Tolerance Interval Limits Under Technical Cutoff Constraints
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Solution Overview
Problem
Existing methods for determining tolerance intervals in production processes, such as ISO 16269-6, do not adequately consider technical limits, leading to questionable results and high computational demands, especially in the context of manufacturing drug delivery devices.
Innovation Solution
A method that involves providing sample values, choosing a probability distribution function based on technical parameters, determining a cutoff value, specifying a probability content, and calculating transformed probability content to establish tolerance intervals, considering technical limits, thereby reducing computational complexity and ensuring accurate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard statistical methods (ISO 16269-6) are used to calculate tolerance intervals, then the calculation can be performed without considering technical limits, but the results become questionable and computationally demanding when technical limits are present
Solution Approach 1:
The patent transforms the probability content parameter by applying a mathematical transformation that incorporates the technical limit. Specifically, it uses a transformed probability content that accounts for the truncated distribution, allowing accurate tolerance interval calculation while simplifying the computational approach by working with transformed parameters rather than directly handling the complex truncated distribution integration
Solution Approach 2:
The patent introduces a cutoff value as an intermediary parameter that represents the technical limit in the statistical calculation. This cutoff value serves as a mediator between the technical constraint and the statistical distribution, enabling the integration of technical limits into the tolerance interval calculation without requiring complex direct optimization
2Measurement precision
If technical limits are incorporated into the tolerance interval calculation, then the accuracy for parameters with physical boundaries is improved, but the computational demand increases
Solution Approach 1:
The patent applies a parameter transformation approach where the probability content is transformed to account for the technical limit. This transformation allows the calculation to achieve precise tolerance limits by working with modified probability parameters that inherently incorporate the technical boundary, thereby improving measurement precision while avoiding iterative computational methods that would increase calculation time
3Ease of manufacture
If a transformed probability content is used to calculate tolerance intervals considering technical limits, then accurate results are achieved with reduced computational complexity, but the method requires additional steps for transformation
Solution Approach 1:
The patent performs preliminary transformation of the probability content parameter before conducting the tolerance interval calculation. By pre-transforming the probability content to account for the technical limit, the method simplifies the subsequent calculation steps, making the overall process easier to manufacture or implement despite the initial transformation step
Data Source
AI summary
Disclosed is a method for determining a limit, comprising:providing a plurality of sample values, wherein the sample values define a sample value distribution, the sample values being values of a technical parameter related to a sample, wherein the sample items are parts of drug delivery devices, wherein the sample items are of the same construction, and wherein the technical parameter is limited by at least one technical limit value,depending on the technical parameter and/or the sample values, choosing a probability distribution function,using the technical limit value to determine a cutoff value for the probability distribution function,specifying a probability content for the tolerance interval, andproviding the limit of the tolerance interval for the technical parameter based on a transformed probability content, wherein said transformed probability content is based on the cutoff value and based on the specified probability content.