ANN Prediction of Laser Power, Cutting Speed, and Number of Cut Annual Rings and Their Influence on Selected Cutting Characteristics of Spruce Wood for CO(2) Laser Processing

利用人工神经网络预测激光功率、切割速度和切割年轮数及其对云杉CO₂激光加工切割特性的影响

阅读:1

Abstract

In this work, we focus on the prediction of the influence of CO(2) laser parameters on the kerf properties of cut spruce wood. Laser kerf cutting is mainly characterized by the width of kerf and the width of the heat-affected zone, which depend on the laser power, cutting speed, and structure of the cut wood, represented by the number of cut annual rings. According to the measurement results and ANN prediction results, for lower values of the laser power (P) and cutting speed (v), the effect of annual rings (ARs) is non-negligible. The results of the sensitivity analysis show that the effect of v increases at higher energy density (E) values. With P in the range between 100 and 500 W, v values between 3 and 50 mm·s(-1), and AR numbers between 3 and 11, the combination of P = 200 W and v = 50 mm·s(-1), regardless of the AR value, leads to the best cut quality for spruce wood. In this paper, the main goal is to show how changes in the input parameters affect the characteristics of the cutting kerf and heat-affected zones for all possible input parameter values.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。