Additive Manufacturing Defect Detection via Height Map Imaging
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Solution Overview
Problem
Existing defect detection methods for additively manufactured objects face challenges in efficiently processing large amounts of indirect measurement data, leading to complex calculations and difficulties in real-time detection of minute shape features, particularly in narrow portions where welding defects are likely to occur.
Innovation Solution
A defect detection method that involves detecting the height distribution of the surface shape of the additively manufactured object, converting this information into a two-dimensional height information image, and then identifying specific shape feature portions based on brightness value levels in the image, thereby determining the possibility of welding defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If coordinate information from light cutting method is used for defect detection, then three-dimensional shape measurement is achieved, but data amount becomes huge and storage capacity is required
Solution Approach 1:
The patent extracts only the essential shape information needed for defect detection from the complete three-dimensional coordinate data. By converting full 3D coordinate information into two-dimensional height distribution maps, the system extracts the critical dimensional data while discarding redundant spatial information, thereby reducing data storage requirements while maintaining measurement precision for defect detection
Solution Approach 2:
The patent transforms three-dimensional coordinate information into two-dimensional height distribution maps. This dimensionality reduction converts complex 3D spatial coordinates into simplified 2D representations where height values are mapped to pixel intensities, significantly reducing data quantity while preserving the essential shape features needed for detecting narrow portions and welding defects
2Measurement precision
If complete measurement data is processed for defect detection, then detection accuracy is improved, but calculation complexity increases and processing speed decreases
Solution Approach 1:
The patent extracts only the critical shape features necessary for defect detection from complete measurement data. By focusing on height distribution patterns in two-dimensional maps rather than processing all three-dimensional coordinate information, the system achieves accurate detection of narrow portions while significantly reducing calculation complexity and processing time
Solution Approach 2:
The patent applies partial action by processing only the essential height distribution information needed for defect detection rather than analyzing complete three-dimensional measurement data. This selective processing of critical shape features maintains detection accuracy for welding defects while avoiding the computational burden of processing redundant spatial data
3Reliability
If real-time defect detection is implemented during additive manufacturing, then welding defects are reduced, but processing speed must be increased
Solution Approach 1:
The patent extracts essential height distribution information from measurement data in real-time during additive manufacturing. By converting three-dimensional coordinates into two-dimensional height maps and focusing on critical shape features, the system achieves rapid processing that enables real-time defect detection while maintaining reliability for identifying narrow portions that may lead to welding defects
Solution Approach 2:
The patent implements partial processing by analyzing only the height distribution patterns necessary for defect detection rather than processing complete three-dimensional measurement data. This selective approach enables real-time processing during additive manufacturing, improving productivity while maintaining the reliability needed to detect welding defects and narrow portions
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
This method enables easy detection of specific shape feature portions with reduced data storage requirements, allowing for fast defect detection without omission, and facilitating real-time adjustments to manufacturing plans to minimize welding defects.
Implementation Method 1
an image of a bright line is captured when a bead is irradiated with a linear laser light, and a height of a bead surface is detected from a position of the bright line in the captured image
Data Source
AI summary
A defect detection method for detecting a welding defect that occurs in an additively manufactured object when the additively manufactured object is built by depositing beads formed by melting and solidifying a filler metal, the method includes: detecting a height distribution of a surface shape of the additively manufactured object during the building; representing information on the detected height distribution as a variable of a brightness value of each pixel of a two-dimensional image, and generating a height information image obtained by converting the information on the height distribution into information on a distribution of the brightness value; detecting a shape feature portion having a specific shape feature according to a level of the brightness value of the height information image; and determining a possibility that the detected shape feature portion becomes the welding defect.