by selecting optimum particle content and/or particle size of the reinforcement.

Satya Bir Singh

Papers

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

A novel hybrid GWO-SCA approach for optimization problems

Modeling the anisotropy and creep in orthotropic aluminum–silicon carbide composite rotating disc

Modeling of Creep Behavior of a Rotating Disc in the Presence of Both Composition and Thermal Gradients

Steady state creep and material parameters in a rotating disc of Al-SiCp composite