Prediction model for determining the optimum operational parameters in laser forming of fiber-reinforced composites

• 作者: Andrea De Bartolomeis Annamaria Gisario Atabak Rahimzadeh Massimiliano Barletta Mehrshad Mehrpouya
• 来源期刊: 先进制造进展（英文版）
• 关键词: Laser forming (LF) Fiber-reinforced composite Fiber-metal laminates (FMLs) Glass laminate aluminum reinforced epoxy (GLARE) Artificial neural networks (ANNs)
• 年，卷（期）: 2020,（2）
• 页码范围: 242-251
• 页数: 10页
• 语种: 英文

摘要

Composite materials are widely employed in various industries,such as aerospace,automobile,and sports equipment,owing to their lightweight and strong structure in comparison with conventional materials.Laser material processing is a rapid technique for performing the various processes on composite materials.In particular,laser forming is a flexible and reliable approach for shaping fiber-metal laminates (FMLs),which are widely used in the aerospace industry due to several advantages,such as high strength and light weight.In this study,a prediction model was developed for determining the optimal laser parameters (power and speed) when forming FML composites.Artificial neural networks (ANNs) were applied to estimate the process outputs (temperature and bending angle) as a result of the modeling process.For this purpose,several ANN models were developed using various strategies.Finally,the achieved results demonstrated the advantage of the models for predicting the optimal operational parameters.