Weighted Statistical Indicators for Manufacturing Quality Control
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Solution Overview
Problem
Current statistical methods for monitoring and controlling manufacturing processes in industries like aeronautics face challenges with variable part numbers and significant variations in production samples, leading to inadequate quality control and inefficient production adjustments.
Innovation Solution
A method using weighted averages and standard deviations calculated with exponential weighting, allowing for the calculation of statistical indicators like capability indices and centring coefficients, which adjusts manufacturing parameters to optimize quality control across variable production flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical controls are performed during production to ensure quality criteria, then quality monitoring is improved, but production throughput may be impaired due to potential production stops
Solution Approach 1:
The patent applies exponential weighting to make the statistical indicators dynamic rather than static. By weighting recent measurements more heavily than older ones, the system adapts to current production conditions while maintaining statistical rigor. This dynamic approach allows continuous quality monitoring without requiring production stops for reinitialization, thus resolving the contradiction between quality control and production throughput.
Solution Approach 2:
The patent transforms traditional statistical indicators by applying exponential weighting functions to their calculation parameters. This parameter change enables the indicators to reflect recent production trends more accurately, allowing the system to detect quality deviations earlier and adjust accordingly without stopping production, thereby maintaining both quality control and throughput.
2Ease of operation
If traditional statistical indicators are used with fixed sampling, then manufacturing process monitoring is simplified, but they fail to account for variable sample sizes and recent production trends
Solution Approach 1:
The patent introduces exponential weighting to make statistical indicators dynamic, allowing them to adapt to variable sample sizes and reflect recent production trends. This maintains ease of operation by using standard statistical frameworks while improving measurement precision through time-weighted calculations that emphasize current production conditions over historical data.
3Reliability
If production flow is regulated to stop completely when quality defects are excessive, then quality standards are maintained, but manufacturing efficiency is reduced
Solution Approach 1:
The patent uses exponentially weighted statistical indicators to detect quality deviations earlier in the production process. By continuously monitoring with heightened sensitivity to recent measurements, the system can take preliminary corrective actions before quality defects become excessive, preventing the need for complete production stops and reducing manufacturing time loss while maintaining quality standards.
Data Source
AI summary
The invention pertains to a method of manufacturing parts produced with a manufacturing device, based on the analysis of at least one statistical indicator representative of a characteristic dimension of the parts, according to which: a) in the course of time several samples are collected, each sample comprising several parts produced with the manufacturing device; b) the characteristic dimension of each part of the sample is measured; c) for each sample collected a weighted mean and a weighted standard deviation of the characteristic dimension are calculated according to an exponential weighting on the basis of a mean and standard deviation of the characteristic dimensions measured on the parts of said sample, of weighted means and of weighted standard deviations of the characteristic dimension which are calculated for previously collected samples; d) for each sample collected a value of the statistical indicator is calculated on the basis of the weighted mean and of the weighted standard deviation thus calculated; e) a value of the statistical indicator thus calculated for the sample collected is compared with a reference value to detect a possible deviation; f) the manufacture of the parts is steered as a function of the results of the comparison by fitting the manufacturing device adjustment parameters to optimize the deviation between the value of the statistical indicator and the reference value.