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<p>The residual stress within material is one of the main reasons for machining deformation, and it is more severe for die-forged blanks. A challenge is posed for residual stress field inference by the extremely complex distribution of the residual stress field in die-forged blanks. To address these challenges, this paper proposes a method for inferring the residual stress field in die-forged blanks based on multi-source information fusion. The proposed method is facilitated by fusing local residual stress measurement data near surface layer, simulation residual stress data in bulk component, as well as in-situ deformation force monitoring data dues to residual stress relaxation during machining process. Precise inference of 3D residual stress is realized by introducing the constraints of residual stress solution with in-situ deformation force as well as the relationship among relative variables, which are cooperated in the proposed neural network structure. This method has been validated on 7075 aluminum alloy die-forged blank, in the simulation environment, the mean absolute error of all test samples is as follows: X-direction: 13.89&#xa0;MPa; Y-direction: 7.37&#xa0;MPa; Z-direction: 17.11&#xa0;MPa, and with mean absolute error of 0.09&#xa0;mm using predicting deformation as evaluation index under the actual machining conditions.</p>

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A residual stress field inference method of die-forged blanks based on multi-source information fusion

  • 期刊论文

  • 发表日期：

    2025年11月23日

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  • Dehua Li,
  • Yingguang Li,
  • Changqing Liu,
  • JI Vincent

摘要

The residual stress within material is one of the main reasons for machining deformation, and it is more severe for die-forged blanks. A challenge is posed for residual stress field inference by the extremely complex distribution of the residual stress field in die-forged blanks. To address these challenges, this paper proposes a method for inferring the residual stress field in die-forged blanks based on multi-source information fusion. The proposed method is facilitated by fusing local residual stress measurement data near surface layer, simulation residual stress data in bulk component, as well as in-situ deformation force monitoring data dues to residual stress relaxation during machining process. Precise inference of 3D residual stress is realized by introducing the constraints of residual stress solution with in-situ deformation force as well as the relationship among relative variables, which are cooperated in the proposed neural network structure. This method has been validated on 7075 aluminum alloy die-forged blank, in the simulation environment, the mean absolute error of all test samples is as follows: X-direction: 13.89 MPa; Y-direction: 7.37 MPa; Z-direction: 17.11 MPa, and with mean absolute error of 0.09 mm using predicting deformation as evaluation index under the actual machining conditions.