# Global residual stress field inference method for die-forging structural parts based on fusion of monitoring data and distribution prior

## TL;DR

This paper introduces a new method to accurately infer residual stress fields in die-forged aircraft parts by combining monitoring data with prior knowledge.

## Contribution

A novel global residual stress field inference method that integrates monitoring data and distribution prior knowledge for irregular die-forging parts.

## Key findings

- Prior knowledge from finite element analysis reduces parameters needed for stress field inference.
- The method was validated in both simulation and real-world environments.
- It addresses ill-conditioned problems caused by complex stress distributions in irregular geometries.

## Abstract

Die-forging structural parts are widely used in the main load-bearing components of aircrafts because of their excellent mechanical properties and fatigue resistance. However, the forming and heat treatment processes of die-forging structural parts are complex, leading to high levels of internal stress and a complex distribution of residual stress fields (RSFs), which affect the deformation, fatigue life, and failure of structural parts throughout their lifecycles. Hence, the global RSF can provide the basis for process control. The existing RSF inference method based on deformation force data can utilize monitoring data to infer the global RSF of a regular part. However, owing to the irregular geometry of die-forging structural parts and the complexity of the RSF, it is challenging to solve ill-conditioned problems during the inference process, which makes it difficult to obtain the RSF accurately. This paper presents a global RSF inference method for the die-forging structural parts based on the fusion of monitoring data and distribution prior. Prior knowledge was derived from the RSF distribution trends obtained through finite element analysis. This enables the low-dimensional characterization of the RSF, reducing the number of parameters required to solve the equations. The effectiveness of this method was validated in both simulation and actual environments.