The use of grey relational analysis to determine laser cutting parameters for QFN packages with multiple performance characteristics

Tsai, Ming‐Jong; Li, Chen-Hao

doi:10.1016/j.optlastec.2009.03.006

Cited by 41 publications

(17 citation statements)

References 13 publications

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“…Another disadvantage is that the factors interaction can not be detected when using this approach. Therefore, other techniques, which overcome these obstacles, have to replace it, such as DOE, ANN etc [12][13]. A good literature review on the techniques used in optimizing certain manufacturing process and the selection of the appropriate technique has been outlined by Benyounis and Olabi [14].…”

Section: Design Of Experimentsmentioning

confidence: 99%

Investigating the CO2 laser cutting parameters of MDF wood composite material

Eltawahni

Olabi

Benyounis

2011

Optics & Laser Technology

106

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Laser cutting of medium density fibreboard (MDF) is a complicated process and the selection of the process parameters combinations is essential to get the highest quality of the cut section. This paper presents laser cutting of MDF based on design of experiments (DOE). CO 2 laser was used to cut three thicknesses 4, 6 and 9 mm of MDF panels. The process factors investigated are: laser power, cutting speed, air pressure and focal point position. In this work, cutting quality was evaluated by measuring, upper kerf width, lower kerf width, ratio between the upper kerf width to the lower kerf width, cut section roughness and the operating cost. The effect of each factor on the quality measures was determined and special graphs were drawn for this purpose. The optimal cutting combinations were presented in favours of high quality process output and in favours of low cutting cost.

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Section: Design Of Experimentsmentioning

confidence: 99%

Investigating the CO2 laser cutting parameters of MDF wood composite material

Eltawahni

Olabi

Benyounis

2011

Optics & Laser Technology

106

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“…Chiang et al 2006). Similarly, Tsai and Li (2009) used GRA to optimise multiresponse laser cutting process for QFN packages in semiconductors manufacturing, where the response weights are determined subjectively, by engineering experience. Other approaches assumed that the weights are equal for all responses (e.g.…”

Section: Multiresponse Optimisation Based On Grey Relational Analysismentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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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%

“…RSM has been used with weighted sum of separately developed regression models for each quality parameters as objective functions for optimisation of HAZ and kerf taper during cutting of carbon fibre reinforced plastic (CFRP) composites (Mathew et al 1999). During GRA weighted sum of grey relational coefficients is used to compute grey relational grade to determine optimum solution during optimisation of surface roughness (Caydas and Hascalık 2008;Li and Tsai 2009), kerf quality ) and HAZ (Caydas and Hascalık 2008;Tsai and Li 2009;Li and Tsai 2009)while cutting St-37 steel (Caydas and Hascalık 2008), quad flat non-lead (QFN) strip ) and electronic printed circuit board ). Recently, hybrid approach of TM and RSM (Dubey and Yadava 2008a, b) has been incorporated for optimisation of pulsed Nd:YAG laser cutting process.…”

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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The use of grey relational analysis to determine laser cutting parameters for QFN packages with multiple performance characteristics

Cited by 41 publications

References 13 publications

Investigating the CO2 laser cutting parameters of MDF wood composite material

Investigating the CO2 laser cutting parameters of MDF wood composite material

Review of Multiresponse Process Optimisation Methods

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

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