CAD Disassembly Network Modeling for End-of-Life Component Recovery
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Solution Overview
Problem
Existing assembly designs hinder the efficient recovery of reusable components at end-of-life due to component degradation, making disassembly costly and difficult.
Innovation Solution
A machine learning-based method that generates multilayer direct disassembly networks (MDDNs) using CAD assembly files, enhanced with degradation data and neural networks to optimize disassembly sequences and costs, supported by user feedback and CAD integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional assembly designs are used, then manufacturing and assembly are simplified, but disassembly becomes costly and difficult due to component degradation
Solution Approach 1:
The system performs preliminary analysis of component degradation patterns and disassembly requirements during the design phase. By using machine learning models trained on degradation data, the system predicts future disassembly challenges and proactively optimizes assembly designs to facilitate easier disassembly while maintaining manufacturing simplicity
Solution Approach 2:
The system changes design parameters such as fastening types, connection methods, and component configurations based on predicted degradation patterns. By adjusting these parameters according to ML model recommendations, the assembly maintains ease of manufacture while improving future disassembly ease despite component degradation
2Reliability
If complex disassembly procedures are required, then complete disassembly can be achieved, but disassembly cost and time increase significantly
Solution Approach 1:
The system segments the disassembly process into priority-based sequences, identifying which components should be removed first based on degradation patterns and recovery value. This segmentation enables efficient disassembly that achieves complete recovery while minimizing time and cost by focusing on high-priority components first
Solution Approach 2:
The system performs preliminary disassembly planning using machine learning models to predict optimal disassembly sequences before actual disassembly occurs. This advance planning identifies the most efficient path to complete disassembly, reducing both time and cost while ensuring completeness
3Duration of action of stationary object
If component degradation is allowed over time, then product use-life is extended, but disassembly difficulty increases
Solution Approach 1:
The system performs preliminary analysis of degradation patterns and their impact on disassembly complexity at the design stage. By using machine learning models to predict future states, the system proactively designs assemblies that will remain easier to disassemble even as components degrade over extended use-life periods
Solution Approach 2:
The system adjusts design parameters such as material selections, connection methods, and component configurations based on predicted degradation patterns. These parameter changes are optimized to maintain acceptable disassembly complexity levels even after extended operational periods when component degradation occurs
Data Source
AI summary
A method for training a machine learning model using enhanced multilayer direct disassembly networks (MDDNs). The method includes converting MDDNs into knowledge graphs and using them to train a generative and a discriminator component. The generative component produces synthetic disassembly structures, which are evaluated by the discriminator. Feedback from the discriminator is used to refine the generative model, improving the accuracy of disassembly network modeling.