Ultrasonic Nanocrystal Surface Modification (UNSM) is known as one of the surface treatment techniques that utilizes an ultrasonic vibration energy to improve mechanical properties and performance of materials. The dynamic nature of this surface treatment process deforms the top surface and subsurface of materials with a very high strain rate, which makes the direct observation of residual stress and refined layers very difficult. In this study, a novel alternative approach is proposed that is based on the artificial neural network (ANN) concept for predicting residual stress and hardness of various nickel-based alloys that have been subjected to UNSM treatment. Experimental measurement data were used in the ANN training process and validation. The trained model showed the capability of predicting residual stress and hardness accurately with a Pearson correlation value (R) of 0.988 and 0.996, a root mean squared error (RMSE) of 84.231 and 17.028, and a mean absolute error (MAE) of 68.586 and 13.450 for residual stress and hardness models when tested using the test dataset, respectively. It can be concluded that ANN as the alternative approach is a suitable method for accurately performing prediction for practical use in the absence of a mathematical model. Since the experimental result was used in the ANN model training process, the predicted result by the ANN model appears to agree with the experimental results of the UNSM treatment. Because of these demonstration results, the ANN-based prediction model can be used as a tool to optimize the UNSM treatment parameters.

超声纳米晶体表面改性（UNSM）是一种表面处理技术，它利用超声振动能来改善材料的机械性能和性能。这种表面处理过程的动态特性使材料的顶面和子面以很高的应变速率变形，这使得直接观察残余应力和精炼层非常困难。在这项研究中，提出了一种新的替代方法，该方法基于人工神经网络（ANN）概念，用于预测经过UNSM处理的各种镍基合金的残余应力和硬度。实验测量数据用于ANN训练过程和验证。经过训练的模型显示出准确预测残余应力和硬度的能力，皮尔森相关值（R）为0.988和0.996，均方根误差（RMSE）为84.231和17.028，平均绝对误差（MAE）为68.586和分别使用测试数据集进行测试时，残余应力模型和硬度模型为13.450。可以得出的结论是，在没有数学模型的情况下，作为替代方法的ANN是一种适合实际使用的准确预测方法。由于将实验结果用于ANN模型训练过程，因此ANN模型的预测结果似乎与UNSM处理的实验结果一致。由于这些演示结果，基于ANN的预测模型可以用作优化UNSM治疗参数的工具。