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Comparative analysis of laser profile cutting of Ni-based superalloy sheet using RSM and ANN
Laser beam cutting (LBC) is widely accepted method for creating the complex geometries in difficult to cut sheetmetal with close tolerances and high precision. In this study, the curved profiles have been cut in Ni-based superalloy (Inconel718) thin sheet using pulsed Nd-YAG laser. The performance of the process in terms of top kerf deviation (TKD) and kerf taper (KT) has been analyzed by employing the response surface modelling (RSM) and neural network modelling (NNM) approaches. Experiments have been conducted using the Box-Behnken design (BBD) by considering the four laser cutting parameters namely lamp current, pulse width, pulse frequency and cutting speed. The experimental results have been used further to develop the regression and neural network models. Subsequently, predicted results from both the models have been compared with the actual experimental results. On comparison, the prediction error for RSM were found as 14.06% and 14.18%, while for NNM were 12.23% and 6.80% for TKD and KT, respectively. Hence, both the models confirmed strong ability to match the responses. NN model exhibited superior predictive power.
Comparative analysis of laser profile cutting of Ni-based superalloy sheet using RSM and ANN
Laser beam cutting (LBC) is widely accepted method for creating the complex geometries in difficult to cut sheetmetal with close tolerances and high precision. In this study, the curved profiles have been cut in Ni-based superalloy (Inconel718) thin sheet using pulsed Nd-YAG laser. The performance of the process in terms of top kerf deviation (TKD) and kerf taper (KT) has been analyzed by employing the response surface modelling (RSM) and neural network modelling (NNM) approaches. Experiments have been conducted using the Box-Behnken design (BBD) by considering the four laser cutting parameters namely lamp current, pulse width, pulse frequency and cutting speed. The experimental results have been used further to develop the regression and neural network models. Subsequently, predicted results from both the models have been compared with the actual experimental results. On comparison, the prediction error for RSM were found as 14.06% and 14.18%, while for NNM were 12.23% and 6.80% for TKD and KT, respectively. Hence, both the models confirmed strong ability to match the responses. NN model exhibited superior predictive power.
Comparative analysis of laser profile cutting of Ni-based superalloy sheet using RSM and ANN
Int J Interact Des Manuf
Sharma, Amit (author) / Joshi, Priyanka (author)
2024-07-01
13 pages
Article (Journal)
Electronic Resource
English
Comparative analysis of laser profile cutting of Ni-based superalloy sheet using RSM and ANN
Springer Verlag | 2024
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