NC Drilling Position Learning for Faster Hole Alignment
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Solution Overview
Problem
Numerically controlled machining tools face inefficiencies due to variances between theoretical and actual positions on workpieces, leading to non-optimized movements and reduced productivity in automated drilling and countersinking processes.
Innovation Solution
The implementation of a machine learning-based optimization method that calculates 'learned positions' by analyzing historical data to adjust theoretical positions, reducing the need for machine tool repositioning and improving alignment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rototranslation is used to adapt theoretical map to actual dimensions, then position variances are compensated, but machine tool movements become non-optimized and productivity decreases
Solution Approach 1:
The system performs preliminary learning of actual positions during initial machining operations, storing this information for future use. This preliminary action enables the system to predict and compensate for position variances before they affect productivity, allowing optimized movements from the start of production
Solution Approach 2:
The system implements feedback by continuously comparing theoretical positions with actual measured positions, using machine learning algorithms to learn from discrepancies. This feedback loop enables the system to adapt and optimize tool paths dynamically, resolving the contradiction between precision compensation and movement efficiency
2Ease of manufacture
If theoretical positions are used for drilling, then programming is simplified, but variances between theoretical and actual positions reduce alignment accuracy
Solution Approach 1:
The system creates a learned position map that copies and adapts the theoretical map based on actual measured positions. This learned map maintains the simplicity of programmed instructions while incorporating real-world position data, allowing the system to use simplified programming while achieving high alignment accuracy through the learned position corrections
Data Source
AI summary
A method for optimizing the execution of automated drilling systems controlled by a numerical control, NC, machine, wherein the NC machine performs the following steps: (i) identifying each drill executed by an automated drilling tool at first drilling event (t); storing a theoretical position of each executed drill at the event (t); (iii) calculating a learned position using a machine learning model and based on the stored theoretical position at the event (t); (iv) estimating an intermediate position by applying a tendency statistical function, and (v), if the difference between the intermediate position and the learned position is less than a pre-configured threshold, using the intermediate position for the drilling tool to position a next drill and execute it at a subsequent event (t+1).