AI Component Classification in Product Data Management
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Solution Overview
Problem
Companies face challenges in efficiently classifying components in product lifecycle management due to the high cost of manual processes, which hinders the reuse of components and increases time to market and costs.
Innovation Solution
A method and system using artificial intelligence models to classify components in a product data management (PDM) system by predicting probabilities based on text, image, and shape data, and employing a closed-loop weighted model for accurate categorization, thereby automating the classification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification processes are used to classify components in a PDM system, then classification accuracy can be maintained through human judgment, but the cost and time required for classification increases significantly
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated system comprising a trained machine learning model that receives component data (images, CAD files, text descriptions) and automatically predicts classification categories. This substitution eliminates human labor while maintaining high classification accuracy through the model's learning from training datasets, thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system performs preliminary classification of components before they are fully integrated into the PDM system. By pre-classifying components using the trained model based on their metadata, images, and CAD data, the system prepares components for efficient retrieval and reuse, reducing the time required for later classification operations while maintaining accuracy through the model's pre-trained knowledge.
2Measurement precision
If manual classification processes are used to classify components in a PDM system, then detailed human analysis can be applied to each component, but the cost of classification increases significantly
Solution Approach 1:
The patent replaces expensive manual classification labor with an automated machine learning system. The trained model processes component data (images, CAD files, text) and generates classification predictions without requiring human intervention, thereby maintaining high classification accuracy while dramatically reducing the cost associated with manual labor, time, and resource consumption.
Solution Approach 2:
The system uses training datasets consisting of copied examples of previously classified components to teach the machine learning model. By learning from these copied examples, the model acquires classification knowledge without requiring ongoing human expertise for each new component, reducing the cost of classification while maintaining accuracy through pattern recognition from the training data.
3Productivity
If automated classification systems are implemented, then time and cost efficiency improve, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal machine learning model that can handle multiple types of component data (images, CAD files, text descriptions) and classify components across various categories. This multi-functional approach consolidates what would otherwise require multiple separate classification systems into a single unified model, improving productivity while managing system complexity through consolidation rather than proliferation of separate systems.
4Adaptability or versatility
If traditional PDM systems are used without AI integration, then system simplicity is maintained, but component reuse and innovation efficiency are reduced
Solution Approach 1:
The patent enhances traditional PDM systems by integrating a machine learning-based automated classification system. This substitution of manual classification with AI-driven automation enables the system to efficiently categorize components, improving adaptability and component reuse capability. The integration adds intelligence to the PDM system, allowing it to automatically understand and classify components based on their characteristics, thereby enhancing versatility without requiring complete system replacement.
Data Source
AI summary
A method and system for classifying components in a product data management (PDM) environment is disclosed. A method includes obtaining data files having information associated with a component to be classified in a PDM database. Each data file includes different types of information associated with the component. A series of predictions indicating a probability of the component belonging to one or more categories is computed based on each type of information associated with the component using one or more artificial intelligence models. An overall probability of the component belonging to the one or more categories is computed based on each of the series of predictions. The component is classified in at least one category of the one or more categories based on the computed probability of the component belonging to the one or more categories. The category associated with the classified component is output on a graphical user interface.