Evaluation Site Accuracy Control for Vehicle Assembly
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Solution Overview
Problem
In the prototyping stage before mass production, it is challenging to derive accurate regression equations for vehicle assembly accuracy using a limited amount of data, as the number of workpiece vehicles required for accurate correlation coefficient determination is insufficient.
Innovation Solution
A method involving finite element analysis, Lasso regression, and Bayesian estimation to identify critical evaluation sites and joining areas in a three-dimensional model, allowing for the generation of regression models aligned with actual parts and predicting partial regression coefficient probability distributions to adjust evaluation sites effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of objective variables is increased to improve correlation accuracy, then the correlation coefficient accuracy is improved, but the amount of data required increases
Solution Approach 1:
The patent extracts only the critical evaluation sites and critical joining areas that have the most significant impact on assembly accuracy. By using finite element analysis to identify sites with large displacement changes, the method filters out non-critical variables, reducing the number of objective variables while maintaining correlation accuracy.
Solution Approach 2:
The patent segments the evaluation sites into critical and non-critical categories based on their impact on assembly accuracy. This segmentation allows the regression model to focus on a subset of variables (critical joining areas) that provide sufficient explanatory power, reducing the overall data requirement.
2Manufacturing precision
If the number of workpiece vehicles is increased to obtain accurate correlation coefficients, then the regression equation accuracy is improved, but the prototyping stage feasibility deteriorates
Solution Approach 1:
The patent extracts only the essential variables (critical joining areas with large displacement changes) from the complete set of possible evaluation sites. This extraction reduces the number of required data points, making it feasible to conduct regression analysis during the prototyping stage with a limited number of workpiece vehicles.
Solution Approach 2:
The patent performs finite element analysis in advance to pre-identify critical evaluation sites and critical joining areas before actual measurement. This preliminary identification guides the selection of variables for regression analysis, ensuring that the limited available data is used efficiently on the most impactful sites.
3Adaptability or versatility
If all evaluation sites are included in the regression model, then the model comprehensiveness is improved, but the model complexity increases
Solution Approach 1:
The patent extracts only the critical evaluation sites and critical joining areas that significantly impact assembly accuracy. By removing non-critical variables from the model, the method reduces model complexity while maintaining comprehensiveness regarding the most important factors.
Solution Approach 2:
The patent applies different levels of analysis to different parts of the system. Critical joining areas receive detailed analysis and inclusion in the regression model, while non-critical areas are excluded. This local differentiation optimizes model complexity by focusing computational resources on high-impact locations.
Data Source
AI summary
Provided is a production method for a completed component, the production method including: a first step for performing finite element analysis to determine an amount of change at evaluation sites of the completed component on a three-dimensional model of the completed component that occurs when displacement is applied to joining areas between the individual parts on the three-dimensional model; a second step for extracting combinations of critical evaluation sites having a relatively large amount of change among the evaluation sites on the three-dimensional model and their corresponding critical joining areas among the joining areas on the three-dimensional model; a third step for generating regression models through Lasso regression; a fourth step for performing Bayesian estimation; a fifth step for selecting an adjustment site of the individual parts; and a sixth step for producing the completed component.