Adaptive machining to reduce part distortion after forging

Patent metadata

Field	Value
Publication number	US-2025315018-A1
Application number	US-202418627109-A
Country	US
Kind code	A1
Filing date	Apr 4, 2024
Priority date	Apr 4, 2024
Publication date	Oct 9, 2025
Grant date	—

Abstract

A method of adaptive machining of a forged part includes the steps of 1) forming a rough part and subjecting the rough part to heat treatment, 2) cooling the rough part, 3) performing rough machining on the rough part, 4) measuring a geometry of the rough part after the rough machining, and associating the measured geometry with heating and cooling parameters from steps 1) and 2), and providing the measured geometry to a machine learning module, 5) providing the machine learning module with a training set that associates the measured geometry with a predicted reaction to finish machining and 6) adapting a finish machining strategy based upon the prediction. A system is also disclosed.

First claim

What is claimed is:

1 . A method of adaptive machining of a forged part comprising the steps of:
1) forming a rough part and subjecting the rough part to heat treatment; 2) cooling the rough part; 3) performing rough machining on the rough part; 4) measuring a geometry of the rough part after the rough machining, and associating the measured geometry with heating and cooling parameters from steps 1) and 2), and providing the measured geometry to a machine learning module; 5) providing the machine learning module with a training set that associates the measured geometry with a predicted reaction to finish machining; and 6) adapting a finish machining strategy based upon the prediction.

2 . The method as set forth in claim 1 , wherein the machine learning module considers temperatures on the rough part during the heat treatment of step 1).

3 . The method as set forth in claim 2 , wherein the machine learning module considers a cooling rate of the rough part during step 2).

4 . The method as set forth in claim 3 , wherein the finished part is an aerospace part.

5 . The method as set forth in claim 4 , wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.

6 . The method as set forth in claim 1 , wherein the machine learning module considers a cooling rate of the rough part during step 2).

7 . The method as set forth in claim 6 , wherein the finished part is an aerospace part.

8 . The method as set forth in claim 7 , wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.

9 . The method as set forth in claim 1 , wherein the finished part is an aerospace part.

10 . The method as set forth in claim 9 , wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.

11 . A system for machining a part after a forging operation comprising:
at least one machine for providing rough machining and subsequent machining; and a control for the at least one machine, the control having a machine learning module and processing circuitry operable to associate heat treatment information from a heat treating system and cooling information from a cooling system, with measured information from rough machining to predict a residual stress and operable to develop and implement a finished machining strategy for the at least one machine based upon the prediction.

12 . The system as set forth in claim 11 , wherein the machine learning module is operable to predict the residual stress based on temperatures on the rough part during the heat treatment.

13 . The system as set forth in claim 12 , wherein the machine learning module is operable to predict the residual stress based on a cooling rate of the rough part.

14 . The system as set forth in claim 13 , wherein the finished part is an aerospace part.

15 . The system as set forth in claim 14 , wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.

16 . The system as set forth in claim 11 , wherein the machine learning module is operable to predict the residual stress based on a cooling rate of the rough part.

17 . The system as set forth in claim 16 , wherein the finished part is an aerospace part.

18 . The system as set forth in claim 17 , wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.

19 . The system as set forth in claim 11 , wherein the finished part is an aerospace part.

20 . The system as set forth in claim 19 , wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.

Assignees

• Rtx Corp

Inventors

• Guo Changsheng
• Zhang Huan
• Kountanya Raja K
• Venkatesh Vasisht

Classifications

• G05B13/027

  using neural networks only · CPC title

• G06N20/00

  Machine learning · CPC title

• G05B2219/32194

  Quality prediction · CPC title

• G05B19/41875

  characterised by quality surveillance of production · CPC title

• G05B13/041 Primary

  in which a variable is automatically adjusted to optimise the performance · CPC title