Statistical Confidence in Simulation Contact Detection
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Solution Overview
Problem
Traditional contact detection algorithms in simulations are inefficient and often require manual adjustment, leading to time-consuming processes due to inadequate tolerance selection, which can result in over- or under-detection of contacts between bodies of varying sizes.
Innovation Solution
A method to determine a confidence level for contact pairs by using a plurality of testing tolerances and calculating a repeatability ratio based on positive detections, allowing for automated detection of contact between faces in simulations, thereby reducing manual effort and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional contact detection algorithms use a single large tolerance value, then detection speed is improved, but contact detection accuracy deteriorates due to over-detection of contacts
Solution Approach 1:
The patent segments the contact detection process into multiple stages: initial detection with a large tolerance value for fast identification of potential contacts, followed by refinement stages with progressively smaller tolerance values to verify and eliminate false positives. This multi-stage segmentation resolves the contradiction by applying different tolerance levels at different detection phases.
Solution Approach 2:
The patent implements dynamic tolerance adjustment where the tolerance value changes adaptively during the detection process. The system starts with a large tolerance for rapid screening, then dynamically reduces tolerance for subsequent verification passes. This dynamic approach maintains high detection speed while improving accuracy through progressive refinement.
2Measurement precision
If manual contact definition is used, then contact detection accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent implements self-service through automated contact detection that performs multi-stage verification independently. The system automatically adjusts tolerance values, identifies potential contacts, verifies them through refinement stages, and produces accurate contact definitions without requiring manual intervention. This self-service automation achieves manual-level accuracy while dramatically reducing setup time.
Solution Approach 2:
The patent applies preliminary action by performing automated contact detection and verification before manual review. The system pre-identifies and validates contacts using multiple tolerance levels, producing a refined contact list that requires minimal manual adjustment. This preliminary automated action reduces both time consumption and the need for manual intervention while maintaining high accuracy.
3Device complexity
If a single tolerance value is used for contact detection, then device complexity is reduced, but reliability of contact detection deteriorates due to over- or under-detection
Solution Approach 1:
The patent changes the tolerance parameter dynamically through multiple detection passes with different tolerance values. The system uses a sequence of tolerance parameters (large to small) to progressively refine contact detection results. This parameter variation approach improves reliability by reducing false positives and negatives while maintaining manageable algorithm complexity through systematic parameter progression.
Data Source
AI summary
Systems and methods are provided for determining a confidence level on contact pairs identified through traditional contact detection algorithms for an assembly of bodies. A plurality of testing tolerances are identified. For each of the identified testing tolerances, a determination is made as to whether a distance between any two faces of the bodies in the assembly is less than that identified testing tolerance and when this happens a count of positive detections is increased. A ratio of the count of positive detections to the total number of times the detections are run tolerances is determined (called repeatability of a face pair), where the confidence level is based on the determined repeatability ratio.