Automated Engineering Design Violation Clustering and Risk Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual iteration through millions of engineering design rule violations is time-consuming and inefficient, as designers struggle to differentiate between critical and non-critical violations, limiting the attractiveness of engineering design aids and potentially compromising product performance.
Innovation Solution
A system that clusters engineering design rule violations based on characteristics and waiver decisions, assigning a risk level to each cluster, allowing for automatic approval of low-risk violations, thereby reducing processing time and improving design efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual iteration through design rule violations is performed, then critical violations can be identified, but processing time increases significantly
Solution Approach 1:
The patent introduces an intermediary classification system that automatically categorizes design rule violations into different risk levels using historical waiver data and machine learning models. This intermediary layer filters and prioritizes violations before human review, reducing the time required to identify critical issues while maintaining reliable detection capabilities.
Solution Approach 2:
The system performs preliminary analysis by pre-processing design rule violations through clustering algorithms and risk assessment models before presenting them to designers. Historical waiver decisions are analyzed in advance to establish patterns, so when new violations occur, they can be quickly categorized and prioritized without requiring full manual review of each violation.
2Manufacturing precision
If all design rule violations are manually reviewed, then design quality is maintained, but design productivity decreases
Solution Approach 1:
The patent applies local quality by treating different design rule violations differently based on their characteristics and historical impact. Instead of uniform manual review, the system identifies specific violation patterns that require human judgment versus those that can be automatically waived, allowing designers to focus their attention only on violations in critical areas while maintaining overall design quality.
Solution Approach 2:
The system changes the parameter of violation handling from binary (review or ignore) to a multi-level classification system with different risk categories. By transforming violations into ranked groups based on historical data and violation characteristics, the system maintains quality control for high-risk violations while enabling automatic processing of low-risk ones, thereby improving productivity.
3Reliability
If design rules are made more restrictive to ensure proper function, then product reliability improves, but design complexity increases
Solution Approach 1:
The patent segments the complex set of design rules into manageable categories based on violation patterns and risk levels. By dividing design rules into groups with similar characteristics and historical waiver patterns, the system makes the complexity more tractable and enables automated processing of routine violations while maintaining strict oversight of critical rules.
Data Source
AI summary
Systems and methods are provided for reducing processing time of an automated engineering design. A repository of engineering design rule violations and corresponding waiver decisions regarding the design rule violations is accessed. A clustering operation is performed on violations in the repository to form clusters of violations based on one or more characteristics of the violations. Waiver decisions associated with violations in each cluster are evaluated to assign a risk level to each cluster. A plurality of detected engineering design rule violations associated with an engineering design are identified. Each of the detected violations is iterated through to determine which cluster that detected violation belongs. Detected violations associated with low risk clusters are automatically to approve the engineering design.