Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

Tsai, Ming‐Jong; Li, Chen-Hao; Chen, Cheng-Che

doi:10.1016/j.jmatprotec.2007.12.138

Cited by 55 publications

(23 citation statements)

References 28 publications

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“…In the field of laser cutting, most of the time the optimal cutting conditions are determined using the Taguchi method (Prajapati et al, 2013) and by coupling response surface models (Sivarao et al, 2013) and ANNs models with different optimization and metaheuristic algorithms such as particle swarm optimization (Ciurana et al, 2009), genetic algorithm (GA) (Tsai et al, 2008), and simulated annealing (Chaki & Ghosal, 2011). The open literature reveals several research attempts based on ANNs such as for modeling and optimization of laser micromachining process (Ciurana et al, 2009;Biswas et al, 2010;Dhara et al, 2008;Dhupal et al, 2007), selection of optimal laser cutting parameters through integration of ANNs with GA (Tsai et al, 2008;Ghoreishi & Nakhjavani 2008), development of a prediction model through integration with the Taguchi method (Yang et al, 2012) and parametric modeling and optimization of lasox cutting (Chaki & Ghosal, 2011). The ANNs are the learning algorithms and mathematical models, which imitate the information processing capability of human brain and can be applied to non-linear and complex data, even if the data are imprecise and noisy (Raja et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Madić¹,

Radovanović²,

Gostimirović³

2015

10.5267/j.ijiec

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In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO 2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN) trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi's L 27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

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Section: Introductionmentioning

confidence: 99%

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Madić¹,

Radovanović²,

Gostimirović³

2015

10.5267/j.ijiec

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“…[16][17][18], different non traditional optimization algorithms like genetic algorithm (GA) and simulated annealing (SA) are also found to have potential in solving complex engineering problems. A few applications of GA have been observed for optimization of process parameters in laser cutting [19] and laser welding [20] where a user defined objective function has been used to optimize process parameters. However, SA is another potential optimization method with an ability to avoid becoming trapped in local minima.…”

Section: Work Piecementioning

confidence: 99%

Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Chaki¹,

Ghosal

2011

Prod. Eng. Res. Devel.

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Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of operational parameters with reasonable accuracy for any experimental dataset.

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“…Optimisation of process parameters for pulsed Nd:YAG LBC have been carried out by researchers based on statistical design of experiment (DOE) methodologies such as Taguchi method (TM) (Dubey and Yadava 2008a, b;Sharma et al 2010), response surface methodology (RSM) (Mathew et al 1999), grey relational analysis(GRA) (Caydas and Hascalık 2008;Tsai and Li 2009;Li and Tsai 2009), and soft computing based techniques like genetic algorithm (GA) (Tsai et al 2008). In TM, weighted effects of signal-to-noise (S/N) ratio of quality characteristics has been used as objective function for optimisation of kerf qualities during cutting of aluminium alloys (Dubey and Yadava 2008a, b) and nickel based super alloy (Sharma et al 2010) plates.…”

Section: Introductionmentioning

confidence: 99%

“…Takeyasu and Kainosho (2014) have devised the objective function to a scheme for international logistics problem which considers a reduced cost for the volume of lots and have successfully employed GA with a new selection method, Multi-step tournament selection method for solving the problem. Tsai et al (2008) have employed GA for optimizing process parameters during laser cutting of QFN packages. In this work, the optimisation has been carried out using weighted sum of separately developed regression models for each quality parameters as objective functions.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model

et al. 2015

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The paper presents an integrated model of artificial neural networks (ANNs) and non-dominated sorting genetic algorithm (NSGAII) for prediction and optimization of quality characteristics during pulsed Nd:YAG laser cutting of aluminium alloy. A full factorial experiment has been conducted where cutting speed, pulse energy and pulse width are considered as controllable input parameters with surface roughness and material removal rate as output to generate the dataset for the model. In ANN-NSGAII model, back propagation ANN trained with Bayesian regularization algorithm is used for prediction and computation of fitness value during NSGAII optimization. NSGAII generates complete set of optimal solution with pareto-optimal front for outputs. Prediction accuracy of ANN module is indicated by around 1.5 % low mean absolute % error. Experimental validation of B Sudipto Chaki sudipto_chaki@yahoo.co.in Ravi N. Bathe optimized output results less than 1 % error only. Characterization of the process parameters in pareto-optimal region has been explained in detail. Significance of controllable parameters of laser on outputs is also discussed.

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Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

Cited by 55 publications

References 28 publications

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model

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