Machine Tool Friction Compensation via Surrogate Model Calibration
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Solution Overview
Problem
Existing machine tools face challenges in reducing friction between mechanical components, leading to deviations in tool positioning and affecting the tolerance of produced parts. Current friction compensation controllers require manual tuning by expert technicians, causing production interruptions and inconsistent quality.
Innovation Solution
A computer-implemented method using surrogate models to automatically determine an optimized friction compensation parameter set for machine tools. The method reads multiple surrogate models, assigns weighting factors based on goodness-of-fit, and iteratively adjusts these factors to achieve optimal friction compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning by expert technicians is used to optimize friction compensation parameters, then the quality and consistency of friction compensation can be improved, but production downtime increases and productivity decreases
Solution Approach 1:
The system performs self-calibration by automatically determining friction compensation parameters through iterative optimization algorithms that evaluate multiple surrogate models and select the best fit without requiring external expert intervention. The controller independently adjusts parameters based on measured position deviations and friction compensation results.
Solution Approach 2:
The patent replaces the manual mechanical tuning process with an automated computational system that uses surrogate models, iterative optimization, and automated parameter determination to substitute human expert intervention with algorithmic decision-making.
2Measurement precision
If manual tuning by expert technicians is used to set friction compensation parameters, then the accuracy of parameter calibration can be improved, but inconsistency in quality among different technicians persists
Solution Approach 1:
The system systematically varies and optimizes friction compensation parameters through automated iterative processes, evaluating multiple parameter sets against measured position deviations to determine the optimal configuration objectively rather than relying on subjective expert judgment.
Solution Approach 2:
The system implements a feedback mechanism where position deviations are measured, friction compensation is applied, results are evaluated, and parameters are adjusted iteratively based on the measured outcomes, ensuring consistent and reproducible calibration results.
3Manufacturing precision
If friction compensation parameters are recalibrated over the machine lifetime, then the accuracy of friction compensation can be maintained, but machine downtime increases
Solution Approach 1:
The system performs comprehensive friction compensation parameter optimization in advance during initial setup or maintenance periods, storing the determined parameters for long-term use to avoid repeated recalibration and minimize operational downtime.
Solution Approach 2:
The system maintains continuous friction compensation accuracy by implementing automated monitoring and adjustment capabilities that can operate during machine operation, eliminating the need for frequent stopping and recalibration.
4Manufacturing precision
If multiple surrogate models are used to determine friction compensation parameters, then the accuracy and robustness of parameter determination can be improved, but computational complexity increases
Solution Approach 1:
The system uses multiple surrogate models to evaluate friction compensation parameters, but applies weighted averaging where models with better goodness-of-fit receive higher weights, achieving high accuracy without requiring all models to contribute equally, thus managing computational complexity effectively.
Solution Approach 2:
The system combines multiple surrogate models into a composite evaluation framework, integrating their predictions through weighted averaging to leverage the strengths of different models and achieve more robust parameter determination than any single model could provide alone.
Data Source
AI summary
A computer-implemented method for reducing friction within a machine tool is provided, including: a) reading a plurality of surrogate models for approximating friction compensation within a given machine tool, b) reading a friction compensation parameter set, c) determining a friction compensation result value for each surrogate model using the compensation parameter set, d) determining a weighted average friction compensation value of the friction compensation result values using the respective weighting factor, e) deducing a quality indicator for the friction compensation parameter set based on the weighted average friction compensation value, f) outputting the friction compensation parameter set, if the quality indicator fulfils a given quality criterion, or repeating b) to e) until the quality indicator fulfills the given quality criterion, g) applying the outputted friction compensation parameter set to the machine tool for reducing friction within the machine tool.