An Automated Fully-Computational Framework to Construct Printability Maps for Additively Manufactured Metal Alloys

In additive manufacturing, the optimal processing conditions need to be determined to fabricate porosity-free parts. For this purpose, the design space for an arbitrary alloy needs to be scoped and analyzed to identify the areas of defects for different laser power-scan speed combinations and can be visualized using a printability map. Constructing printability maps is typically a costly process due to the involvement of experiments, which restricts their application in high-throughput product design. To reduce the cost and effort of constructing printability maps, a fully computational framework is introduced in this work. The framework combines CALPHAD models and a reduced-order model to predict material properties. THen, an analytical thermal model, known as the Eagar-Tsai model, utilizes some of these materials' properties to calculate the melt pool geometry during the AM processes. In the end, printability maps are constructed using material properties, melt pool dimensions, and commonly used criteria for lack of fusion, balling, and keyholing defects. To validate the framework and its general application to laser powder-bed fusion alloys, five common additive manufacturing alloys are analyzed. Furthermore, NiTi-based alloys at three different compositions are evaluated to show the further extension of the framework to alloy systems at different compositions. The defect regions in these printability maps are validated with corresponding experimental observations to compare and benchmark the defect criteria and find the optimal criterion set with the maximum accuracy for each unique material composition. Furthermore, printability maps for NiTi that are obtained from our framework are used in conjunction with process maps resulting from a multi-model framework to guide the fabrication of defect-free additive manufactured parts with tailorable properties and performance.Comment: 18 Figures, 35 page

An Integrated Computational Framework for the Accelerated Development of Tailored Additively Manufactured Metals

Additive manufacturing (AM) is a disruptive technology leveraging innovations of the past and present to enable the design and fabrication of the new standard for components across industries. However, the successful application of the AM process to achieve desired results is in part made possible through the exploitation of inherent material properties and characteristics. Consequently, this process-structure-property-performance relationship must be understood and leveraged within AM to reliably and effectively continue development for improved performance through materials design. The improved performance and functionality of AM components thus necessitates a framework for accelerated materials design and development. For this purpose, a physics-based and data-driven integrated computational materials engineering (ICME) framework is developed, leveraging the utility and efficiency of simulations with experimentation to drive forward materials design and discovery. This is achieved by querying the complex AM PSPP relationships to inform and guide experiments for the cost-effective design of NiTi-based shape memory alloys (SMAs). In this regard, NiTi-based SMAs are prone to Ni loss under the conditions afforded by the AM process and are subject to a strong correlation between Ni content and transformation temperature (TT). Additionally, these materials suffer from difficulty in fabrication through standard manufacturing processes while exhibiting desirable functional properties. For this reason, the first study of this work takes a critical inspection on the vaporization of alloys during the welding and AM process. This is followed by a second study where an ICME framework consisting of a thermal model, a multi-layer model, and a differential evaporation model, is developed to screen for PSPP trends and inform experiments for the laser powder bed fusion (LPBF) AM of metal alloys. This framework is calibrated and validated against experiments for NiTi SMA, utilizing process parameters to predict Ni content and TT in agreement with experimental measurements and trends. The third study leverages optimization techniques alongside the ICME framework to solve the inverse design problem and predict design parameters required for desired component specifications. The fourth study expands the utility of the framework to the NiTiHf system where, after calibration and validation, model predictions for TT were found to be in good agreement with experiments. The fifth study provides a summary of the work and its contributions towards the accelerated development and design of LPBF AM metals, as well as an outlook on future work for expanded utility and application of the ICME framework

Additively Manufactured Carbon-Reinforced ABS Honeycomb Composite Structures and Property Prediction by Machine Learning

The expansive utility of polymeric 3D-printing technologies and demand for high- performance lightweight structures has prompted the emergence of various carbon-reinforced polymer composite filaments. However, detailed characterization of the processing–microstructure–property relationships of these materials is still required to realize their full potential. In this study, acrylonitrile butadiene styrene (ABS) and two carbon-reinforced ABS variants, with either carbon nanotubes (CNT) or 5 wt.% chopped carbon fiber (CF), were designed in a bio-inspired honeycomb geometry. These structures were manufactured by fused filament fabrication (FFF) and investigated across a range of layer thicknesses and hexagonal (hex) sizes. Microscopy of material cross-sections was conducted to evaluate the relationship between print parameters and porosity. Analyses determined a trend of reduced porosity with lower print-layer heights and hex sizes compared to larger print-layer heights and hex sizes. Mechanical properties were evaluated through compression testing, with ABS specimens achieving higher compressive yield strength, while CNT-ABS achieved higher ultimate compressive strength due to the reduction in porosity and subsequent strengthening. A trend of decreasing strength with increasing hex size across all materials was supported by the negative correlation between porosity and increasing print-layer height and hex size. We elucidated the potential of honeycomb ABS, CNT-ABS, and ABS-5wt.% CF polymer composites for novel 3D-printed structures. These studies were supported by the development of a predictive classification and regression supervised machine learning model with 0.92 accuracy and a 0.96 coefficient of determination to help inform and guide design for targeted performance