Machining Surface Simulation Using Critical Servo Feedback Points
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Solution Overview
Problem
Conventional machining simulations fail to accurately predict surface quality due to neglecting vibrations and jerks during acceleration and deceleration, leading to increased calculation time and memory usage, especially when high sampling rates are required for feedback data from servo motors.
Innovation Solution
A simulation apparatus with a data selector unit that reduces data usage by selecting only critical points impacting the machined surface quality, based on predetermined tolerance lengths, angles, and error changes, allowing for efficient simulation processing and graphic display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high sampling rate is used to accurately capture vibrations and jerks during machining, then the accuracy of surface quality prediction is improved, but the calculation time and memory usage increase significantly
Solution Approach 1:
The patent extracts only the critical vibration and jerk data points that significantly impact surface quality, rather than processing all high-frequency feedback data. The data selection unit identifies and extracts key characteristics from the servo motor feedback data, reducing the data volume while preserving the essential information needed for accurate surface quality prediction.
Solution Approach 2:
The patent segments the continuous feedback data into discrete, meaningful intervals based on vibration and jerk characteristics. By dividing the data stream into relevant segments and selecting representative points, the system reduces computational load while maintaining prediction accuracy.
2Manufacturing precision
If all feedback data from servo motors is used in the simulation, then the accuracy of machined surface evaluation is improved, but the memory usage and data processing burden increase
Solution Approach 1:
The data selection unit extracts only the essential vibration and jerk characteristics from the complete feedback data set. By identifying and extracting key parameters such as maximum jerk values and vibration amplitudes at critical points, the system reduces data volume while preserving the information necessary for accurate machined surface evaluation.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific critical points in the machining process where vibrations and jerks have the greatest impact on surface quality. Rather than uniformly processing all data points, the system identifies and prioritizes local regions of high importance.
3Productivity
If conventional machining simulation is performed without considering vibrations and jerks, then the calculation time and memory usage are reduced, but the surface quality prediction becomes inaccurate
Solution Approach 1:
The patent applies partial action by incorporating only the essential vibration and jerk considerations needed for accurate surface quality prediction, rather than performing a complete dynamic analysis. The data selection unit identifies the minimum necessary data points and characteristics required to achieve accurate predictions without the computational burden of full dynamic simulation.
Data Source
AI summary
A simulation apparatus stores series data of positions of points (point sequence data) regarding feedback data from a motor which drives an axis of a machine tool, selects, from this stored point sequence data, a point which has a great impact on finished quality of a machined surface as a point for use in a simulation process, and performs the simulation process based on data regarding the selected point.