基于人工神经网络的磨料水射流切削工艺建模

磨料水射流（AWJ）切割工艺已经被遍及世界的许多车间所采用，其优点广为人知。为了进行精密加工，如精密切割、铣削、钻孔和磨削等，必须精确预测AWJ的侵蚀深度。文章基于人工神经网络（ANN）对AWJ切割工艺进行建模。模型采用三层结构，输入变量有水射流压力、水喷嘴直径、磨料粒子粒度（直径）、磨料流量和切割头进给速度。输出量为AWJ的切割深度。样本数据在JJ-Ⅰ水射流切割机床上实验获取，A3钢样板作为切割试件。采用改进的BP算法和样本数据对建立的人工神经网络进行训练。训练好的网络以一定精度建立了AWJ切割工艺中各参数之间的映射关系。所建模型可以精确预测AWJ的切深。将该模型集成到AWJ切割机床的计算机数控器中，可以实现AWJ精密加工。

Modeling of Abrasive Waterjet Cutting Process Based on Artificial Neural Network

Abrasive water jet cutting is widely accepted by the machine shop over the world and its advantages are well known.In order to perform precise machining,such as precise cutting,milling,drilling and grinding etc.,it is necessary to precisely predict the cutting depth of abrasive water jet.Based on the artificial neural network(ANN),a model for the abrasive water jet cutting process is created.Three layers network topology is adopted.The input variables are water pressure,diameter of orifice,diameter of abrasive particles,flow rate of abrasive and feed rate of cutting head respectively.The output variables are cutting depth of abrasive water jet.The sample data set is collected on JJ-I water jet machining and mild steel A3 is used as the cutting sample material.The network is then trained based on sample data set using improved BP algorithm.The trained network establishes nonlinear relationships among the parameters of abrasive water jet cutting process at given accuracy.Thereafter the cutting depth can be precisely predicted using this ANN model.The precise machining process can be achieved by integrated this model into computer numerical controller.