Adaptive Machining Tolerances for Predicted Part Quality
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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 modeling using a training database formed from initial machining operations, allowing for real-time adjustments 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 stable, but part quality varies and tolerance stack-up occurs leading to unacceptable parts
Solution Approach 1:
The patent implements dynamic adjustment of machining parameters and tolerances based on real-time predictions. The system continuously modifies cutting speeds, feed rates, and tolerances for subsequent machining operations based on predicted part quality from previous operations, transforming static fixed parameters into dynamic adaptive parameters that respond to actual manufacturing conditions.
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where part quality predictions from previous machining operations are fed back to adjust parameters for subsequent operations. The predictive model analyzes outcomes from prior operations and uses this information to proactively modify machining parameters and tolerances before defects occur, creating a self-correcting manufacturing process.
2Manufacturing precision
If tight tolerances are applied to all machining operations, then part quality improves, but manufacturing time and cost increase due to repeated adjustments and rework
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 operations, the system adjusts tolerances and parameters in real-time according to actual manufacturing conditions and predictions, allowing looser tolerances when quality is predicted to be high and tighter control when defects are anticipated.
Solution Approach 2:
The system applies selective precision control rather than universal tight tolerances. By using predictive modeling to identify which specific operations require tight control and which can tolerate broader parameters, the system applies precision only where necessary, avoiding the productivity loss associated with uniformly tight tolerances across all machining operations.
3Manufacturing precision
If machining parameters are adjusted frequently to maintain quality, then part quality improves, but process stability decreases and complexity increases
Solution Approach 1:
The system performs preliminary adjustments of machining parameters and tolerances based on predictions from previous operations. By proactively modifying parameters before quality degradation occurs rather than reacting to defects after they happen, the system maintains process stability while preventing quality issues, reducing the need for frequent corrective adjustments.
Data Source
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AI summary
A method of performing machining steps includes the steps of 1) performing (100) 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 (102) features of the initial parts subsequent to the initial machining, 3) associating (104) the captured features of the initial parts and the stored machining parameters for each of the initial parts, and utilizing (106) the association to form a training database, 4) predicting (108) a part quality for production parts by utilizing a machining parameter of a production machining operation and 5) modifying (110) machining parameters of a subsequent machining production step based upon the predicted part quality. A system is also disclosed.