CNC Trajectory Detection via Univariate Function Conversion
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Solution Overview
Problem
Existing machine processing trajectory detection methods require complex spatial geometry analysis, leading to increased algorithm complexity and potential misses in determining trajectory positions relative to specified safe or prohibited areas.
Innovation Solution
The method converts trajectory data into a univariate function to determine point sets on the inner side of curved surfaces within a specified space area, using inequalities to calculate positional relationships between the trajectory and the space area, thereby simplifying the analysis and improving flexibility and expansibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spatial geometry algorithm is used to determine trajectory position, then trajectory detection can be performed, but algorithm complexity increases exponentially
Solution Approach 1:
The patent segments the trajectory detection process into distinct phases: trajectory sampling to obtain discrete points, classification of points based on their position relative to the specified area, and determination of overall trajectory position. This segmentation breaks down the complex geometric analysis into manageable steps, reducing algorithmic complexity while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that categorizes trajectory points into different sets (first set for points inside the specified area, second set for points outside). This intermediary classification layer simplifies the overall determination process by avoiding direct complex geometric calculations between the entire trajectory and the specified area boundary.
2Measurement precision
If complex spatial geometry analysis is performed, then trajectory position can be determined, but computation time increases
Solution Approach 1:
The trajectory is divided into discrete sampled points that can be independently classified. This segmentation allows for parallel processing of multiple points and avoids the need for continuous complex geometric analysis, significantly reducing computation time while maintaining precision through sufficient sampling density.
Solution Approach 2:
The patent employs partial action by sampling only critical points along the trajectory rather than analyzing every point. By selecting representative samples and classifying them, the system achieves sufficient accuracy without the computational burden of exhaustive analysis of the entire trajectory.
3Reliability
If individual presumption conditions are made for different positional relationships, then trajectory detection can be performed, but the algorithm becomes larger and less flexible
Solution Approach 1:
The patent implements a universal classification framework that can handle various positional relationships between trajectory and specified area through a single unified process. The classification mechanism works consistently regardless of the specific geometric configuration, making the algorithm adaptable to different scenarios without requiring separate presumption conditions for each case.
Solution Approach 2:
The patent changes the approach from geometric parameter analysis to set-based classification. By transforming the problem from continuous geometric relationship analysis to discrete set membership determination, the algorithm becomes more flexible and adaptable to different trajectory and area configurations without increasing complexity.
Data Source
AI summary
The present disclosure provides a method, devices, a numerical control machine, and a computer storage medium for detecting machine processing trajectory space. The method includes: obtaining data of a trajectory AB of a machine processing and spatial data of a specified space area; converting a function of the data of the trajectory AB into a univariate function P=f (u) with respect to a trajectory parameter u; determining a point set Ui of the trajectory AB on an inner side of each curved surface Si based on spatial data of the individual curved surfaces Si forming the specified space area and the function P=f(u); and determining a positional relationship between the trajectory AB and the specified space area based on the point sets Ui.