Multi-Axis Tool Head Collision Forecasting for 3D Machining
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Solution Overview
Problem
Existing methods for collision avoidance during workpiece processing, such as manual checking and brute force algorithms, are complex and require long computation times.
Innovation Solution
A method using a device with a machine learning algorithm to process input data from a workpiece and tool head, determining collision risks through a neural network, and adjusting axis movements to avoid collisions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual checking or brute force algorithms are used for collision avoidance, then collision detection capability is achieved, but computation time becomes very long and system complexity increases
Solution Approach 1:
The patent pre-calculates and stores collision-free processing paths in a database before actual workpiece processing. The machine learning model is trained in advance with simulated processing data to learn collision patterns, enabling rapid real-time collision avoidance decisions without complex runtime computations
Solution Approach 2:
The patent replaces traditional brute force collision detection algorithms with a machine learning-based predictive system. The neural network model predicts potential collisions by learning from historical processing data, substituting computational mechanics with intelligent prediction that requires minimal real-time computation
2Reliability
If manual checking or complex algorithms are used for collision avoidance, then collision detection is possible, but device complexity increases
Solution Approach 1:
The system uses its own historical processing data and simulated collision scenarios to train its machine learning model. The machine learning model autonomously learns collision patterns from the data it processes, eliminating the need for external complex collision detection systems or manual programming of collision avoidance logic
Data Source
AI summary
A method processes a workpiece using a device. The device has a processing machine configured for three-dimensionally processing the workpiece, and a tool head having a tool. The tool head is movable about at least a first axis and a second axis. The method is carried out by the device. The method includes: capturing input data concerning a contour of the workpiece, a contour of the tool head, a distance between the tool head and the workpiece, the first axis, and the second axis; processing the input data to form feature data; processing the feature data in a machine learning algorithm of the device; and outputting a forecast from the machine learning algorithm regarding a collision of the tool head with the workpiece or some other part of the processing machine.