Adaptive Forged Part Machining for Residual Stress Distortion
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Solution Overview
Problem
Manufactured components formed by forging experience variable residual stress due to variations in heat treatment and cooling, leading to part distortion, time delays, and increased manufacturing costs, with current mitigation methods relying on trial and error adjustments.
Innovation Solution
An adaptive machining strategy using machine learning to predict residual stress and distortion by associating measured geometry with heating and cooling parameters, employing a machine learning module to develop an efficient machining strategy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional machining methods are used without adaptation, then the machining process is simple and fast, but part distortion occurs due to variable residual stress
Solution Approach 1:
The system performs preliminary measurements of the intermediate part's geometry after forging and before finish machining. These measurements are used to predict residual stress distribution and determine adaptive machining parameters in advance, allowing the finish machining strategy to be optimized beforehand to compensate for expected distortion
Solution Approach 2:
The system implements a feedback loop where measurements from the intermediate part are continuously fed into a machine learning model that predicts residual stress. The predicted stress information feeds back into the machining control system, which automatically adjusts machining parameters and toolpaths to compensate for distortion, creating a closed-loop adaptive control system
2Manufacturing precision
If trial and error adjustments are made to mitigate distortion, then some distortion reduction is achieved, but time delays and manufacturing costs increase
Solution Approach 1:
The system replaces manual trial-and-error adjustment methods with an automated machine learning-based prediction and control system. The machine learning model automatically predicts residual stress and distortion based on measured data, and the control system automatically adjusts machining parameters, eliminating the need for time-consuming manual iterations and expert intervention
Solution Approach 2:
The system dynamically changes machining parameters such as cutting speeds, feed rates, toolpaths, and depth of cuts based on predicted residual stress values. These parameter adjustments are automatically determined by the machine learning model to optimize distortion compensation while maintaining efficient machining rates
3Manufacturing precision
If adaptive machining with machine learning is implemented, then part distortion is reduced by over 50%, but the system complexity and initial setup increase
Solution Approach 1:
The system introduces measurement devices and a machine learning model as intermediaries between the forging process and the machining process. These intermediaries capture data from the forging process, predict residual stress, and translate this information into optimized machining parameters, bridging the gap between variable forging outcomes and consistent machining quality
Data Source
AI summary
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.