In this study, we proposed a screening strategy of processing conditions for hot forging based on high-throughput experiment equipment, numerical simulation, and machine learning to obtain the optimal conditions for the forging process. Nikle based superalloy IN718 was selected as an application case. We designed high-throughput experiment equipment for hot forging. Numerical simulation of the forging process on the equipment was studied, and a database of 625 examples was obtained. Two BP NN models for average grain size and maximum principal stress predictions, respectively, were trained. These two BP NN models were used to search different processing conditions in searching space consisting of 1 206 000 processing conditions, and an algorithm was designed to screen the processing conditions comprehensively considering the average grain size and the maximum principal stress in the bulge zone. The optimal conditions for different forging displacements were obtained. Compared with the traditional high-cost and time-consuming trial-and-error methods, the method proposed in this paper to optimize the processing technology has significant advantages. This method can be applied to pre-screening for material design and process optimization.

在本研究中，我们提出了一种基于高通量实验设备、数值模拟和机器学习的热锻加工条件筛选策略，以获得锻造过程的最佳条件。Nikle 基高温合金 IN718 被选为应用案例。我们设计了高通量的热锻实验设备。对该设备的锻造过程进行了数值模拟研究，获得了625个实例的数据库。训练了两个分别用于平均晶粒尺寸和最大主应力预测的 BP NN 模型。这两个 BP NN 模型用于在由 1 206 000 个加工条件组成的搜索空间中搜索不同的加工条件，并设计了一种算法来综合考虑平均晶粒尺寸和凸起区的最大主应力来筛选加工条件。得到了不同锻造位移的最佳条件。与传统的高成本、耗时的试错法相比，本文提出的优化加工工艺的方法具有显着优势。该方法可用于材料设计和工艺优化的预筛选。