Integration of models that capture the complex physics of solidification on the macro and microstructural scale with the flexibility to consider multicomponent materials systems is a significant challenge in modeling additive manufacturing processes. This work aims to link process variables, such as energy density, with non-equilibrium solidification by integrating additive manufacturing process simulations with solidification models that consider thermodynamics and diffusion. Temperature histories are generated using a semi-analytic laser powder bed fusion process model and feed into a CALPHAD-based ICME (CALPHAD: Calculation of Phase Diagrams, ICME: Integrated Computational Materials Engineering) framework to model non-equilibrium solidification as a function of both composition and processing parameters. Solidification cracking susceptibility is modeled as a function of composition, cooling rate, and energy density in Al-Cu Alloys and stainless steel 316L (SS316L). Trends in solidification cracking susceptibility predicted by the model are validated by experimental solidification cracking measurements of Al-Cu alloys. Non-equilibrium solidification in additively manufactured SS316L is investigated to determine if this approach can be applied to commercial materials. Modeling results show a linear relationship between energy density and solidification cracking susceptibility in additively manufactured SS316L. This work shows that integration of process and microstructure models is essential for modeling solidification during additive manufacturing.

Portable and wearable medical instruments are poised to play an increasingly important role in health monitoring. Mobile spirometers are available commercially, and are used to monitor patients with advanced lung disease. However, these commercial monitors have a fixed product architecture determined by the manufacturer, and researchers cannot easily experiment with new configurations or add additional novel sensors over time. Spirometry combined with exhaled breath metabolite monitoring has the potential to transform healthcare and improve clinical management strategies. This research provides an updated design and benchmark testing for a flexible, portable, open access architecture to measure lung function, using common Arduino/Android microcontroller technologies. To demonstrate the feasibility and the proof-of-concept of this easily-adaptable platform technology, we had 43 subjects (healthy, and those with lung diseases) perform three spirometry maneuvers using our reconfigurable device and an office-based commercial spirometer. We found that our system compared favorably with the traditional spirometer, with high accuracy and agreement for forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), and gas measurements were feasible. This provides an adaptable/reconfigurable open access “personalized medicine” platform for researchers and patients, and new chemical sensors and other modular instrumentation can extend the flexibility of the device in the future.

Algorithmic classifiers are crucial components of clinical decision support (CDS) systems needed to advance healthcare delivery. Robust CDS systems must be derived and validated via creation of multi-reviewer adjudicated gold standard datasets. Manual annotation of physiologic data such as mechanical ventilator waveform data (VWD) can be time-consuming, and lacks methodological consistency in dataset development. To address these issues, we have created a system for annotating and adjudicating VWD called the Annotation PipeLine (APL) to optimize VWD annotation by expert reviewers. APL combines visual assessment of waveform characteristics with metadata display, enabling inclusion of quantitative thresholds into annotation decisions by reviewers. APL also includes specific features for resolving multi-reviewer disagreements and generating gold standard data sets. APL’s unique combination of methods and open source framework may accelerate the creation of CDS algorithms for ventilator management, and may serve as a model for future research into physiologic waveform annotation systems.