Combining the Taguchi method with artificial neural network to construct a prediction model of a CO2 laser cutting experiment

Abstract

When using the Taguchi method, an L18 or L27 orthogonal array is usually adopted. However, this requires many experiments (18 or 27 runs, respectively), consuming time, and resources. This study proposes a progressive Taguchi neural network model, which combines the Taguchi method with the artificial neural network to construct a prediction model for a CO 2 laser cutting experiment. During CO 2 laser cutting, energy from the moving laser is accumulative. The paper develops an integral equation of energy density during laser beam movement and lets it determine the sliding level of control factor. Meanwhile, the paper proposes that in Stage 1, only less number of experiments is required to be conducted by L9 orthogonal array. After the crucial supplementary experimental training samples proposed in Stage 2 are also included, high-accuracy prediction of artificial neural network can be completed. Based on analysis from the progressive Taguchi neural network, the Stage 1 preliminary network-with only a few available experimental examples-has achieved good predictive ability from regions near the Taguchi control points. For regions further out, the predictions have been increasingly unreliable. Nevertheless, the high precision of Stage 2 Taguchi network has good predictive results for all regions.

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

“…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).…”

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“…Chaki and Ghosal [14] developed simulated annealing hybrid with ANN model for predicting the quality of cut during laser cutting of mild steel plates and concluded that optimization using this hybrid simulated annealing with ANN optimization yields good accuracy. Yang et al [15] combined Taguchi with ANN model for the prediction of responses in laser cutting and confirmed that the training samples can be reduced by this hybrid approach. Syn et al [16] utilized fuzzy logic for predicting the dross and surface roughness and found that the fuzzy model exhibits a good correlation with the experimental results.…”

St-37 Malzemesinin Lazer Kesiminde İşleme Parametrelerinin Deneysel ve İstatiksel Olarak İncelenmesi

“…Lazer işleme teknolojisi, kullanım kolaylığı, yüksek hassasiyet, düşük maliyet, yüksek parça kalitesi, yüksek işleme hızı ve daha az parça firesi nedeniyle imalat endüstrisinin birçok alanında yaygın olarak kullanılmaktadır. Bu özelliklere ilaveten, temassız olarak kesilmesi zor malzemeler, kırılgan malzemeler, iletken ve iletken olmayan malzemeler, yumuşak ve ince malzemeler gibi gelişmiş mühendislik malzemelerin işlenmesi için kullanılmaktadır [1][2][3]. Bununla birlikte lazerle işleme sırasında, kesme bölgesinde iş parçası yüksek sıcaklıklara maruz kalmaktadır.…”