Coordinate Measuring Strategy Adaptation for Faster High-Quality Inspection
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Solution Overview
Problem
Current methods for measuring objects using coordinate measuring machines result in high measurement times, increased computational load, and storage requirements, which are costly and inefficient, especially when achieving high measurement quality.
Innovation Solution
A method and apparatus that dynamically adjust the measurement strategy by altering parameters such as measurement points, speed, and data processing to reduce measurement time, computational load, and storage capacity while maintaining or exceeding the required measurement quality, allowing for real-time adaptation and optimization of the measurement process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of measurement points are captured to achieve high measurement quality, then measurement quality is improved, but measurement time increases
Solution Approach 1:
The system dynamically adjusts measurement parameters (number of measurement points, scanning speed, sensor type) based on the actual quality characteristics of the measurement object. By changing these parameters adaptively, the system achieves high measurement quality for critical features while reducing measurement time for less critical areas, thus resolving the contradiction between measurement quality and measurement time.
Solution Approach 2:
The measurement strategy is differentiated by applying different measurement densities and qualities to different regions or features of the measurement object. Critical features receive high-density measurement points for high quality, while non-critical areas use lower measurement densities, thereby achieving overall high measurement quality without proportionally increasing total measurement time.
2Measurement precision
If a large number of measurement points are captured to achieve high measurement quality, then measurement quality is improved, but computational outlay increases
Solution Approach 1:
The system adapts computational parameters such as evaluation algorithms, filtering methods, and data processing intensity based on the measurement object's characteristics and quality requirements. By adjusting these parameters dynamically, the system reduces unnecessary computational processing while maintaining measurement quality, thus resolving the contradiction between measurement quality and computational outlay.
Solution Approach 2:
The system extracts and processes only the essential measurement data required for quality assessment, filtering out redundant information. By focusing computational resources on critical measurement points and features rather than processing all measurement points uniformly, the system achieves high measurement quality with reduced computational outlay.
3Measurement precision
If a large number of measurement points are captured to achieve high measurement quality, then measurement quality is improved, but data storage capacity increases
Solution Approach 1:
The system dynamically adjusts data storage parameters including measurement point density, data precision, and storage format based on the measurement object's quality requirements. By changing these parameters adaptively, the system stores sufficient data to ensure measurement quality while minimizing unnecessary data storage, thus resolving the contradiction between measurement quality and data storage capacity.
Solution Approach 2:
The system extracts and stores only the essential measurement data required for quality assessment, eliminating redundant data. By filtering and storing only critical measurement points and features rather than all measurement data uniformly, the system achieves high measurement quality with reduced data storage requirements.
4Loss of time
If the relative speed between measurement object and sensor is increased to reduce measurement time, then measurement time is reduced, but measurement quality deteriorates
Solution Approach 1:
The system dynamically adjusts the scanning speed parameter based on the measurement object's characteristics, feature criticality, and real-time measurement quality feedback. By changing speed adaptively - slower for critical features requiring high quality, faster for non-critical areas - the system reduces overall measurement time while maintaining measurement quality where required.
Solution Approach 2:
Different scanning speeds are applied to different regions or features of the measurement object. Critical features are measured at lower speeds for high quality, while non-critical areas are measured at higher speeds, thereby achieving high measurement quality for essential features without proportionally increasing total measurement time.
Data Source
AI summary
A method for determining an altered measurement strategy for measurement of a measurement object using a coordinate measuring machine includes measuring the measurement object according to an initial measurement strategy. The method includes determining a measurement quality of the measurement. The method includes, in response to the measurement quality being greater than a predetermined target minimum measurement quality, altering the initial measurement strategy to produce the altered measurement strategy. The altering is performed such that at least one of time required to measure the measurement object in accordance with the altered measurement strategy is reduced, computational outlay required to measure the measurement object in accordance with the altered measurement strategy is reduced, and data storage capacity required to measure the measurement object in accordance with the altered measurement strategy is reduced.