Adaptive Component Sampling for Faster Quality Assessment
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Solution Overview
Problem
Current quality assessment methods for components require significant time, reducing production yield, and there is a need for a method to reliably assess quality while minimizing this time, especially under changing production conditions.
Innovation Solution
A method and system that dynamically adapt the selection of components for quality assessment based on production and measurement parameters, using closed-loop control to adjust sampling frequency and measurement strategies, allowing for quick and reliable quality assurance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality assessment methods are used to ensure reliable quality control, then quality assurance is maintained, but the time required for quality assessment increases significantly
Solution Approach 1:
The patent implements dynamic adaptation of the measurement strategy by continuously monitoring production parameters and automatically adjusting the sampling frequency and measurement depth. The system transitions from static, fixed measurement plans to dynamic, adaptive measurement strategies that respond to real-time production conditions, thereby reducing assessment time while maintaining reliability.
Solution Approach 2:
The system employs closed-loop feedback by monitoring production parameters (such as machine settings, material batches, environmental conditions) and using this information to adaptively adjust the quality assessment strategy. The feedback mechanism allows the system to identify when reduced measurement intensity is acceptable, thereby reducing time requirements while preserving quality assurance reliability.
2Reliability
If sampling frequency is increased to improve quality assessment reliability under changing production conditions, then quality control reliability is maintained, but production yield is reduced
Solution Approach 1:
The system dynamically adjusts sampling frequency based on production parameter stability. When production conditions are stable, the system reduces sampling frequency to maximize productivity. When changes are detected in production parameters, the system automatically increases sampling frequency to maintain quality control reliability, thus resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent changes the parameter of sampling frequency adaptively based on production conditions. Instead of using a fixed high sampling frequency that reduces productivity, the system varies this parameter in response to production parameter changes, maintaining reliability only when necessary and maximizing productivity when conditions are stable.
3Reliability
If comprehensive measurement of all components is performed to ensure quality assurance, then quality reliability is improved, but the time required for measurement increases
Solution Approach 1:
The system applies partial measurement action by selectively measuring only a subset of components based on production parameter analysis. Instead of comprehensively measuring all components, the system identifies and measures only those components that are necessary for quality assurance under current production conditions, thereby reducing measurement time while maintaining reliability.
Solution Approach 2:
The patent segments the measurement process into different levels of intensity based on production conditions. The measurement strategy is divided into multiple segments (full measurement, reduced measurement, spot checking) that are selected adaptively, allowing the system to achieve quality assurance with minimal measurement time by using the appropriate segment for each production context.
Data Source
AI summary
A method for measuring components produced by a production device includes selecting components to be measured from multiple components. The selection is made according to at least one selection parameter. The at least one selection parameter includes a sampling frequency. The method includes determining at least one production parameter. The at least one production parameter includes a production condition. The method includes adapting the sampling frequency based on the production parameter or a change in the production parameter. Adapting includes reducing the sampling frequency in response to one or more production parameters not changing by more than a predetermined amount.