Thermal Profile Prediction for Additive Manufacturing Material Properties
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Solution Overview
Problem
Additive manufacturing techniques face challenges in predicting material properties, especially in parts with complex geometries, due to variations in thermal characteristics during the fabrication process, which can lead to inconsistent material properties.
Innovation Solution
A system and method that involves collecting thermal data from standard parts using infrared cameras, pyrometers, and thermocouple sensors, and using machine-learning algorithms to predict material properties of future parts based on stored thermal profiles and corresponding material properties, allowing for the determination of yield strength, tensile strength, and elongation strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additive manufacturing is used to fabricate parts with complex geometry, then manufacturing flexibility and design freedom are improved, but material property consistency deteriorates due to thermal variations during fabrication
Solution Approach 1:
The system performs preliminary thermal monitoring during the additive manufacturing process to capture thermal profiles before final material properties are established. By measuring thermal characteristics during fabrication and storing them for later analysis, the system enables prediction of material properties before physical testing, allowing for process optimization and consistency improvement.
Solution Approach 2:
The invention replaces traditional mechanical property testing (physical sampling and laboratory testing) with a non-contact optical measurement system using infrared cameras and pyrometers. This substitution enables thermal profile measurement during manufacturing without disrupting the additive manufacturing process, and allows for prediction of material properties through machine learning algorithms trained on thermal data.
2Measurement precision
If traditional physical sampling and testing methods are used to determine material properties, then measurement accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system creates a thermal signature copy or fingerprint of each additive manufactured part by capturing its thermal profile during fabrication. This thermal copy serves as a unique identifier and predictor of material properties, replacing the need for time-consuming physical sampling and laboratory testing. The thermal signature can be stored and referenced for quality assurance without requiring actual material extraction and testing.
Solution Approach 2:
The invention introduces thermal profiles as an intermediary between the manufacturing process and material property assessment. Instead of directly measuring mechanical properties through destructive testing, the system uses thermal characteristics during fabrication as a mediator to predict final material properties. This intermediary approach enables indirect but accurate assessment of material quality without the time and cost of traditional testing methods.
3Reliability
If thermal monitoring is performed during additive manufacturing, then material property prediction capability is improved, but system complexity increases
Solution Approach 1:
The system employs universal measurement devices (infrared cameras and pyrometers) that can monitor thermal characteristics across different additive manufacturing processes and part geometries. These multi-functional devices capture thermal profiles that serve multiple purposes: process monitoring, material property prediction, and quality assurance. The thermal monitoring system is designed to be adaptable to various manufacturing scenarios without requiring process-specific customization, thereby managing complexity while maintaining prediction accuracy.
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 accurate prediction of material properties, improving quality assessment and allowing for virtual design and testing, thereby reducing costs and iteration in the design-build-test cycle, particularly beneficial for complex geometries.
Implementation Method 1
one or more infrared cameras for collecting thermal data associated with additive-manufactured standard parts
Implementation Method 2
obtaining thermal profiles at select predetermined locations of physical samples of the plurality of standard parts during additive-manufacturing
Data Source
Figure 1
Figure 2A~2B
Figure 3A
AI summary
A method is provided for predicting material properties of a part to be additive-manufactured. The method comprises additive-manufacturing (610) a plurality of standard parts, and obtaining (620) thermal profiles at select predetermined locations of physical samples of the plurality of standard parts during additive-manufacturing of the plurality of standard parts. The method also comprises storing (630) the thermal profiles and corresponding material properties of the physical samples of the plurality of standard parts in a database. The method further comprises running (640) a machine-learning algorithm to predict material properties of the part to be additive-manufactured based upon the thermal profiles and corresponding material properties of the physical samples of the plurality of standard parts stored in the database.