Normalized Thermal Sensor Data for Additive Manufacturing Defect Detection
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Solution Overview
Problem
Current additive manufacturing quality assurance methods are limited, particularly in non-destructive testing, as data from wide area thermal sensors can be biased by varying distances and scan lengths, making it difficult to reliably characterize the quality of parts produced.
Innovation Solution
The method involves monitoring a heat source with an optical temperature sensor across a powder bed, recording scan intensity and duration, generating characteristic curves, and comparing them to baseline curves to identify defects, while accounting for distance and scan length variations using additional sensors and data normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a wide area thermal sensor is used to monitor the additive manufacturing process, then the coverage area is increased, but the measurement precision deteriorates due to varying distances between the sensor and different portions of the build plane
Solution Approach 1:
The patent divides the build plane into multiple discrete regions, each with its own characteristic curve. By segmenting the monitoring area into smaller zones, the sensor can maintain relatively consistent measurement conditions within each zone, thereby improving measurement precision while preserving wide area coverage across all regions.
Solution Approach 2:
The patent transforms raw temperature data into normalized characteristic curves by changing the parameter representation. Instead of using absolute temperature values that vary with distance, the system uses normalized curves that account for distance variations, thereby maintaining measurement precision across the wide sensor coverage area.
2Adaptability or versatility
If the heat source scans across different portions of the powder bed with varying scan lengths, then the manufacturing flexibility is improved, but the measurement consistency deteriorates due to artificial bias in thermal data
Solution Approach 1:
The system records actual scan parameters (intensity and duration) during manufacturing and uses this feedback to generate region-specific characteristic curves. This feedback mechanism allows the system to adapt to varying scan lengths and patterns while maintaining measurement consistency by comparing actual thermal data against the appropriate baseline for each region.
Solution Approach 2:
The patent establishes baseline characteristic curves for each region before actual manufacturing begins. These pre-established baselines serve as reference standards that account for expected variations in scan parameters, enabling consistent measurement and defect detection even when scan lengths and patterns vary during production.
3Measurement precision
If conventional destructive testing is used to verify part quality, then the measurement precision is improved, but the productivity deteriorates due to part destruction and inability to apply to production parts
Solution Approach 1:
The patent replaces mechanical destructive testing with optical thermal monitoring during the manufacturing process. By using optical sensors to detect thermal signatures and compare them against characteristic curves, the system achieves quality verification without physically destroying the part, thereby maintaining both measurement precision and production throughput.
Solution Approach 2:
The system performs quality verification during the manufacturing process itself rather than after completion. By monitoring thermal data in real-time and comparing it against baseline characteristic curves, defects can be detected early, preventing waste of materials and maintaining high productivity while ensuring part quality.
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 non-destructive identification and characterization of defects in additive manufacturing, ensuring the quality of produced parts by correcting for systematic errors and providing a baseline for consistent production processes.
Implementation Method 1
monitoring a heat source scanning across a powder bed using an optical temperature sensor
Implementation Method 2
the heat source melts the incrementally added powder by welding regions of the powder layer creating a moving molten region
Implementation Method 3
the heat source melts the incrementally added powder by welding regions of the powder layer creating a moving molten region
Data Source
AI summary
The disclosed embodiments relate to the monitoring and control of additive manufacturing. In particular, a method is shown for removing errors inherent in thermal measurement equipment so that the presence of errors in a product build operation can be identified and acted upon with greater precision. Instead of monitoring a grid of discrete locations on the build plane with a temperature sensor, the intensity, duration and in some cases position of each scan is recorded in order to characterize one or more build operations.