Rust Detecting Device for Joining Surface Susceptibility
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Solution Overview
Problem
Existing technologies lack a method to efficiently determine the rust susceptibility of joining surfaces in structural bodies like vehicles, which is crucial for design and prevention.
Innovation Solution
A rust detecting device that assesses the rust susceptibility of joining surfaces by considering the complexity of the surface shape, using input information such as three-dimensional CAD data and CAE analysis data, and employing a machine learning-trained model to predict rust susceptibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional rust evaluation methods are used, then rust prediction on general surfaces is achieved, but rust susceptibility of joining surfaces cannot be determined
Solution Approach 1:
The invention applies local quality by specifically addressing the unique characteristics of joining surfaces. It divides the evaluation into general surfaces and joining surfaces, with dedicated evaluation methods for each. For joining surfaces, it considers specific factors such as gap dimensions, overlapping area ratios, and joining methods, thereby achieving reliable rust susceptibility determination for this specific local area that conventional general surface methods cannot handle.
2Measurement precision
If physical vehicle testing is conducted for rust evaluation, then accurate rust susceptibility data is obtained, but labor and time costs increase significantly
Solution Approach 1:
The invention implements preliminary action by performing rust susceptibility evaluation during the design phase using three-dimensional CAD data and CAE analysis data. This allows rust evaluation to be completed before physical vehicle testing, enabling design modifications to be made in advance. The system calculates rust susceptibility based on joining surface geometry and conditions, providing accurate predictions without requiring actual physical testing, thereby significantly reducing both time and labor costs while maintaining measurement precision.
3Productivity
If comprehensive rust evaluation is performed in the data phase, then development time is reduced, but evaluation complexity increases
Solution Approach 1:
The invention uses three-dimensional CAD data and CAE analysis data as intermediaries to bridge the gap between design phase information and rust susceptibility evaluation. Instead of requiring complex physical testing setups, the system processes existing digital design data to calculate rust susceptibility. This intermediary approach enables comprehensive evaluation during the data phase, improving productivity while managing complexity by leveraging already-available digital models rather than creating new complex evaluation infrastructure.
Data Source
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AI summary
[Problem to be Solved] Provide a rust detecting device capable of efficiently determining a rust susceptibility of a joining surface at which a plurality of members is joined. [Solution] A rust detecting device 1 includes a detecting unit 12 configured to determine a rust susceptibility of a joining surface BP based on input information Iin including information indicating a complexity of a shape of the joining surface BP. The detecting unit 12 may determine a rust susceptibility of the joining surface BP by providing the input information Iin to a machine learning-trained model M. The information indicating a complexity of a shape of the joining surface BP may be expressed by an area-to-perimeter ratio of the joining surface BP.