Adaptive Workpiece Inspection Using Statistical Control Rules
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Solution Overview
Problem
Current quality control methods for workpieces in industrial production require significant user intervention and knowledge, leading to inefficiencies and susceptibility to errors, as well as an inability to automatically adjust testing scope based on production changes or special features.
Innovation Solution
A method that dynamically identifies unstable and stable test features using statistical control rules, measuring unstable features more frequently and adjusting the testing scope accordingly, allowing for adaptive quality control without requiring constant user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection criteria are defined by users, then quality control can be performed, but user knowledge requirements increase and error susceptibility increases
Solution Approach 1:
The system automatically determines inspection criteria by analyzing production process data and identifying unstable features, eliminating the need for manual user definition. The coordinate measuring machine autonomously adjusts measurement parameters based on statistical process control rules, making the system self-configuring and reducing dependency on user expertise.
Solution Approach 2:
The system dynamically changes measurement parameters based on detected instability in production processes. When variations exceed thresholds, the system automatically increases measurement frequency or changes inspection criteria, adapting parameters in real-time without user intervention while maintaining quality control reliability.
2Reliability
If comprehensive inspection criteria are applied to all workpieces, then quality control accuracy is maintained, but inspection time increases and production yield decreases
Solution Approach 1:
The system applies differentiated inspection strategies to different workpieces based on their specific risk profiles. Workpieces exhibiting unstable features undergo more frequent or comprehensive inspection, while stable workpieces receive reduced inspection, localizing quality control efforts to where they are most needed and maintaining accuracy without universal time penalties.
Solution Approach 2:
The system performs partial inspections on stable workpieces (measuring only critical features) and excessive inspections on unstable workpieces (measuring all features multiple times). This dynamic adjustment of inspection scope maintains overall quality accuracy while reducing average inspection time across the production batch.
3Productivity
If inspection scope is reduced to speed up process, then production yield increases, but quality control reliability decreases and user knowledge requirements increase
Solution Approach 1:
The inspection scope dynamically adjusts based on real-time production process stability. The system continuously monitors process parameters and automatically expands or contracts the inspection scope in response to detected variations, maintaining quality reliability while optimizing for production speed without requiring user knowledge to determine the appropriate scope.
Solution Approach 2:
The system uses feedback from production process data to continuously optimize inspection scope. By analyzing correlations between process parameters and measurement results, the system automatically identifies which features require inspection and adjusts the scope accordingly, maintaining reliability while maximizing productivity without user intervention.
4Reliability
If all test features are measured with equal frequency, then comprehensive quality control is achieved, but measurement time increases
Solution Approach 1:
The system applies different measurement frequencies to different test features based on their instability characteristics. Unstable features are measured more frequently while stable features are measured less frequently, localizing intensive measurement efforts to critical features and reducing overall measurement time while maintaining comprehensive quality control.
Solution Approach 2:
The system segments test features into stable and unstable categories based on statistical analysis, then applies differentiated measurement strategies to each segment. This segmentation allows comprehensive monitoring of all features while optimizing measurement time by concentrating resources on unstable features that require more frequent inspection.
Data Source
Figure 1
Figure 2~3
AI summary
Method for measuring workpieces (4), wherein each of the workpieces (4) has several structural features that are present in the same way in the other workpieces (4), and which are the test features (18, 20) to be measured, the method comprising the following steps: a. Determining or assuming (S1) at least one unstable test feature (18; 20), wherein the unstable test feature exhibits or is assumed to exhibit a violation of at least one statistical control rule, b. Determining or assuming (S1) at least one stable test feature (18; 20), wherein the stable test feature does not exhibit or is assumed to exhibit a violation of at least one statistical control rule, c. Measuring (S3) a plurality of workpieces (4), wherein the at least one unstable test feature (20) is measured more frequently than the at least one stable test feature (18), d.Determine (S4) whether at least one unstable test characteristic (20) continues to violate at least one statistical control rule, such that at least one unstable test characteristic (21) remains unstable and e. Determine whether at least one stable test characteristic (18) continues to not violate at least one statistical control rule, such that at least one stable test characteristic (18) remains stable.