Laser cutting of wood: experimental study for machine learning
by George-Christopher Vosniakos; Ioanna Kyriopoulou; Panorios Benardos; Dimitrios Pantelis
International Journal of Mechatronics and Manufacturing Systems (IJMMS), Vol. 17, No. 3, 2024

Abstract: The use of CO₂ lasers for cutting wood patterns has become a common practice. The aim of this work is the quantitative correlation of the laser cut surface characteristics such as roughness, flatness and burning, with the process parameters. For this purpose, cuts were made using a CO₂ laser, with a maximum power of 3 kW. The way in which the type of wood, power of the laser, feed, focusing distance of the laser beam and pressure of the auxiliary gas affect cutting quality was studied quantitatively for 4 types of wood, namely oak, Swedish pine, particle board and fiberboard (MDF). Cutting quality was quantified through kerf geometry, i.e., initial, neck, maximum and mean width, variation of kerf width through its length, as well as ripples along the thickness. Based on the experimental results, a set of feedforward type artificial neural networks (ANNs) were trained as predictors of part quality. The models are sufficiently accurate with generalisation error from 5% to 15% despite the restricted training dataset.

Online publication date: Fri, 13-Dec-2024

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