Industrial Component Similarity Search for Fuzzy Interchangeability
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Solution Overview
Problem
Existing methods for searching and identifying interchangeable industrial components are limited by their reliance on theoretical models, offering little adaptability or flexibility, and fail to effectively incorporate user feedback into the search process.
Innovation Solution
A computer-implemented method that uses similarity embeddings and user feedback to select interchangeable industrial components, incorporating neural networks and fuzzy logic to create dynamic interchangeability classes, allowing for the generalization of replaceability into a fuzzier context while maintaining performance and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If theoretical models are used for component similarity search, then manufacturing precision and reliability are maintained, but adaptability and flexibility deteriorate
Solution Approach 1:
The patent implements feedback loops where user interactions with search results (selections, rejections, modifications) are continuously incorporated to refine and update the similarity search models. This allows the system to adapt to new situations and user preferences while maintaining the reliability of established theoretical foundations through structured feedback integration.
Solution Approach 2:
The patent transforms static theoretical models into dynamic systems that evolve over time. The similarity search models are continuously updated based on user feedback, allowing the system to adapt its behavior and parameters dynamically while maintaining core reliability through controlled evolution of the models.
2Ease of operation
If user feedback is incorporated as a polishing step, then ease of operation improves, but device complexity increases and feedback integration remains superficial
Solution Approach 1:
The patent merges the user feedback mechanism with the core search algorithm rather than treating it as a separate polishing step. User feedback is integrated directly into the similarity computation process, combining what were previously separate functions into a unified system that reduces overall complexity while improving ease of operation.
Solution Approach 2:
The system automatically processes and integrates user feedback without requiring manual intervention or complex configuration. The feedback mechanism serves itself by automatically updating models and re-ranking results based on user interactions, reducing the operational burden on users while maintaining system simplicity.
3Manufacturing precision
If strict replaceability criteria are applied, then manufacturing precision is maintained, but adaptability to fuzzy contexts deteriorates
Solution Approach 1:
The patent applies different levels of precision to different aspects of component comparison. Critical dimensions and specifications maintain strict precision requirements, while other attributes allow for fuzzy matching and user-defined flexibility. This local differentiation of quality requirements enables both precision and adaptability to coexist.
Solution Approach 2:
The patent allows dynamic adjustment of precision parameters based on the specific search context, component type, and user preferences. By changing the strictness of matching criteria for different parameters and situations, the system can maintain manufacturing precision where needed while providing adaptability in fuzzy contexts where appropriate.
Data Source
AI summary
A computer implemented method for improving a similarity search of an industrial component model including obtaining a set of industrial component models, each having associated attributes and a similarity embedding, receiving a similarity request using a given industrial component model as an input, the output of said similarity request being a first subset of industrial component models selected from the set of industrial component models based on the comparison between similarity embeddings and the similarity embedding of the input industrial component model, receiving a second subset of industrial component models from said first subset of industrial component models based on an interchangeability criteria of the input industrial component model with any industrial component model of said second subset of industrial component models, associating a similarity attribute to the input industrial component model, and computing a new set of similarity embeddings.