Measurement System Evaluation Using Pre-Analysis Variation Isolation
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Solution Overview
Problem
Industrial products face variations during manufacturing, affecting their consistency and reproducibility, which can be challenging to address using existing measurement systems and analysis methods.
Innovation Solution
A computer-program product is provided that generates computer-generated likelihoods for candidate evaluations of industrial products by analyzing measurement system factors such as operators, tools, and products, using a metric set to isolate sources of variation before a measurement system analysis, allowing for resource optimization and improved measurement system design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional measurement system analysis is conducted without preliminary evaluation, then comprehensive measurement data can be collected, but resource consumption and testing time increase significantly
Solution Approach 1:
The system performs preliminary evaluation of measurement system factors (equipment, operators, environment) before conducting the full measurement system analysis. This preliminary action identifies potential sources of variation and allows for optimized test design, reducing the time and resources needed for the complete analysis while maintaining measurement precision.
Solution Approach 2:
The system allows users to perform partial evaluations by selecting specific subsets of measurement system factors to evaluate. This enables focused analysis on critical factors without requiring evaluation of all possible factors, thereby reducing testing time and resource consumption while still achieving sufficient measurement analysis accuracy for decision-making.
2Reliability
If comprehensive measurement tests are conducted on all factors, then complete understanding of variation sources is achieved, but resource allocation becomes inefficient
Solution Approach 1:
The system conducts preliminary assessments of measurement system factors to identify which factors are most likely to contribute to variation. This preliminary action enables efficient resource allocation by focusing comprehensive testing only on the identified critical factors rather than all factors equally, thus maintaining evaluation completeness while improving productivity.
Solution Approach 2:
The system applies different evaluation depths to different measurement system factors based on their identified importance. Critical factors receive comprehensive evaluation while less important factors receive preliminary or reduced evaluation. This local quality approach ensures reliable assessment of variation sources while optimizing resource allocation efficiency.
3Productivity
If measurement system analysis is performed without isolating variation sources, then analysis can be completed quickly, but measurement precision and accuracy decrease
Solution Approach 1:
The system segments the measurement system analysis into distinct components: preliminary evaluation of individual factors, identification of variation sources, and focused analysis on those sources. This segmentation allows for efficient processing of each component separately while maintaining the precision needed to accurately identify variation sources, resolving the contradiction between analysis speed and measurement precision.
Data Source
AI summary
A computing system receives a request for computer-generated likelihood(s) for candidate evaluations of an industrial product set according to a measurement system analysis (MSA). The MSA comprises tests for evaluating, according to a measurement standard, the industrial product set. The request indicates a metric set representing metric(s) each quantifying an estimate of contribution to variation in evaluating the industrial product set according to the MSA. The system generates a design comprising a respective input set for each test of the MSA. The respective input set comprises setting(s) for conducting a test of the MSA. The design is designed to isolate candidate sources for contributing to the variation in evaluating the industrial product set according to the MSA. The system (e.g., prior to the MSA) outputs, based on the metric set and the design, the computer-generated likelihood(s) for the candidate evaluations of the industrial product set according to the MSA.