Neural Network Fastener Design Automation
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Solution Overview
Problem
Current methods for selecting and designing fasteners rely heavily on human intervention, which can be inefficient and may not always yield optimal results, especially when only partial information is provided, and there is a need for a more automated and accurate process that considers various parameters such as geometry, materials, and usage context.
Innovation Solution
A computer-implemented system utilizing neural network models, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to query a fastener description repository, allowing for the identification and generation of matching or combined fastener designs based on initial parameter values, including images and property values, to provide a set of possible matching fasteners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human designers use CAD programs to specify all fastener characteristics, then design flexibility and control are improved, but design time and resource consumption increase
Solution Approach 1:
The system enables self-service by allowing the fastener design system to automatically generate design recommendations and complete design specifications without requiring continuous human intervention. The neural network model independently processes partial inputs and generates comprehensive fastener designs, reducing the time designers need to spend manually specifying all characteristics.
Solution Approach 2:
The system performs preliminary action by pre-processing the partial fastener information provided by designers and pre-generating multiple potential design options before the designer makes a final selection. This preliminary processing reduces the overall design time by preparing structured recommendations in advance.
2Measurement precision
If human designers manually select fasteners from catalogs, then design accuracy can be maintained, but productivity and efficiency decrease
Solution Approach 1:
The system replaces the manual mechanical process of browsing and selecting fasteners from catalogs with an automated neural network-based system. The neural network model automatically processes design requirements, queries the fastener database, and retrieves matching fasteners, substituting human manual search and selection operations with automated computational processes that maintain accuracy while dramatically improving efficiency.
Solution Approach 2:
The system uses copying by creating digital representations and feature extractions from fastener images and specifications stored in the database. The neural network model works with these digital copies and feature vectors rather than requiring physical catalog browsing, enabling automated comparison and matching while preserving design accuracy.
3Manufacturing precision
If complete fastener specifications are provided, then design precision is improved, but information processing requirements increase
Solution Approach 1:
The system applies extraction by using feature extraction techniques to identify and isolate the most critical fastener parameters from images and specifications. Instead of processing all possible fastener characteristics equally, the neural network model extracts and prioritizes key features such as geometry, material properties, and functional requirements, reducing computational overhead while maintaining design precision.
Solution Approach 2:
The system implements partial action by accepting and processing only the essential partial information provided by designers rather than requiring complete specifications upfront. The neural network model can work with incomplete inputs, extracting relevant features and generating design recommendations with sufficient precision without needing all possible parameters, thus reducing information processing requirements.
Data Source
AI summary
A method includes obtaining an initial set of fastener parameter values for a fastener, executing a neural network model using features extracted at least from the initial set of fastener parameter values to query a fastener description repository, obtaining, from the fastener description repository, a set of possible matching fasteners, and presenting a matching fastener when the matching fastener is in the set of possible matching fasteners.