Door Trim Contact Analysis for Early BSR Risk Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automobile manufacturers face challenges in reducing Buzz, Squeak, and Rattle (BSR) noise issues during the development stage, leading to increased testing costs and rising field claims related to vehicle interior noise.
Innovation Solution
A BSR pre-validation system and method with door trim contact point analysis that identifies high-risk areas for BSR generation by analyzing door trim design data, material databases, and BSR improvement history information, and derives improvement measures to mitigate these risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If BSR validation is performed only after manufacturing completion, then manufacturing process flexibility is maintained, but BSR noise problems are detected too late requiring costly retesting and rework
Solution Approach 1:
The patent applies preliminary action by performing BSR validation at the drawing stage before manufacturing. The system analyzes contact surfaces, material combinations, and fastening conditions in design data to identify potential BSR risks early, allowing design modifications to prevent noise issues before production begins.
Solution Approach 2:
The patent replaces physical prototyping and manual testing with an automated computational system. The BSR pre-validation unit uses algorithms to analyze design data, calculate risk scores based on contact surface parameters, and identify potential noise issues without requiring physical samples or laboratory testing.
2Reliability
If extensive testing is conducted during development stage to reduce BSR noise, then BSR noise quality improves, but testing costs and time consumption increase
Solution Approach 1:
The patent extracts only the critical parameters needed for BSR validation from complete design data. The system identifies and analyzes specific contact surfaces, material combinations, and fastening conditions that contribute to BSR risks, ignoring non-critical design elements to reduce computational and validation resources required.
Solution Approach 2:
The patent applies partial action by focusing validation efforts only on high-risk contact surfaces identified through automated analysis. Rather than testing all possible design scenarios, the system calculates risk scores and concentrates resources on the most problematic areas, achieving effective BSR prevention with reduced testing scope.
3Reliability
If BSR validation is performed at drawing stage with detailed analysis, then BSR noise quality improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the BSR validation process into distinct functional units: data acquisition, contact surface identification, material analysis, risk calculation, and reporting. Each unit handles a specific aspect of the validation, making the overall complex system manageable through modular design and clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates complex design data into simplified risk assessments. The BSR pre-validation unit acts as a mediator between raw design information and final validation conclusions, using standardized algorithms to convert diverse input data into comparable risk scores that guide design decisions.
Data Source
AI summary
Provided is a BSR pre-validation system and method with door trim contact point analysis, which includes: an information collection unit obtaining door trim design data, a material database, and BSR improvement history information; a material analysis unit extracting material information of contact surface parts based on the door trim design data and the material database, and determining friction noise risk between matching parts; a pre-validation unit determining contact surfaces with a squeak risk index or rattle risk index higher than or equal to a preset threshold value as an expected risk group; and an improvement measure derivation unit deriving an improvement measure for the expected risk group based on the BSR improvement history information, and at a drawing stage, the improvement measure is derived through BSR pre-validation, and BSR pre-validation and a single-item validation result are compared to confirm a validity of the BSR pre-validation.