AI Component Matching for Tolerance-Driven Failure Reduction
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Solution Overview
Problem
Existing end-products face failures due to component parts that fall on opposite extremes of their tolerance range, leading to inefficiencies and increased costs from discarding otherwise usable parts.
Innovation Solution
An AI model is implemented to assign specification scores to component parts based on their tolerance ranges, recommending optimal combinations that minimize product failures by ensuring precise interfacing, thereby reducing wastage and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If component parts with tight tolerance ranges are used, then product precision is improved, but manufacturing cost and waste increase
Solution Approach 1:
The patent dynamically adjusts tolerance parameters based on combination context rather than applying fixed tight tolerances to all components. The AI model analyzes the specific combination of components and determines appropriate tolerance ranges for each component within that combination. This allows looser tolerances (reducing waste) where acceptable while maintaining precision where critical, resolving the contradiction between precision and waste reduction.
Solution Approach 2:
The system applies different tolerance requirements to different components based on their specific role and interaction within the product assembly. Rather than uniformly applying tight tolerances to all components, the AI model identifies which components critically affect product precision and which have more flexibility. This localized quality approach maintains necessary precision while minimizing manufacturing waste across the entire product.
2Reliability
If AI analysis is applied to all component combinations, then product failure probability decreases, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the component selection process into multiple stages: initial filtering based on individual specifications, intermediate evaluation of component pairs or small groups, and final optimization of complete combinations. The AI model applies different levels of analysis intensity at each stage, focusing computational resources on critical decision points. This segmentation reduces overall computational complexity while maintaining comprehensive failure probability assessment.
Solution Approach 2:
The system applies AI analysis selectively to component combinations rather than uniformly to all possible combinations. The AI model identifies and focuses analysis on combinations with higher failure risk based on initial screening criteria, historical data, or component criticality. This partial action approach reduces computational burden while still capturing the majority of failure risks, resolving the contradiction between thorough analysis and computational complexity.
Data Source
AI summary
Described are techniques for artificial intelligence (AI) assisted recommendations for component parts for end-products that reduce the occurrence of a product failures. The techniques include obtaining measurement data for groups of component parts configured to be assembled as part of an end-product. The techniques further include obtaining specification scores for the component parts included in the groups of component parts based on the measurement data. The techniques further include selecting a component part combination from the groups of component parts using artificial intelligence analysis of the specification scores to determine that the component part combination decreases a probability of a product failure as compared to historical occurrences of the product failure. The techniques further include outputting information for at least one component part included in the component part combination.