Additive Manufacturing Defect Prediction Process Map
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Solution Overview
Problem
Current additive manufacturing techniques require repetitive and costly processes for defect inspection and optimization, involving time-consuming manual adjustments and extensive simulations, which are often impractical for general users due to specialized knowledge and resource requirements.
Innovation Solution
A method and system for predicting defects based on design and manufacturing data, collecting defect detection data during manufacturing, and generating process maps to optimize manufacturing conditions, allowing for automated adjustments and reduced time and cost in defect detection and repair.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If defect inspection is performed after manufacturing by manual classification and plotting, then manufacturing conditions can be established, but time and cost are significantly consumed
Solution Approach 1:
The system performs preliminary defect prediction using AI/ML models before actual manufacturing is completed. By analyzing design data and manufacturing conditions in advance, the system predicts potential defects and generates process maps that guide manufacturing parameter selection, eliminating the need for time-consuming post-manufacturing defect inspection and manual classification.
Solution Approach 2:
The invention replaces manual mechanical classification and plotting processes with automated AI/ML-based defect prediction systems. The system automatically analyzes manufacturing data, predicts defects, and generates process maps without human intervention, substituting manual operations with intelligent automated systems that significantly reduce time consumption.
2Manufacturing precision
If extensive simulations are executed to optimize manufacturing conditions, then manufacturing precision can be improved, but computational resources and time are greatly consumed
Solution Approach 1:
Instead of executing extensive physical simulations, the system creates a virtual model (process map) that copies and represents the complex manufacturing behavior. The process map is generated once using comprehensive data analysis and then reused for optimizing manufacturing conditions, eliminating the need to repeatedly execute time-consuming simulations while maintaining optimization accuracy.
Solution Approach 2:
The system performs comprehensive analysis and generates the process map in advance before actual manufacturing optimization is needed. This preliminary action captures the complex relationships between manufacturing parameters and outcomes, allowing subsequent optimizations to be performed quickly by referencing the pre-generated process map rather than executing new simulations.
3Reliability
If actual manufacturing is performed to verify simulations, then manufacturing conditions can be validated, but time and cost are significantly increased
Solution Approach 1:
The system incorporates feedback from defect detection data collected during actual manufacturing into the AI/ML models. By continuously learning from real manufacturing outcomes, the system validates and refines its predictions without requiring separate verification manufacturing runs. The feedback loop ensures reliability while minimizing the need for additional time-consuming validation experiments.
Solution Approach 2:
The process map serves as a virtual copy that represents validated manufacturing conditions. Once the process map is generated using initial manufacturing data, it can be reused and referenced for validation purposes without requiring repeated actual manufacturing runs, significantly reducing the time and cost of verification while maintaining reliability.
4Measurement precision
If specialized knowledge and extensive resources are allocated for defect optimization, then defect detection accuracy can be improved, but device complexity and cost increase
Solution Approach 1:
The system performs defect prediction and process map generation autonomously using AI/ML algorithms without requiring specialized human knowledge or extensive manual resources. The automated system collects manufacturing data, analyzes it using trained models, and generates optimization recommendations independently, eliminating the need for expert intervention while maintaining high defect detection accuracy.
Solution Approach 2:
The invention replaces specialized human expertise and manual defect optimization processes with automated AI/ML-based systems. The intelligent algorithms automatically analyze manufacturing data, predict defects, and generate process maps without human intervention, substituting complex manual operations with simpler automated systems that reduce both device complexity and resource requirements while maintaining or improving accuracy.
Data Source
AI summary
An additive manufacturing development method includes predicting a defect that occurs in a product based on a combination of a plurality of design data and a plurality of manufacturing conditions, collecting defect detection data for defect detection by monitoring the product during manufacturing in accordance with the combination of the plurality of design data and the plurality of manufacturing conditions, and generating a process map in which the plurality of manufacturing conditions are plotted using the predicted defect and the collected defect detection data. The method further includes collecting defect repair data for defect repair by monitoring the product during manufacturing and repairing a defect detected from the product, and storing the defect and the defect repair data in association with each other using the defect repair data and a repair result.