Dynamic Specification Limit Calibration for Binning Accuracy
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Solution Overview
Problem
Traditional manufacturing methods rely on statically defined specification limits, leading to misclassifications due to measurement device variabilities, errors, and changing operating conditions, resulting in increased waste, reduced efficiency, and higher costs.
Innovation Solution
A computer-program product that uses a graphical user interface to input measured unit distributions, device characteristics, and distribution types, and computes an estimated true unit distribution to dynamically set calibrated specification limits, reducing misclassification errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static specification limits are used for classification, then the system is simple to implement, but misclassification errors increase due to measurement device variabilities and operating condition changes
Solution Approach 1:
The patent implements dynamic specification limits that automatically adjust based on real-time measurement device characteristics and operating conditions. The system continuously monitors measurement variability and recalibrates specification limits to maintain optimal classification accuracy, transforming the static classification approach into a dynamic adaptive system that responds to changing conditions.
Solution Approach 2:
The system incorporates feedback loops where classification results and measurement device performance data are continuously fed back into the specification limit calculation process. This feedback mechanism enables the system to learn from past classifications and measurement variations, automatically refining specification limits to reduce misclassification errors while maintaining system simplicity through automated decision-making.
2Reliability
If dynamic specification limits are implemented to reduce misclassification errors, then classification accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The system manages computational complexity by focusing parameter changes on key measurement device characteristics and operating conditions that most significantly impact classification accuracy. Rather than processing all possible variables, the system identifies and monitors critical parameters, adjusting specification limits based on changes in these specific factors to achieve high accuracy with controlled computational requirements.
3Measurement precision
If measurement device variabilities are accounted for in specification limits, then misclassification errors decrease, but the system requires more sophisticated calibration processes
Solution Approach 1:
The system implements self-service calibration where the measurement and classification system automatically adjusts its own specification limits based on monitored measurement device variabilities. The system performs self-diagnosis and self-calibration by detecting changes in measurement characteristics and automatically recalibrating specification limits without requiring external intervention or complex manual calibration procedures.
Data Source
AI summary
A system, method, and computer-program product includes obtaining, via a graphical user interface: an input of a measured unit distribution derived from measurements, by a measuring device, of a plurality of instances of a physical unit; an input of characteristics of the measuring device used in the measurements of the plurality of instances of the physical unit; and an input of a type of distribution for fitting a set of measurement values of the physical unit to a target distribution; computing, via a unit distribution estimation algorithm, an estimated true unit distribution of the plurality of instances of the physical unit based on (a) the input of the measured unit distribution, (b) the input of the characteristics of the measuring device, and (c) the input of the type of distribution; and using quantitative characteristics of the estimated true unit distribution to mitigate binning classification error of the physical units.