Production Process Control With Adaptive Feature Testing
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Solution Overview
Problem
Existing production processes incur unnecessary costs due to excessive testing during stable periods and fail to identify errors adequately during unstable periods due to insufficient testing, as current methods do not account for the dynamic nature of process stability.
Innovation Solution
A method that identifies a main feature and secondary features, testing the main feature frequently and secondary features sparingly, using IoT technologies for automated detection, and adjusting test frequencies based on stability criteria to optimize testing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random sampling measurements are carried out at regular time intervals, then quality monitoring is performed, but unnecessary testing costs are incurred during stable periods and errors may not be identified adequately during unstable periods
Solution Approach 1:
The test frequency is made dynamic rather than static. The system continuously monitors process stability and adjusts the measurement frequency accordingly - increasing frequency when instability is detected and reducing frequency during stable periods. This resolves the contradiction by making the testing regime adaptive to actual process conditions rather than following a fixed schedule.
Solution Approach 2:
The system implements feedback control by continuously evaluating process stability based on measurement data and using this information to adjust future testing frequency. When measurements indicate process instability, the system increases monitoring intensity; when stability is confirmed, it reduces testing. This feedback mechanism ensures reliable error detection while minimizing unnecessary testing overhead.
2Manufacturing precision
If multiple features are tested frequently, then comprehensive quality control is achieved, but testing costs and time consumption increase significantly
Solution Approach 1:
The quality control process is segmented into different levels based on feature importance and process stability. Primary features are monitored continuously, while secondary features are monitored less frequently or only when instability is detected. This segmentation allows comprehensive quality control for critical parameters while reducing testing time for less critical ones.
Solution Approach 2:
Different measurement frequencies are applied to different features based on their importance and variability characteristics. Critical features that directly affect product quality receive frequent monitoring, while less critical features are monitored less intensively. This localized quality approach ensures comprehensive control where needed while minimizing overall testing time.
3Reliability
If test frequency is increased to detect errors adequately, then error identification improves, but testing overhead and costs increase
Solution Approach 1:
The system dynamically adjusts test frequency based on real-time process stability assessment rather than maintaining a constant high frequency. During stable periods, testing frequency is reduced to maintain production efficiency. When instability signals are detected, frequency increases automatically to ensure reliable error detection. This dynamic approach resolves the contradiction between detection capability and production efficiency.
Solution Approach 2:
The system uses feedback from process measurements to intelligently control testing frequency. When the feedback indicates stable process conditions, testing is reduced to maintain productivity. When feedback suggests potential instability or error risk, testing frequency increases to ensure detection. This feedback-driven approach optimizes both error detection and production efficiency.
Data Source
AI summary
A method for controlling a production process for components, wherein the components or a production device used for producing the components have or has features which are metrologically detectable. The method comprising specifying a test plan for detecting primary feature(s) and secondary feature(s) by tests, wherein the primary feature(s) is/are measured at a first test frequency and the secondary feature(s) is/are measured at a second test frequency, wherein at least one stability criterion is defined for the primary feature(s); producing the components and carrying out the test plan for producing test results in parallel, wherein solely the primary feature(s) is/are tested at the first test frequency; evaluating the determined test results; and, if at least one test result for the primary feature(s) violates the stability criterion, continuing the carrying out of the test plan, wherein at least a secondary feature is tested.