Multi-Laser Powder Bed Fusion Defect Metric for Process Optimization
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Solution Overview
Problem
There is no known method to predict defect formation and dependency to process parameters in multi-laser powder bed fusion additive manufacturing, which can produce defects such as lack of fusion and keyhole porosity, complicating the production of high-quality components.
Innovation Solution
A system and process are developed to determine a scalar metric for predicting defects in multi-laser powder bed fusion additive manufacturing, using a defect model to optimize the manufacturing process by employing a single number as an objective or constraint, and interfacing with an external optimization framework to minimize defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-laser additive manufacturing is used to increase production rate and allowable part size, then productivity is improved, but the likelihood of defect formation increases due to multiple laser interactions
Solution Approach 1:
The patent applies preliminary action by predicting defect formation before it occurs during the additive manufacturing process. A predictive model uses process parameters (laser power, scan speed, hatch spacing) to forecast potential defects such as lack of fusion and keyhole porosity, allowing preventive measures to be taken during the manufacturing process itself rather than relying on post-build inspection only.
Solution Approach 2:
The patent implements feedback through a quality metric that provides real-time information about predicted defect density. This metric feeds back into the manufacturing process control, enabling dynamic adjustment of process parameters to maintain material quality while preserving high productivity benefits of multi-laser manufacturing.
2Productivity
If the number of lasers acting simultaneously is increased to improve production rate, then productivity is improved, but the complexity of predicting and controlling defect formation increases
Solution Approach 1:
The patent applies segmentation by dividing the complex multi-laser interaction problem into manageable components. The predictive model separately evaluates different defect types (lack of fusion, keyhole porosity, overlapping/undercutting) and their dependencies on specific process parameters, making the overall complex system more analyzable and controllable through structured parameter optimization.
3Manufacturing precision
If iterative trial and error methods are used to adjust parameters for quality, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by using a predictive model to determine optimal process parameters before actual manufacturing begins. The model calculates expected defect density and quality metrics in advance, eliminating the need for time-consuming iterative trial and error during production while maintaining high manufacturing precision.
Solution Approach 2:
The patent uses a virtual copy of the manufacturing process in the form of a predictive model that simulates part formation and defect development. This digital twin allows parameter optimization to be tested and validated before physical manufacturing, replacing time-consuming physical trial and error with faster computational analysis.
Data Source
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AI summary
A process for an external optimization framework utilizing a defect model for multi-laser additive manufacturing of a part including determining a scalar metric for the part; employing the scalar metric in the defect model; providing at least one output from the defect model to the external optimization framework; and optimizing the powder bed fusion additive manufacturing process for the part with the external optimization framework.