Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

Behera, Rasmi Ranjan; Ghadai, Ranjan Kumar; Kalita, Kanak; Banerjee, Sanjib

doi:10.1007/s12588-016-9163-2

Cited by 33 publications

(19 citation statements)

References 35 publications

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“…Overall from all the interaction plots, it is evident that at alow feed rate and low spindle speed, delamination is lesser. This is in direct agreement with the conclusions ofBehera et al [18]; Vankanti and Ganta [20]; and Hansda and Banerjee [21]. Vankanti and Ganta [20]have used an L9 orthogonal array for thedesign of the experiments and used an ANOVA test to determine the significance of the parameters.…”

supporting

confidence: 88%

Optimizing Drilling Induced Delamination in GFRP Composites using Genetic Algorithm& Particle Swarm Optimisation

Kalita

Mallick

Bhoi

et al. 2018

Advanced Composites Letters

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Composites are widely used in several applications ranging from automotive to aircraft industry due to their high strength to weight ratio. More often than not drilling on these composite laminates are conducted to serve some functional or aesthetic requirement. Delamination caused due to drilling pose a severe problem to the integrity of the structure. It is often not possible to develop an exact mathematical model to predict the delamination associated with such drilling. So, in this paper, an empirical model is developed based on the extensive experiments performed on polyester composite reinforced with chopped fibreglass. To account for the various parameters a Box-Behnken design of experiments is conducted for four parameters (material thickness, drill diameter, spindle speed, and feed rate) each having threedistinct levels. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques are then used for predicting the global optimum (minimum delamination factor). The performance of both GA and PSO in terms of predicting the global optimum is found to be same. However, PSO converged much faster and required far lesser computational time.

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supporting

confidence: 88%

Optimizing Drilling Induced Delamination in GFRP Composites using Genetic Algorithm& Particle Swarm Optimisation

Kalita

Mallick

Bhoi

et al. 2018

Advanced Composites Letters

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“…The training data set of desired outputs generated utilizing experimental data set, as reflected in Table 3. In order to get a closed-form solution, MATLAB function feed-forward back-propagation neural network (TRAINGD) was used for training the data of the network, which has been widely used for prediction and optimization of various machining processes (Dhar et al 2008;Dhupal et al 2009;Somashekhar et al 2010;Kuo et al 2012;Dahbi et al 2017;Behera et al 2016). TRAINGD is a network training function which works in accordance with the gradient descent method that measures the output error, calculates the gradient of the error by adjusting as well as updating the weight variable repetitively in the direction of the negative gradient of the performance function and also reduces the mean square error (MSE) between expected data and training data set.…”

Section: Model Prediction Using Artificial Neural Networkmentioning

confidence: 99%

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

2018

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Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide (Al 2 O 3) ceramic material with pulsed Nd:YAG laser by considering the air pressure, lamp current, pulse frequency, pulse width and cutting speed as process parameters. Thirty-two sets of laser microgrooving trials based on central composite design (CCD) design of experiments (DOEs) are performed, and response surface method (RSM), artificial neural network (ANN) and genetic algorithm (GA) are subsequently applied for mathematical modeling and multi-response optimization. The performance of the predictive ANN model based on 5-8-8-3 architecture gave the minimum error (MSE = 0.000099) and presented highly promising to confidence with percentage error less than 3% in comparison with experimental result data set. The ANN model combined with GA leads to minimum deviation of upper width, lower width and depth value of − 0.0278 mm, 0.0102 mm and − 0.0308 mm, respectively, corresponding to optimum laser microgrooving process parameters such as 1.2 kgf/cm 2 of air pressure, 19.5 Amp of lamp current, 4 kHz of pulse frequency, 6% of pulse width and 24 mm/s of cutting speed. Finally, the results have been verified by performing a confirmatory test.

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“…The most widely used metamodeling technique for the optimization of manufacturing /machining process is the Response Surface Methodology (RSM), which is essentially a polynomial regression approach [9][10][11][12][13]. Some authors have also used some advanced metamodeling techniques like artificial neural network (ANN) [14][15][16], ANFIS [17], gene expression programming (GEP) [3] in the machining operation. By developing the metamodel it can be coupled with a global optimization algorithm to find the optimum machining parameters.…”

Section: Introductionmentioning

confidence: 99%

Symbolic regression metamodel based multi-response optimization of EDM process

Kumar¹,

Kalita²,

Gao³

2020

FME Transactions

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Electrical Discharge Machining (EDM) is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials. It does not require a cutting tool and can machine complex geometries easily. However, it suffers from drawbacks like a poor rate of machining and excessive tool wear. In this research, an attempt is made to address these issues by using a metamodel coupled with global optimization approach to predict suitable combinations of input parameters (current, pulse on-time and pulse off-time) that would effectively increase the material removal rate and minimize the tool wear. The metamodels are built by using a novel symbolic regression approach carried out using Genetic Programming (GP). On comparative evaluation against traditional response surface methodology (RSM) metamodels, the GP metamodels show much better and accurate estimation. GP metamodels are then coupled with a genetic algorithm to carry out multiobjective optimization of the EDM process.

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Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

Cited by 33 publications

References 35 publications

Optimizing Drilling Induced Delamination in GFRP Composites using Genetic Algorithm& Particle Swarm Optimisation

Optimizing Drilling Induced Delamination in GFRP Composites using Genetic Algorithm& Particle Swarm Optimisation

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

Symbolic regression metamodel based multi-response optimization of EDM process

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