Technologietransferzentrum Main-Spessart (TTZ-MSP)
Laser powder bed fusion of metal has emerged as a key technology in additive manufacturing, enabling the production of intricate, high-performance metal components directly from digital designs. However, challenges such as dimensional inaccuracies and internal defects continue to hinder its broader industrial application. Addressing these limitations requires enhanced process monitoring and control strategies. This study introduces an innovative process monitoring system, designed to improve defect detection and process control. By employing a dual scan head configuration, enabling precise and independent path planning of the laser and the measurement field of an infrared camera, the Synchronized Path Infrared Thermography (SPIT) setup utilizes the principle of exploiting differences in cooling behavior to identify subsurface defects. Pre-printed samples with embedded cylindrical defects ranging from 300 to 1000 μm in diameter are used and an additional layer of powder is applied and fused within the experimental setup. The volumetric energy density and scanning speed are varied to analyze their influence on process monitoring reliability. The sensor scan head is synchronized with the laser scan head’s movements, while the infrared camera captures thermal radiation at 1904 fps. The results demonstrate the system’s capability to detect subsurface defects with a minimum size of 356 µm.
This contribution presents a new technique for active thermography in laser powder bed fusion of metals (PBF-LB/M), utilizing the Synchronized Path Infrared Thermography (SPIT) setup. The approach uses the processing laser to thermally excite the surface of a stainless steel specimen allowing for in-situ non destructive testing. A secondary galvanometer scanner, optimized for MWIR radiation, in combination with a high-speed thermography system, is capable of conducting detailed subsurface defect analysis. The tests are carried out with artificially induced defects of varying sizes, the method uses temperature gradient analysis and higher order derivatives of the temperature profile along the laser path to identify undesirable thermal behavior. The findings highlight the potential of SPIT as a non-destructive and efficient tool for enhancing PBF-LB/M process monitoring, with implications for improved manufacturing quality and safety.
This HDF5-dataset contains in-situ high-speed infrared thermography data acquired during the Laser-Based Powder Bed Fusion (PBF-LB/M) process. The data was collected using a Synchronized Path Infrared Thermography (SPIT) setup, which employs a dual-scanhead configuration to guide both the processing laser and the thermal camera's field of view.
The primary feature of this dataset is the application of a temporally gated acquisition strategy. The infrared camera's integration time (800 µs) was synchronized with a modulated processing laser (500 Hz) to capture thermal data exclusively during the laser-off phases. This method effectively isolates the material's thermal emission from high-intensity laser reflections.
Dual Scan head approach for in-situ defect detection in laser powder bed fusion of metals - Dataset
(2025)
This dataset contains thermographic data from a study on in-situ defect detection in the Laser Powder Bed Fusion of Metals (PBF-LB/M) process. The data was collected using a novel experimental setup named Synchronized Path Infrared Thermography (SPIT), which employs a dual scan head configuration. One scan head directs the processing laser, while the second scan head positions the measurement field of an infrared (IR) camera. This setup allows for the precise analysis of the cooling behavior of the material decoupled from the immediate laser-material interaction zone.
The experiments were conducted on pre-fabricated stainless steel (EOS StainlessSteel PH1, DIN 14540) samples with embedded, cylindrical subsurface defects of varying diameters. A single layer of metal powder was applied to these samples and then fused by the laser. The dataset includes a series of measurements where process parameters, specifically the volumetric energy density and the laser scanning speed, were systematically varied to assess their influence on defect detection reliability.
The provided data consists of raw thermographic recordings, which capture the surface temperature distribution in the heat-affected zone behind the melt pool. These recordings can be used to identify localized areas of elevated temperature caused by the insulating effect of the subsurface defects, which impede heat transfer into the substrate. This dataset is valuable for researchers working on process monitoring, defect detection algorithms, and the validation of thermal simulations in additive manufacturing.
Additive manufacturing (AM) has revolutionized production by offering design flexibility, reducing material waste, and enabling intricate geometries that are often unachievable with traditional methods. As the use of AM for metals continues to expand, it is crucial to ensure the quality and integrity of printed components. Defects can compromise the mechanical properties and performance of the final product. Non-destructive testing (NDT) techniques are necessary to detect and characterize anomalies during or post-manufacturing. Active thermography, a thermal imaging technique that uses an external energy source to induce temperature variations, has emerged as a promising tool in this field. This paper explores the potential of in-situ non-destructive testing using the processing laser of a PBF-LB/M setup as an excitation source for active thermography. With this technological approach, artificially generated internal defects underneath an intact surface can be detected down to a defect size of 350 μm – 450 μm.
