The selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box–Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.

参数的选择会影响增材制造过程中的表面粗糙度。本研究旨在确定输入参数的最佳组合，以使用级联前向神经网络 (CFNN) 和遗传算法来预测和最小化 3D 打印机上熔融沉积建模所产生的样品的表面粗糙度。采用具有三个级别的四个独立打印参数的Box-Behnken Design，并使用3D打印机制造了25个零件。对制造的零件进行粗糙度测试。混合算法生成的模型在预测和优化 3D 打印零件的表面粗糙度方面取得了最佳结果。与之前的随机测试结果相比，经过优化参数训练的 CFNN 的表面粗糙度预测精度更加准确。