Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization

Kalita, Kanak; Shivakoti, Ishwer; Ghadai, Ranjan Kumar

doi:10.1080/10426914.2017.1303156

Cited by 67 publications

(23 citation statements)

References 26 publications

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“…The I D /I G ratio increased with an increase in the N 2 flow rate from 40 to 100 sccm. An increased I D /I G ratio signifies the increase in the sp 3 bond and is proportional to the H of the SiCN thin film [18]. The extent of the disorder can be defined in terms of nanocrystallite size (L a ) which can be calculated using the equation given below:…”

Section: Resultsmentioning

confidence: 99%

Effect of nitrogen flow rate on the mechanical properties of CVD-deposited SiCN thin films

et al. 2019

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Silicon carbonitride (SiCN) thin films were deposited on p-Si (100) substrates with different N 2 flow rates using SiC and Si 3 N 4 powder precursors by chemical vapour deposition. To investigate the structural, vibrational and mechanical properties, the SiCN thin films were characterized by atomic force microscopy, Raman spectroscopy, X-ray diffraction (XRD), Fourier transform infrared and nanoindentation techniques. The XRD results reveal nanocrystals embedded with amorphous networks in the SiCN thin films. An increase in the I D /I G ratio with an increase in the N 2 flow rate indicated the increase of sp 3 bonds in the SiCN thin film. The hardness (H), Young's modulus (E), plasticity index (H/E) and (H 3 /E 2) increase with an increase in the N 2 flow rate.

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Section: Resultsmentioning

confidence: 99%

Effect of nitrogen flow rate on the mechanical properties of CVD-deposited SiCN thin films

et al. 2019

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“…Further, to draw meaningful conclusions from these experiments, it is necessary that these experiments are statistically significant.The design of experiments based on response surface method (RSM) is perhaps the most favoured ones because often far lesser trials are needed as compared to a full factorial design. However, developing a statistically significant model is a much easier task as compared to searching a 3 or more-factor domain for maximising or minimising an output response [12]. It is physically impossible to perform experiments to determine an optimum parameter setting.…”

Section: Introductionmentioning

confidence: 99%

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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“…Recently, Shivakoti et al (2017) highlighted the use of fuzzy TOPSIS method for the selection of optimal laser micromarking process parameters to improve the marking performance on high strength temperature resistance material such as gallium nitride (GaN). In an another study Kalita et al (2017) performed an investigation with same workpiece material for optimization of process parameters in laser beam micromarking using genetic algorithm (GA) and particle swarm optimization (PSO). The results showed that there is a close agreement between the GA parameter settings with PSO results.…”

Section: Introductionmentioning

confidence: 99%

“…From the existing literature, most of the studies are on laser micromachining, viz. microdrilling (Biswas et al 2010;Kuar et al 2012;Nandi and Kuar 2015;Biswas et al 2015;Zhang et al 2015), microturning (Dhupal et al 2008;Kibria et al 2010Kibria et al , 2012Kibria et al , 2013Hao et al 2018), micromilling (Campanelli et al 2013;Mohammed et al 2017;Darwish et al 2017;Yang et al 2018) and micromarking (Shivakoti et al 2017;Kalita et al 2017;Peter et al 2013;Brihmat-Hamadi et al 2017); work in the field of laser microgrooving (Dhupal et al 2007;Kuar et al 2008) is very limited. Laser microgrooving is based on the interaction of a laser beam with the workpiece surface, non-uniform melting and ejection of material from the groove walls, and laser power reduction, as the beam propagates into the groove, can be identified as a cause for the variation in lower width and depth formation which is of prime importance to enhance the product quality.…”

Section: Introductionmentioning

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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Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization

Cited by 67 publications

References 26 publications

Effect of nitrogen flow rate on the mechanical properties of CVD-deposited SiCN thin films

Effect of nitrogen flow rate on the mechanical properties of CVD-deposited SiCN thin films

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

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