Adaptive Workpiece Measurement for Unstable Feature Control
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Solution Overview
Problem
Current quality control methods for industrial workpieces require significant user intervention, leading to inefficiencies and susceptibility to errors, as they rely on manual definition of test criteria and lack automation, which hinders the ability to adapt to production sequence changes and environmental variations.
Innovation Solution
A method that dynamically identifies unstable and stable test features using statistical control rules, adjusting measurement frequency and scope to prioritize more frequent measurement of unstable features, allowing for adaptive quality control and reduced measurement time through the use of dynamic test plans and coordinate measuring machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual definition of test criteria is used, then quality control can be performed, but user intervention increases susceptibility to errors and reduces efficiency
Solution Approach 1:
The system automatically determines test criteria by analyzing production sequence data and identifying critical parameters without requiring manual user definition. The coordinate measuring machine autonomously selects measurement points and evaluates workpiece quality based on learned patterns from production data, eliminating user intervention while maintaining reliable quality control.
Solution Approach 2:
The patent replaces manual user operations with an automated computer-based system that processes production sequence data to determine test criteria. The system uses algorithms to analyze production parameters and automatically generates measurement plans, substituting human judgment with computational analysis to improve reliability and reduce errors.
2Reliability
If full measurement scope is applied to all workpieces, then comprehensive quality control is achieved, but measurement time increases and yield decreases
Solution Approach 1:
The system applies different measurement intensities to different workpieces based on their specific characteristics and production sequence context. Instead of uniformly measuring all workpieces with the same comprehensive scope, the system dynamically adjusts the measurement plan to focus on critical parameters for each specific workpiece, achieving comprehensive quality control where needed while reducing unnecessary measurements elsewhere.
Solution Approach 2:
The patent implements adaptive measurement scopes that adjust the extent of testing based on production conditions. When production sequences indicate stable processes, the system reduces measurement scope to maintain yield. When variations are detected, the system expands measurement coverage to ensure quality, applying partial measurement action rather than always using full measurement scope.
3Productivity
If test scope is reduced to accelerate testing, then measurement time decreases, but quality control accuracy may be compromised
Solution Approach 1:
The system dynamically adjusts the measurement scope and depth based on real-time analysis of production sequence data. The measurement plan is not fixed but adapts to current production conditions, workpiece characteristics, and process stability indicators. This dynamic adjustment ensures that measurement precision is maintained by including necessary measurements while accelerating the process by excluding unnecessary ones.
Solution Approach 2:
The patent changes measurement parameters such as the number of measurement points, selection of critical features, and evaluation criteria based on production context. By dynamically modifying these parameters, the system optimizes the balance between measurement speed and accuracy, adjusting the measurement scope to match the actual quality risks present in each production batch.
4Reliability
If user-defined test criteria are used, then quality control can be performed, but automation of production sequence is hindered
Solution Approach 1:
The system enables full automation by having the coordinate measuring machine and control system autonomously determine test criteria without user intervention. The system self-adjusts measurement parameters, selects measurement points, and evaluates quality based on automated analysis of production sequence data, allowing the entire production sequence including quality control to run automatically.
Solution Approach 2:
The patent replaces manual user-based quality control with an automated computer-controlled system that processes production data and executes measurements autonomously. The system substitutes human decision-making with algorithmic analysis, enabling integration of quality control into the automated production sequence without requiring user intervention at each stage.
Data Source
AI summary
A method is described for measuring workpieces, each having structural features that form test features for measurement. The method determines an unstable one and a stable one of the test features, based on expected violation or satisfaction, respectively, of a statistical control rule. The method measures workpieces such that the unstable test feature is measured more frequently than the stable test feature. The method ascertains whether the unstable test feature remains unstable and whether the stable test feature remains stable. The method measures additional workpieces if the unstable test feature remained unstable and the stable test feature remaining stable. The determining is repeated if the unstable test feature is no longer unstable, the stable test feature is no longer stable, or any other measurement feature changes, such as if a new batch of workpieces is to be measured, environmental conditions change, or measurement has proceeded longer than a predefined threshold.