Additive Manufacturing Melt Pool Feature Extraction
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Solution Overview
Problem
Conventional additive manufacturing (AM) tools lack an online tuning mechanism to adjust process parameters in real-time, leading to variable production quality due to factors like power distribution and moisture content, which affects the quality of end products.
Innovation Solution
An AM feature extraction method that involves temperature measurement and photography of melt pools, converting images into gray level co-occurrence matrices to calculate homogeneity indices and extract melt-pool features, allowing for prediction of virtual metrology values and adjustment of process parameters using a prediction algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional AM tools use fixed process parameters for production, then the manufacturing process is simple and stable, but the production quality varies due to process variations such as power distribution, flow control, and moisture content
Solution Approach 1:
The patent implements real-time feedback by capturing images of the powder bed and melt pools during additive manufacturing, processing these images to extract features, and using the extracted features to dynamically adjust process parameters. This closed-loop feedback system enables quality control while maintaining manageable system complexity through automated image processing algorithms.
Solution Approach 2:
The patent replaces traditional mechanical measurement and quality inspection systems with an optical-based image processing system. By using cameras to capture images and computational algorithms to analyze melt pool characteristics, the system achieves real-time quality monitoring without complex mechanical measurement devices.
2Manufacturing precision
If quality measurements are performed only after product completion, then the measurement process is simple, but poor processing quality of intermediate layers cannot be detected in time to affect end product quality
Solution Approach 1:
The patent performs preliminary quality assessment by capturing and analyzing images of powder layers and melt pools during the manufacturing process. This allows detection of quality issues in intermediate layers before they propagate to the final product, enabling real-time intervention without delaying the overall production timeline.
Solution Approach 2:
The patent implements continuous quality monitoring by systematically capturing images at multiple stages (powder bed, melt pool formation, layer completion) throughout the additive manufacturing process. This continuous observation ensures that quality issues are detected in real-time rather than at discrete intervals, maintaining constant oversight without interrupting production.
3Manufacturing precision
If real-time temperature measurement and image processing are performed on melt pools, then product quality can be monitored in time, but the device complexity and computational requirements increase
Solution Approach 1:
The patent introduces an image processing system as an intermediary between the physical manufacturing process and quality assessment. By capturing optical images of the melt pool and powder bed, and using computational algorithms to extract relevant features, the system translates complex physical phenomena into analyzable data without requiring direct complex measurement devices in the manufacturing chamber.
Solution Approach 2:
The patent creates optical copies (images) of the physical melt pool and powder bed states. Instead of directly measuring complex physical parameters like temperature and fluid dynamics, the system captures visual representations and processes these copies computationally, simplifying the measurement approach while maintaining real-time monitoring capability.
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 real-time quality assessment and adjustment of process parameters, improving the yield and quality of end products by predicting potential issues before completion.
Implementation Method 1
directing an energy beam to powder bodies on each of the powder layers sequentially after the each of the powder layers is placed on the powder bed to melt powder bodies to form melt pools
Implementation Method 2
a temperature measurement is performed on each of melt pools formed on each of powder layers stacked on a powder bed during a fabrication of a workpiece product, thereby obtaining a temperature of each of the melt pools of the workpiece product
Data Source
AI summary
An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed; performing photography on each of the melt pools on the powder bed; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; first converting each of the workspace images to a gray level co-occurrence matrix (GLCM); building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.