Additive Manufacturing Thermal Monitoring via Predicted Image Comparison
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Solution Overview
Problem
Current additive manufacturing technologies face challenges in predicting and monitoring transient thermal behavior, which affects the quality of 3D printed parts and the health of manufacturing devices, leading to potential printing quality issues and machine state drift.
Innovation Solution
Implementing a neural network-based system that calculates predicted thermal images using contone maps and captured thermal images to determine a risk score, indicating potential issues in part quality and machine performance, enabling real-time monitoring and health assessment of additive manufacturing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional thermal monitoring methods are used, then device complexity is reduced, but manufacturing precision and reliability deteriorate due to inability to predict transient thermal behavior
Solution Approach 1:
The patent creates a virtual thermal image that replicates the expected thermal behavior of the additive manufacturing process. This virtual copy is generated through simulation and compared against actual thermal images to detect deviations. The copying principle allows indirect monitoring of thermal behavior without requiring direct measurement of all thermal parameters, thus improving precision while managing system complexity.
Solution Approach 2:
The system performs preliminary thermal simulation and prediction before actual manufacturing occurs. By pre-calculating expected thermal behavior and creating a virtual thermal image in advance, the system establishes a baseline for comparison. This preliminary action enables proactive detection of potential quality issues before they manifest in the actual part, improving manufacturing precision without requiring complex real-time intervention systems.
2Reliability
If real-time thermal monitoring is implemented, then reliability improves through early detection of issues, but loss of time increases due to processing and analysis requirements
Solution Approach 1:
The system implements continuous feedback by comparing actual thermal images against the virtual thermal image in real-time. This feedback loop enables immediate detection of deviations from expected thermal behavior, allowing for timely intervention to prevent quality issues. The feedback mechanism is optimized to provide reliable monitoring while maintaining efficient processing speeds through targeted comparison of critical thermal parameters.
Solution Approach 2:
The patent replaces complex mechanical thermal measurement systems with computational thermal simulation and image processing. Instead of using numerous physical sensors and complex mechanical monitoring apparatus, the system uses software-based thermal modeling and digital image analysis. This substitution reduces physical system complexity and processing time while maintaining or improving reliability through advanced computational methods.
3Manufacturing precision
If detailed thermal analysis is performed, then manufacturing precision improves, but use of energy increases due to computational requirements
Solution Approach 1:
The system applies local quality by focusing thermal analysis on critical regions and parameters rather than performing uniform detailed analysis across the entire build volume. The virtual thermal image and comparison algorithms concentrate computational resources on areas most likely to affect part quality, such as regions with complex geometry, material transitions, or high thermal gradients. This localized approach maintains manufacturing precision while reducing overall energy consumption.
Solution Approach 2:
The patent dynamically adjusts analysis parameters based on process conditions, part geometry, and criticality. Rather than maintaining constant high-resolution thermal analysis throughout manufacturing, the system modifies analysis depth, resolution, and frequency according to real-time conditions. This adaptive parameter adjustment enables precise thermal monitoring where needed while reducing computational energy consumption during less critical phases of the manufacturing process.
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 allows for effective online monitoring of additive manufacturing processes, enabling timely intervention to prevent printing quality issues and machine state drift, thereby ensuring consistent part quality and optimal device performance.
Implementation Method 1
a thermal image sensor or sensors 106...obtain, for the layer, a captured thermal image
Data Source
AI summary
Examples of methods for monitoring additive manufacturing by an electronic device are described herein. In some examples, a predicted thermal image is calculated for a layer. In some examples, a captured thermal image is obtained for the layer. In some examples, a risk score is calculated for the layer based on the predicted thermal image and the captured thermal image. In some examples, a mitigation operation is performed in response to determining that the risk score is outside of a threshold range.