基于神经网络的选区激光熔化残余应力预测

当前对选区激光熔化产生的残余应力预测方法主要为数值模拟，但由于设备、环境、粉末等因素差异性较大，且具有较大不确定性，很难建立符合实际情况的数值模拟模型。利用神经网络在预测多变量、复杂线性信息处理方面能力强的特点，建立适用于预测316L不锈钢粉末选区激光熔化残余应力的模型。使用选区激光熔化技术打印相当数量的不同工艺参数的试样，采用超声波检测其内部残余应力作为神经网络的训练样本，并使用这些样本对神经网络模型进行训练，获得具有预测功能的神经网络，将验证样本的工艺参数输入神经网络，计算出预测的残余应力值，与实际检测值进行对比。实验结果表明，预测值与实际测量值偏差较小，验证了所提方法的有效性。采用神经网络预测残余应力的方法，可以快速确定不同选区激光熔化工艺参数对应的残余应力，避免设置残余应力较高的工艺参数，有效缩短制备高质量工件试样的周期，降低成本。

Prediction of residual stress in selective laser melting based on neural network

At present, numerical simulation is the main method for predicting residual stress produced by selective laser melting. However, due to different factors such as equipment, environment and powder, it is difficult to establish a practical numerical model. The accuracy of numerical simulation needs to be verified. Based on the strong ability of neural network in predicting multivariable and complex linear information processing, a model for predicting residual stress in selective?laser?melting (SLM) is established. SLM technology is used to print a considerable number of samples with different process parameters, and ultrasonic wave is used to detect the internal residual stress. Training samples of neural network are used to train the neural network model, and the neural network with predictive function is obtained. The process parameters of the validated samples are input into the neural network, and the predicted residual stress value is calculated, which is in accordance with the actual situation. The detection values were compared. The experimental results show that the deviation between the predicted value and the measured value is small and the accuracy is high, which verifies the effectiveness of the proposed method.The method of predicting residual stress by neural network can quickly determine the residual stress corresponding to laser melting process parameters in different selection areas, avoid setting process parameters with high residual stress, effectively shorten the cycle of preparing high-quality workpiece samples and reduce the cost.