CMM Position Estimation via Computational Model Feedback
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Solution Overview
Problem
Coordinate measuring machines (CMMs) face challenges in accurately determining the position of movable parts due to mechanical vibrations, as existing methods like acceleration sensors often produce inaccurate results over time, with phase and amplitude deviations from actual vibrations.
Innovation Solution
A method involving a computational model that measures position and acceleration values, using feedback of position and acceleration deviations to output updated estimates, which accounts for vibrations and corrects for the distance between measurement and estimation locations, allowing for precise position determination of movable parts in CMMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acceleration sensors are used to determine position under mechanical vibrations, then position information can be obtained, but measurement precision deteriorates over time due to phase and amplitude deviations
Solution Approach 1:
The patent implements feedback by continuously comparing the actual position measured by the position measuring system with the estimated position from the computational model, and using this position deviation to correct the model's estimates. This closed-loop feedback mechanism maintains measurement accuracy over time by compensating for vibration-induced phase and amplitude deviations that would otherwise accumulate and degrade reliability.
Solution Approach 2:
The patent replaces direct mechanical vibration measurement approaches with a computational model that uses feedback control. Instead of relying solely on acceleration sensors that drift over time, the system substitutes a mathematical model that continuously adapts to actual vibration conditions through feedback, thereby maintaining precision without the reliability issues of pure mechanical sensing.
2Ease of operation
If position measuring system measures at one location but estimation is needed at another location on the movable part, then measurement can be performed, but measurement precision deteriorates due to distance between measurement and estimation locations
Solution Approach 1:
The patent introduces a computational model as an intermediary between the position measuring system and the desired position information. This model takes the measured position data and the measured vibration acceleration as inputs, then computes the position at the desired location by accounting for the dynamic effects of vibration and the spatial relationship between measurement and estimation points, thereby maintaining precision despite the distance separation.
Solution Approach 2:
Instead of using multiple physical sensors at different locations, the patent substitutes a computational approach that mathematically bridges the gap between measurement and estimation locations. The computational model uses the measured position and vibration data to infer positions at other locations on the movable part, eliminating the need for additional mechanical measurement infrastructure while maintaining precision.
3Measurement precision
If computational model uses feedback of position and acceleration deviations, then measurement precision improves, but device complexity increases
Solution Approach 1:
The computational model is self-adjusting through feedback from position and acceleration deviations. It automatically adapts its estimates without requiring external calibration or manual intervention, using the measured deviations to continuously refine its internal state and improve position determination precision while maintaining manageable complexity through autonomous operation.
Data Source
AI summary
A coordinate measuring machine comprising a movable part, a drive and drive controller, and a position measuring system for measuring position values of the movable part. Intended values produced by the drive controller are supplied to a computational model (MOD) of a computational device. The intended values respectively predetermine an intended state of the drive. Position deviations (Δs) are formed between the measured position values (s) and position estimates of the computational model (MOD). The position estimates are produced by the computational model (MOD) for a predetermined location at the movable part. Acceleration values are measured by an acceleration sensor arranged at a location at the movable part. Acceleration deviations (Δa) are formed between the measured acceleration values (a) and acceleration estimates of the computational model (MOD). During the operation of the coordinate measuring machine, the position deviations (Δs) and the acceleration deviations (Δa) are supplied to the computational model (MOD), which outputs updated position estimates (ŝ) and updated acceleration estimates (â), accordingly. The position of the movable part is determined by the updated position estimates (ŝ).