Search

This work identifies sources of error in melt pool characterization tests that are frequently employed to study processing parameters in laser powder bed fusion (L-PBF) additive manufacturing (AM). At an early stage in process parameter development, researchers typically characterize the shape of single, non-overlapping melt tracks formed with varying processing conditions such as laser power and scanning velocity. However, as L-PBF enters mainstream manufacturing, researchers must test the limits of these commonly relied-upon characterization experiments to avoid errors that could impact process development. Hence, this study expands on conventional methods by depositing single-tracks and overlapping multitracks on a powder layer at multiple interpass temperatures, thereby mimicking heat buildup during the manufacturing of a multilayer part. While single-track melt pool depth and width do not increase as a function of interpass temperature as expected, multitrack experiments demonstrate a clear gradient in both melt pool depth, width and bead height between the first and final tracks. This work quantitatively demonstrates that the setup of single-track characterization experiments has a notable impact on the process parameter development. Alternatively, multitrack experiments closely capture the deposition conditions and melt pool variability observed during multilayer part fabrication.

Melt pool contours are an important empirical process measurement for metallic additive manufacturing. Melt pool boundaries indicate the maximal extent of the melting temperature contour. Melt pool boundaries characterize the effects of process parameters on melt pool size and variability and provide calibration and validation data for process simulations predicting thermal fields and microstructure. Serial cross sectioning of samples can produce numerous micrographs valuable for building robust melt pool contour datasets. However, the acquisition of contours from micrographs by hand is error prone and prohibitively time consuming for datasets large enough to capture variation within individual scan tracks and across a range of process conditions. In this work, we propose a semantic segmentation algorithm for automating melt pool contour acquisition from micrographs. The proposed algorithm is demonstrated on datasets from two different single scan track experiments conducted on a Powder Bed Fusion-Laser Beam platform using a bare Ti-6Al-4 V plate and an Inconel 718 plate with a 40 μm powder layer at different preheat temperatures. The algorithm was successfully trained and predicted the melt pool boundaries in the 300 image Ti-6Al-4 V dataset with 95 % accuracy and an estimated reduction of 91 % in processing time compared to data acquisition by hand. For the 1201 image Inconel 718 dataset, the algorithm achieved 89 % accuracy and an estimated 99 % time savings compared to hand measurements. The results for both datasets are demonstrated with a discussion about observed variability in the melt pool characteristics. • Semantic segmentation automates acquisition of melt pool contours produced by Powder Bed Fusion-Laser Beam from micrographs. • Datasets from 1501 Ti-6Al-4 V and Inconel 718 images are provided for melt pools at different preheat and process conditions. • The algorithm predicted melt pool boundaries with 95% accuracy (Ti-6Al-4 V dataset) and 89% accuracy (Inconel 718 dataset). [ABSTRACT FROM AUTHOR]

The aim of this manuscript is to give a compact overview of the results that illustrate the applicability of processing-structure-property relationships in the increasingly important context of 3D printing of metals. A process qualification approach based on the physics-based understanding of defect formation in laser powder bed fusion (L -PBF) additive manufacturing (AM) is investigated for an aerospace-grade titanium alloy (Ti-6Al-4V). A physically interpretable qualification approach is critical for enabling L -PBF part certification for structure-critical applications. This approach relies on systematic experimentation, characterization, testing, and data analysis tasks including design of experiments varying power and velocity to generate varying defect populations, process window development based on defect structure, high throughput fatigue testing, and fractography, 2D porosity characterization, and use of extreme value statistics to develop a porosity metric that, in turn, could have predictive power for the variation in fatigue performance. Results from four-point bend fatigue tests demonstrate that a process window can be defined based on this key mechanical property. This relatively high throughput approach can, in turn, support a reduced set of round bar fatigue tests typically used for qualification. Overall, the proposed ecosystem for process qualification of L -PBF AM shows promise and is expected to apply to other materials and powder bed fusion AM technologies. • A delineated process window for L-PBF Ti-6Al-4V was identified for both porosity and fatigue in power and velocity space. • There is a distinct increase in porosity as one moves into the keyhole range or towards lack -of-fusion. • Extreme value analysis was used for quantifying the largest pore size. • The fatigue life measured in 4-point bend fatigue decreases as porosity increases and is maximal within the process window. • 4-point bend fatigue is found to be practical for screening printed material quality. [ABSTRACT FROM AUTHOR]