Optical Powder Impurity Detection for Faster AM Material Inspection
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Solution Overview
Problem
Current methods for detecting impurities in additive manufacturing materials rely on manual inspection by human operators, which are time-consuming and prone to underestimating the amount of foreign object debris, affecting the quality and mechanical properties of finished parts.
Innovation Solution
An automated system using a light source and camera to illuminate and capture image data of additive manufacturing material, processed by a computing device to accurately quantify impurities through computer vision techniques, including multiple wavelength illumination and image processing algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by human operators is used to detect impurities, then the process is simple to implement, but the detection accuracy is low and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated optical imaging system. A camera captures images of the powder sample, and image processing algorithms automatically identify and count impurities, eliminating the need for human operators to manually examine each sample under microscopes.
Solution Approach 2:
The patent creates a digital copy (image) of the physical powder sample. The camera captures an optical representation of the sample, and this digital image is then processed through algorithms that simulate and enhance the inspection process, allowing for more accurate and faster impurity detection than direct manual observation.
2Productivity
If manual inspection is used, then the equipment complexity is low, but the productivity is reduced
Solution Approach 1:
The imaging system serves multiple functions: it captures the visual appearance of the powder, enables automated impurity detection through image processing, and can potentially analyze other powder characteristics. This multi-functional approach increases productivity while the complexity is managed through integrated software solutions.
Solution Approach 2:
The patent transforms the inspection process from direct physical observation to digital image analysis. By changing the parameter of detection from human visual inspection to computational image processing, the system achieves higher productivity. The complexity is offset by using standard image processing techniques and algorithms that can be implemented on conventional computing hardware.
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 rapid and accurate detection of impurities, improving the quality and mechanical properties of additively manufactured parts by reducing human error and ensuring material suitability for further use.
Implementation Method 1
illuminating, by a light source, a sample of additive manufacturing material with light, while illuminating the sample of the additive manufacturing material with light, causing a camera to acquire image data of the sample
Data Source
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AI summary
An example method (200) for detection of impurities (130) in additive manufacturing material (106) includes illuminating (202), by a light source (102), a sample (104) of additive manufacturing material with light (107), while illuminating the sample of the additive manufacturing material with light, causing (204) a camera (108) to acquire image data (109) of the sample, and processing (206) the image data to determine an amount of impurities in the sample of the additive manufacturing material. An example system (100) for detection of impurities in additive manufacturing material includes a light source for illuminating a sample of additive manufacturing material with light, a camera for acquiring image data of the sample while illuminating the sample of the additive manufacturing material with light, and a computing device (110) having one or more processors (112) configured to execute instructions (114) stored in memory (116) for processing the image data to determine an amount of impurities in the sample of the additive manufacturing material.