Optimization of X-Ray CT Measurement Accuracy for Metal AM Components

The in situ inspection technology for additive manufacturing combines different types of cameras strategically placed around the part to monitor its properties during construction. The IR cameras collect accurate temperature data to validate thermal math models, while the visual cameras obtain highly detailed data at the exact location of the laser to build accurate, as-built geometric models. Furthermore, certain adopted techniques (e.g., single to grouped pixels comparison to avoid bad/biased pixels) reduce false positive readings. NASA has developed and tested prototypes in both laser-sintered plastic and metal processes. The technology detected errors due to stray powder sparking and material layer lifts. Furthermore, the technology has the potential to detect anomalies in the property profile that are caused by errors due to stress, power density issues, incomplete melting, voids, incomplete fill, and layer lift-up. Three-dimensional models of the printed parts were reconstructed using only the collected data, which demonstrates the success and potential of the technology to provide a deeper understanding of the laser-metal interactions. By monitoring the print, layer by layer, in real-time, users can pause the process and make corrections to the build as needed, reducing material, energy, and time wasted in nonconforming parts.

Modeling additive manufacturing processes can be difficult due to the scale difference between the active processing point (e.g., a sub-millimeter melt pool) and the part itself. Typically, the tools used to model these processes are either too computationally intensive (due to high physical fidelity or inefficient computations) or are focused solely on either the microscale (e.g., microstructure) or macroscale (e.g., cracks). These pitfalls make the tools unsuitable for fast and efficient evaluations of additive manufacturing build files and parts. Failures in parts made by laser powder bed fusion (L-PBF) often come when there is a lack of fusion or overheating of the metal powder that causes areas of high porosity. AM-PM uses a point field-based method to model L-PBF process conditions from either the build instructions (pre-build) or in situ measurements (during the build). The AM-PM modeling technique has been tested in several builds including a Ti-6Al-4V test article that was divided into 16 parts, each with different build conditions. With AM-PM, calculations are performed faster than similar methods and the technique can be generalized to other additive manufacturing processes. The AM-PM method is at technology readiness level (TRL) 6 (system/subsystem model or prototype demonstration in a relevant environment) and is available for patent licensing.

Additive manufacturing or 3-D printing is a rapidly growing field where solid, objects can be produced layer by layer. This technology will have a significant impact in many areas including industrial manufacturing, medical, architecture, aerospace, and automotive. The advantages of additive manufacturing are reduction in material costs due to near net shape part builds, minimal machining required, computer assisted builds for rapid prototyping, and mass production capability. Traditional thermal nondestructive evaluation (NDE) techniques typically use a stationary heat source such as flash or quartz lamp heating to induce a temperature rise. The defects such as cracks, delamination damage, or voids block the heat flow and therefore cause a change in the transient heat flow response. There are drawbacks to these methods.

NASA’s PLED thermal inspection system consists of an array of high- powered LED chips configured to deliver controlled pulses of visible light. The system includes 8 LED chip arrays, mounted on an aluminum heat sink and housed in a hood configuration. The inspection hood is specially designed with filters to prevent internal reflections. The LEDs are powered by regulated power supplies and controlled via a computer interface that synchronizes heat pulses with an infrared camera. An acrylic filter is placed over the LEDs to block residual infrared radiation, ensuring that only visible light reaches the target surface. The system’s infrared camera, operating in the mid-wave infrared (MWIR) range does not detect the visible light and captures the transient thermal response of the material, allowing for precise defect detection. By eliminating the need for high-intensity broadband infrared sources, the PLED system provides a cleaner and more accurate thermal response, particularly for unpainted metals and additively manufactured (AM) components. Performance validation of the PLED system has demonstrated significant advantages over traditional flash thermography. In tests on aluminum samples with material loss and AM Ti-6Al-4V metal specimens, the PLED system successfully detected defects with superior contrast and no heat source reflections. Principal Component Analysis (PCA) applied to PLED inspection data revealed clearer defect indications compared to flash-based methods, which introduced unwanted artifacts due to transient reflections. Additionally, the PLED system enabled quantitative thermal diffusivity measurements, offering a new approach to single-sided material characterization. NASA's PLED thermal inspection technology is available for patent licensing. Potential applications include corrosion detection in aerospace components, quality control of AM metal parts, structural health monitoring of industrial materials, and more.

NASAs software technology uses an Image Quality Indicator (IQI)-based model that can predict whether cracks of a certain size can be detected, as well as a model that can provide appropriate conditions to optimize x-ray crack detection setup. Because this modeling software can predict minimum crack sizes that can be detected by a particular X-ray radiography testing setup, users can test various setups until the desired crack detection capabilities are achieved (predicted) by the modeling system. These flaw size parameter models use a set of measured inputs, including thickness sensitivity, detector modulation transfer function, detector signal response function, and other setup geometry parameters, to predict the minimum crack sizes detectable by the testing setup and X-ray angle limits for detecting such flaws. Current X-ray methods provide adequate control for detection of volumetric flaws but do not provide a high probability of detection (POD), and crack detection sensitivity cannot be verified for reliable detection. This results in reduced confidence in terms of crack detection. Given that these cracks, if undetected, can cause catastrophic failure in various systems (e.g., pressure vessels, etc.), verifying that X-ray radiography systems used for NDE can detect such cracks is of the utmost importance in many applications.