Multi-Dimensional Object Classification via Compound Vector Representation
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Solution Overview
Problem
Current product lifecycle management systems face inefficiencies in design, testing, and deployment due to the lack of effective classification and similarity detection of multi-dimensional objects, leading to increased time, resources, and costs, particularly in industries like aerospace where hundreds of thousands of parts require management across global factories, and the need for personalized products is on the rise.
Innovation Solution
Implementing a computer-implemented system that generates a compound vector representation of multi-dimensional objects from CAD files, converting them into mesh and graph representations, and processing these through machine learning systems for improved classification and similarity detection, reducing the complexity and resource requirements compared to traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional product lifecycle management systems are used for managing multi-dimensional objects, then comprehensive management coverage is achieved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent transforms complex multi-dimensional object data into simplified vector representations by changing the parameter representation from raw geometric data to normalized vector features. This parameter transformation enables faster processing while maintaining classification accuracy, directly resolving the contradiction between comprehensive management and time efficiency
Solution Approach 2:
The invention extracts essential geometric features from complex CAD models and represents them as compact vector embeddings. By taking out only the critical dimensional characteristics and representing them in a simplified vector form, the system achieves rapid classification without processing the full complexity of original multi-dimensional object data
2Measurement precision
If complex machine learning models are used for accurate classification, then classification accuracy improves, but computational resources and data footprint increase
Solution Approach 1:
The patent creates simplified vector representations that copy only the essential geometric characteristics of multi-dimensional objects. These vector copies retain the necessary information for accurate classification while being computationally lightweight, enabling simple ML models to achieve high accuracy without complex architectures or large data footprints
3Manufacturing precision
If detailed geometric representations are maintained for all objects, then manufacturing precision is preserved, but data processing complexity increases
Solution Approach 1:
The system changes the parameter representation from detailed geometric coordinates to normalized vector embeddings that preserve essential manufacturing characteristics. This parameter transformation maintains sufficient precision for manufacturing applications while dramatically reducing data structure complexity and improving processing efficiency
Data Source
AI summary
Implementations are directed to converting a product representation stored in a computer-readable file to a mesh representation, the product representation including a multi-dimensional model of an object, generating a graph representation from the mesh representation, the graph representation including a set of vertices, each vertex associated with a set of coordinates in multi-dimensional space, providing a compound vector representation as a data structure including a set of vectors, each vector in the set of vectors including an m-bit vector that encodes a respective vertex of the set of vertices, the m-bit vector including a set of bit groups, each bit group representing a respective coordinate associated in the set of coordinates of the respective vertex, and processing the compound vector representation through a ML system to generate a prediction associated with the object.