Prediction of laser drilled hole geometries from linear cutting operation by way of artificial neural networks

نویسندگان

چکیده

Abstract This paper deals on artificial intelligence (AI) application for the estimation of kerf geometry and hole diameters laser micro-cutting micro-drilling operations. To this aim cutting drilling operation were performed NIMONIC 263 superalloy sheet, 0.38 mm in nominal thickness, by way a 100 W fibre modulated wave regime. Linear cuts holes (by trepanning) fixing average power at 80 changing pulse duration, speed, focus depth path (the latter only operations). Kerf width holed diameter, upper downsides, measured digital microscopy. Different neural networks (ANNs) developed tested to predict widths (at downside). Two ANNs addressed linear process modelling; also, two further base features. The trained with subset data containing conditions kerf/hole geometry. ANN test was remaining data. results show that can model cut as function parameters. Moreover, is more efficient. Therefore, functional correlation between geometries achievable assessed.

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ژورنال

عنوان ژورنال: The International Journal of Advanced Manufacturing Technology

سال: 2021

ISSN: ['1433-3015', '0268-3768']

DOI: https://doi.org/10.1007/s00170-021-06857-2