Grant: Discovery Grants Program - Individual

This proposal aims to address the knowledge gaps at the intersection of material science and data analytics by developing a rapid qualification framework for parts manufactured by Laser Beam Powder Be Fusion (LB-PBF) additive manufacturing (AM) technique. Due to the currently practiced time-consuming, expensive and inefficient qualification approaches, American National Standards Institute identified the lack of rapid qualification process for LB-PBF parts as a major standardization gap in this AM industry. By coupling multiscale simulations and characterization with data analytics and by structuring a mesoscale mechanical testing framework, this work will establish processing strategy-structure-performance (PSP) relationships for meso-scale building strategies and subsequently demonstrate a new qualification approach on the structural integrity for LB-PBF parts. The major outcomes of this proposed research program will be (1) establishing a rapid qualification framework of LB-PBF AM parts to design the process with desired fatigue performance, and (2) developing workforce with diverse, multidisciplinary technical skillsets for the Canadian AM industry. Successful execution of this project will have a transformative impact on the fabrication of AM alloy parts with a much-accelerated qualification cycle. It will make the technology commercially viable and propels the Canadian AM industry to a global leadership position. This research program will enable AM industry to save both cost and time to quickly qualify their parts. The goal of this proposed research is to develop a data-driven, non-destructive evaluation (NDE) framework that enables much-accelerated qualification of safety-critical, complex LB-PBF parts, saving time and cost. A new PSP database for a crucial collection of Discrete Building Strategy Sets (DBSS) will be generated to support the qualification process by combining experimentation, data analysis, and physics-based modeling. For each DBSS, this database will include: 1) location-specific process control parameters; 2) detailed microstructure maps, including volumetric distributions of pores/cracks collected via X-ray computed tomography (XCT) scans, 3D "slice and view" data cubes in combination with data analysis and reconstruction, and physics-based simulations linking defects to fatigue performance; and 3) mechanical testing in meso-, micro-, and nano-scale to link localized fatigue performance with scan strategies, machine parameters, and residual stresses. Data analytics will be utilized to evaluate the performance of components and provide guidance on processing strategies for making parts with improved performance, thus achieving rapid qualification. I believe this research will eventually be a national resource and the "go-to place" for AM qualification and my lab will become fully competitive by securing external funding from other agencies and industrial partners.