Free Vibration Analysis of Laminated Composites by a Nine Node Isoparametric Plate Bending Element

Proceedings of the Institution of Mechanical Engineers, Part C:

Dey

Haldar

2018

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The present research work explores genetically optimized skew laminates, whose stacking sequence has been varied to maximize their fundamental frequencies with the help of an efficient optimization algorithm. Genetic algorithm, rather than being applied blindly with empirical parameters, is tuned with respect to the problem at hand. Following an extensive study, genetic algorithm parameters are selected carefully so as to ensure a robust optimized stacking sequence. The sensitivity of ply angles is also investigated so as to warrant against marginal manufacturing perturbations. The safe limit for variation of ply angles without much hampering the frequency is also recommended. Validation with existing solutions illustrates the efficiency of the procedure. A wide range of results with rectangular/ skew plates of different layers having symmetric/ antisymmetric ply-orientations, subject to different boundary conditions are solved, which demonstrate the efficacy of the approach.

Section: Resultsmentioning

confidence: 99%

Robust genetically optimized skew laminates

Proceedings of the Institution of Mechanical Engineers, Part C:

Dey

Haldar

2018

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“…42 The criteria used here for selecting the best metamodel is that no term in the chosen metamodel should have a p-value > 0.1. 41,43 An interesting observation from Figure 9 is that not necessarily the highest R 2 pred metamodel is the best one. Researchers from other scientific fields like Chemometrics [16][17][18][19][20][21][22][23] have often criticized the insufficiency of R 2 as a model validation metric.…”

Section: Effect Of Number Of Termsmentioning

confidence: 94%

Search for accurate RSM metamodels for structural engineering

Journal of Reinforced Plastics and Composites

Dey

Haldar

2019

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With rapid advancement in computing power and development of numerical tools and scientific theories in fields like structural engineering, simple experiments can now be carried out in-silico. However, simulating many real-life phenomena in analytical fields still remains largely intractable or requires huge computational resources. A number of researchers have developed suitable metamodels to reduce the computational time needed to solve complex structural problems. As such, response surface method has become quite popular due to its versatility and ability to reduce even the most hard-to-model engineering problems into a simple polynomial form. The number and type of sampling points needed for building the response surface approximation are selected by design of experimentation techniques like Box-Behnken design, central composite design, D-optimal design, etc. One design may be appropriate for some particular problems, while a different design would perform better on others. To assess the performance of such metamodels, statistical measures like R 2 and error-based metrics like root-mean-squared error have been commonly used by researchers. In this paper, extensive numerical experiments are performed to gauge the performance of Box-Behnken design, central composite design and D-optimal designs as a metamodeling tool in structural engineering. The insufficiency of classical statistical accuracy measures like R 2 , R 2 adj and R 2 pred is demonstrated, and the need for measures based on external test data such as Q 2 F1 , Q 2 F2 or Q 2 F3 is stressed. Subsequently, some recommendations are also made on building metamodels for structural engineering problems.

“…For example, in US a 6.3% growth in 2014 was seen which translated as $8.2billion in value and 5.5 billion pounds as annual shipment (Mallick, 2007). Due to properties like high strength to weight ratio and lighter in weight, composites have been the ideal choice for replacement of conventional materials available in market where weight is an influential factor (Kalita, Ramachandran, Raichurkar, Mokal, & Haldar, 2016). Critical structures like aircraft like Boeing and Airbus are predominantly made up of composites.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of GFRP Composite Drilling Parameters and Genetic Algorithm-Based Optimisation

International Journal of Applied Metaheuristic Computing

Ghadai

Bansod

2021

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In this article, a genetic algorithm (GA) is used for optimizing a metamodel of surface roughness (R_a ) in drilling glass-fibre reinforced plastic (GFRP) composites. A response surface methodology (RSM) based three levels (-1, 0, 1) design of experiments is used for developing the metamodel. Analysis of variance (ANOVA) is undertaken to determine the importance of each process parameter in the developed metamodel. Subsequently, after detailed metamodel adequacy checks, the insignificant terms are dropped to make the established metamodel more rigorous and make accurate predictions. A sensitivity analysis of the independent variables on the output response helps in determining the most influential parameters. It is observed that f is the most crucial parameter, followed by the t and D. The optimization results depict that the R_a increases as the f increases and a minor value of drill diameter is the most appropriate to attain minimum surface roughness. Finally, a robustness test of the predicted GA solution is carried out.