I3C Statistical Indicator for Manufacturing Quality Control
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Solution Overview
Problem
Current manufacturing processes face challenges in maintaining high production rates and quality standards, particularly in the aeronautical industry, where quality controls often require halting production to adjust manufacturing conditions, and statistical indicators like the centering coefficient (Cc) are influenced by standard deviation, leading to unreliable confidence intervals.
Innovation Solution
A process that calculates a new statistical indicator, I3C, which takes into account the standard deviation to provide stable and reliable centering information, allowing for automated adjustment of manufacturing parameters to optimize the production flow and ensure quality compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical checks are carried out during production to control quality, then quality reliability is improved, but production continuity deteriorates due to potential halts
Solution Approach 1:
The patent implements a feedback mechanism where statistical indicators (centering coefficient Cc and confidence intervals) are continuously calculated from production data and used to automatically adjust manufacturing parameters. This closed-loop control allows quality monitoring without halting production, as the system proactively adjusts parameters to maintain quality within specifications.
Solution Approach 2:
The manufacturing system performs self-adjustment based on statistical analysis of its own output. The automated calculation of centering coefficients and confidence intervals enables the system to self-diagnose quality deviations and self-correct by modifying manufacturing parameters, eliminating the need for external quality interventions that would halt production.
2Loss of information
If the centering coefficient Cc is used to monitor manufacturing quality, then quality information is obtained, but measurement reliability deteriorates due to influence from standard deviation variations
Solution Approach 1:
The patent combines multiple statistical indicators (centering coefficient Cc and confidence intervals) into a composite quality assessment system. By integrating both the centering coefficient and its confidence interval, the system creates a more robust quality metric that compensates for the limitations of using Cc alone, particularly its sensitivity to standard deviation variations.
Solution Approach 2:
The patent transforms the quality monitoring approach by changing from monitoring a single parameter (Cc) to monitoring a pair of parameters (Cc and its confidence interval). This parameter transformation allows the system to distinguish between genuine quality deviations and variations caused by standard deviation changes, improving measurement reliability.
Data Source
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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 mean μ and a standard deviation σ of the characteristic dimension measured are calculated, and then a value of a statistical indicator I3C defined according to formula (I) is calculated for each sample collected, where: o Ccmax is a maximum centring coefficient imposed for the manufacture of the parts; o TS is an upper tolerance of the characteristic dimension measured ; o TI is a lower tolerance of the characteristic dimension measured ; d) the value of the statistical indicator I3C thus calculated for the sample collected is compared with a reference value to detect a possible deviation; e) the manufacturing flow for the parts is steered as a function of the results of the comparison by fitting the adjustment parameters of the manufacturing device so as to optimize the deviation between the value of the statistical indicator and the reference value.