A Generalised Approach on Kerf Geometry Prediction during CO2 Laser cut of PMMA Thin Plates using Neural Networks

نویسندگان

چکیده

This study presents an application of feedforward and backpropagation neural network (FFBP-NN) for predicting the kerf characteristics, i.e. width in three different distances from surface (upper, middle down) angle during laser cutting 4 mm PMMA (polymethyl methacrylate) thin plates. Stand-off distance (SoD: 7, 8 9 mm), speed (CS: 8, 13 18 mm/sec) power (LP: 82.5, 90 97.5 W) are studied parameters low CO2 cutting. A three-parameter three-level full factorial array has been used, twenty-seven (33) cuts performed. Subsequently, upper, down widths (Wu, Wm Wd) (KA) were measured analysed through ANOM (analysis means), ANOVA variances) interaction plots. The statistical analysis highlighted that linear modelling is insufficient precise prediction characteristics. An FFBP-NN was developed, trained, validated generalised accurate geometry. achieved R-all value 0.98, contrast to models, which Rsq values about 0.86. According plots, parameter optimize KA resulting positive close zero degrees 7 SoD, mm/s CS W LP.

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ژورنال

عنوان ژورنال: Lasers in manufacturing and materials processing

سال: 2021

ISSN: ['2196-7237', '2196-7229']

DOI: https://doi.org/10.1007/s40516-021-00152-4