A screening strategy for hot forging combining high-throughput forging experiment and machine learning

Abstract

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.

Keywords

• hot forging
• machine learning
• high-throughput experiment
• IN718