Additive Manufacturing Defect Prediction Using Weld Image Analysis
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Solution Overview
Problem
Additive manufacturing systems face challenges in efficiently detecting and predicting part defects during the printing process, leading to material waste and increased costs due to manual inspection and the burden of existing weld quality monitoring systems.
Innovation Solution
An additive manufacturing system equipped with a photosensitive detector and processor that images the build surface, subdivides the image into regions, and uses light intensity analysis to identify weld defects, providing data for trained statistical models to predict part defects, thereby automating the defect detection and prediction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection and existing weld quality monitoring systems are used to detect part defects, then defect detection capability is provided, but material waste increases and costs increase due to inability to detect defects early
Solution Approach 1:
The system performs preliminary defect detection during the additive manufacturing process by analyzing weld characteristics in real-time, enabling early identification of defects before the printing process completes. This allows for process cancellation or adjustment when defects are detected, preventing waste of remaining material and reducing overall material loss.
2Manufacturing precision
If comprehensive weld quality monitoring is implemented to improve defect detection, then manufacturing quality improves, but device complexity increases due to additional monitoring systems
Solution Approach 1:
The system uses the existing laser and optics assembly required for the additive manufacturing process itself to generate and analyze light for weld defect detection. By making the manufacturing system multi-functional (both manufacturing and inspection), additional dedicated monitoring hardware is avoided, thereby improving manufacturing quality without proportionally increasing device complexity.
Solution Approach 2:
The system uses its own laser and optical components to perform self-inspection of the weld quality during the manufacturing process. The same laser that creates the weld also provides the light source for detecting weld defects, eliminating the need for separate external monitoring systems and reducing overall system complexity.
3Productivity
If real-time defect detection and prediction is implemented, then productivity improves through early defect identification, but device complexity increases due to advanced imaging and analysis systems
Solution Approach 1:
The photosensitive detector serves dual functions: capturing images of the build surface for manufacturing guidance and detecting weld defects for quality control. This multi-functionality enables real-time defect detection and productivity improvement without requiring separate dedicated inspection hardware, thereby limiting the increase in device complexity.
4Productivity
If automated defect detection systems are used to reduce manual inspection, then productivity improves, but device complexity increases due to automation infrastructure
Solution Approach 1:
The system automates defect detection by having the manufacturing process itself generate the inspection signals. The laser creates welds and simultaneously provides the light source for defect detection, while the photosensitive detector captures images for both manufacturing control and quality inspection. This self-service automation eliminates manual inspection without requiring complex external automation infrastructure.
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 approach enables early detection and prediction of part defects, allowing for process adjustments or cancellation, reducing material waste and costs by automating the inspection process and improving efficiency in quality control.
Implementation Method 1
one or more laser energy sources, an optics assembly configured to direct laser energy from the one or more laser energy sources toward the build surface to melt at least a portion of a layer of material
Implementation Method 2
direct laser energy from the one or more laser energy sources toward the build surface to melt at least a portion of a layer of material disposed on the build surface to form one or more welds
Implementation Method 3
one or more light sources configured to illuminate the build surface. The one or more light sources are configured to emit light in a direction that is at least partially parallel to the build surface
Implementation Method 4
a photosensitive detector configured to image at least a portion of the build surface... identifying the presence of weld defects in the build surface based at least in part on light intensities of the plurality of regions
Data Source
AI summary
Systems and methods for predicting weld and/or part defects during additive manufacturing process are disclosed. Additionally, systems and methods related to controlling an additive manufacturing process using predicted weld and/or build defects are disclosed.