Pyrometry-Based Porosity Detection in Additive Manufacturing
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Solution Overview
Problem
Current methods lack effective and efficient systems for identifying voids or pores in structures produced by additive manufacturing processes, particularly in laser powder bed fusion, which can weaken the material and reduce strength.
Innovation Solution
A system and method utilizing pyrometry data to generate temperature data, identify high temperature areas, and detect outlier shapes or ellipses within these areas, which correlate with voids in the structure, allowing for in-situ prediction and detection of porosity during the manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If micro-CT is used to identify voids in additively manufactured structures, then measurement precision is improved, but loss of time increases due to post-processing requirements
Solution Approach 1:
The system performs preliminary analysis during the additive manufacturing process itself by monitoring pyrometry data and identifying outlier melt pool characteristics in real-time. This preliminary detection eliminates the need for subsequent micro-CT scanning and post-processing, as the void locations are identified and mapped during manufacturing, directly resolving the time loss contradiction while maintaining measurement precision.
2Productivity
If pyrometry data is collected during additive manufacturing, then productivity is improved through real-time monitoring, but device complexity increases due to data processing requirements
Solution Approach 1:
The system extracts only the critical pyrometry data points related to melt pool characteristics (temperature, duration, shape parameters) from the overall manufacturing process data stream. By focusing exclusively on these relevant parameters and filtering out extraneous information, the system achieves real-time void detection without requiring complex processing of all possible manufacturing data, thus improving productivity while controlling device complexity.
Solution Approach 2:
The system creates simplified digital representations (copies) of the physical melt pool characteristics from pyrometry data. These digital models capture the essential features needed for void prediction without requiring the full complexity of the physical measurement system, enabling efficient real-time analysis while reducing the computational burden and overall system complexity.
3Measurement precision
If outlier detection algorithms are applied to identify voids, then measurement precision is improved, but difficulty of detecting and measuring increases due to algorithm complexity
Solution Approach 1:
The system transforms the complex multi-dimensional pyrometry data into a reduced set of critical parameters (melt pool temperature, duration, aspect ratio, and area) that directly correlate with void formation. By changing the parameter representation from raw sensor data to physically meaningful characteristics, the outlier detection algorithm becomes more tractable while maintaining or improving measurement precision through focused analysis of the most relevant features.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient identification of voids in additively manufactured structures, improving material strength by allowing for real-time monitoring and adjustment of the manufacturing process to reduce porosity.
Implementation Method 1
Pyrometry is the measurement of surface temperature by the characteristics of the radiation that is emitted from the surface. A pyrometer is a type of remote-sensing thermometer that is used to measure the temperature of a surface at a distance from the amount of thermal radiation emitted from the surface.
Implementation Method 2
Laser powder bed fusion is one example of such an additive manufacturing process in which metallic powders are melted by a laser and then solidify to create a three-dimensional object.
Data Source
AI summary
A method and apparatus for identifying porosity in a structure made by an additive manufacturing process in which a laser is scanned across layers of material to form the structure. Pyrometry data comprising images of the layers acquired during additive manufacturing of the structure is received. The pyrometry data is used to generate temperature data comprising estimated temperatures of points in the layers in the images of the layers. The temperature data is used to identify shapes fit to high temperature areas in the images of the layers. Conditions of the shapes fit to the high temperature areas in the images of the layers are identified. Outlier shapes are identified in the shapes fit to the high temperature areas in the images of the layers using the conditions of the shapes.