Adaptive Machining Parameters for Tolerance Stack-Up Control
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Solution Overview
Problem
Manufactured parts often face challenges in meeting multiple tolerance ranges and achieving desired part qualities due to tolerance stack-up and fixed machining parameters, leading to inefficiencies and unacceptable parts.
Innovation Solution
An adaptive method and system that modifies tolerances and machining parameters based on predictive analytics using a training database formed from initial machining operations, allowing for real-time adjustments in subsequent machining steps to improve part quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fixed machining parameters and standard tolerances are used, then manufacturing process is simple and consistent, but part quality varies and tolerance stack-up causes unacceptable parts
Solution Approach 1:
The patent implements dynamic adjustment of machining parameters and tolerances based on real-time predictions of part quality. The system transitions from fixed, static parameters to dynamic, adaptive parameters that change based on predicted outcomes, allowing the machining process to optimize itself for each part while maintaining overall system simplicity through automation.
Solution Approach 2:
The system uses machine learning models to predict part quality based on machining parameters, then feeds this prediction back to adjust parameters for subsequent machining steps. This closed-loop feedback mechanism enables continuous optimization of part quality without requiring complex manual intervention, resolving the contradiction between precision improvement and system complexity.
2Manufacturing precision
If tight tolerances are applied to meet all tolerance ranges, then part quality improves, but manufacturing complexity and time increase
Solution Approach 1:
The system dynamically changes tolerance parameters and machining parameters based on predicted part quality. Instead of applying uniformly tight tolerances to all features, the system adjusts parameters adaptively - applying tighter tolerances only where needed and relaxed tolerances where acceptable - thereby reducing total machining time while maintaining compliance with all required tolerance ranges.
Solution Approach 2:
The patent applies the principle of partial action by selectively tightening tolerances and machining parameters only for specific features or steps where quality predictions indicate it is necessary, rather than applying excessive tight controls universally. This selective approach reduces overall machining time while still achieving the required level of precision for critical features.
3Productivity
If uniform machining parameters are used for all parts, then process consistency is maintained, but productivity decreases due to inability to adapt to varying part qualities
Solution Approach 1:
The machining system performs self-service by automatically predicting part quality and adjusting its own parameters without external intervention. The machine learning model enables the system to adapt to varying part qualities autonomously, selecting optimal parameters for each specific part based on predictions, thereby simultaneously improving productivity and adaptability.
Solution Approach 2:
The system dynamically changes machining parameters based on predicted part quality for each individual part or feature. This parameter adaptation capability allows the system to optimize productivity by using less conservative parameters when quality predictions are favorable, while maintaining quality control when predictions indicate potential issues, thus resolving the contradiction between productivity and adaptability.
4Measurement precision
If multiple tolerance checks are performed, then acceptance accuracy improves, but inspection complexity and time increase
Solution Approach 1:
The system performs preliminary action by predicting part quality before the machining step is completed or before final inspection. The machine learning model forecasts whether a part will meet tolerance requirements based on current machining parameters and historical data, allowing quality assessment to occur earlier in the process and reducing the need for extensive post-processing inspection, thereby improving measurement precision while reducing inspection time.
Data Source
AI summary
A method of performing machining steps includes the steps of 1) performing an initial machining on a plurality of initial parts utilizing at least one machine and storing machining parameters for each of the initial parts, 2) capturing features of the initial parts subsequent to the initial machining, 3) associating the captured features of the initial parts and the stored machining parameters for each of the initial parts, and utilizing the association to form a training database, 4) predicting a part quality for production parts by utilizing a machining parameter of a production machining operation and 5) modifying machining parameters of a subsequent machining production step based upon the predicted part quality. A system is also disclosed.