Breaking perceived limits of performance for nanoscale interrogation & transport systems

University of Minnesota Ph.D. dissertation. March 2015. Major: Electrical/Computer Engineering. Advisor: Murti Salapaka. 1 computer file (PDF); xviii, 136 pages.Scientific instruments for nano-interrogation, in particular optical field based probing instruments, typically do not leverage modern control paradigm, thereby constraining themselves to false limits of performance. The first part of my reserach is on developing a novel disturbance estimation paradigm built upon LMI based mixed objective synthesis, which is geared towards systems requiring regulation of a certain system variable against an external disturbance while simultaneously providing a real-time estimate of the disturbance. Examples of such systems include optical traps, scanning probe microscopy, microfluidic sensors, high density data storage systems etc. In general this disturbance is corrupted by process noise (which for nano-scale systems is primarily thermal noise) and the disturbance estimation scheme has to mitigate the effect of such noise to provide any meaningful estimate. In the particular context of the optical field based probing and manipulation, I have experimentally demonstrated more than an order of magnitude improvement in bandwidth over previous state-of-the-art using the aforementioned paradigm. This optimal force clamp will enable biologists to study motor proteins at in-vivo speeds which is not currently possible. The later part of my research is on control of Brownian ratchet based stochastic transport mechanisms where I have used physical insights to reduce the model complexity in order to analytically derive the approximate evolution of the probability density function of the system state. This allowed for obtaining design parameters for optimal performance, which was missing from the previous literature. I will also demonstrate the advantages of using dynamic programming based multi-objective optimization techniques to obtain transport strategies that strike an optimal velocity-efficiency trade-off. Here a key insight obtained is that maximizing velocity of transport can significantly compromise efficiency of transport; an aspect not realized/emphasized by researchers in the area. Extensive Monte Carlo simulations demonstrates up to $35\%$ increase in efficiency from other closed loop strategies and more importantly, being an optimal strategy, provides a benchmark of comparison for other heuristic strategies in the domain

In situ process quality monitoring and defect detection for direct metal laser melting

Quality control and quality assurance are challenges in Direct Metal Laser Melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that can be readily deployed on existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. A Bayesian approach attributes measurements to one of multiple process states and a least squares regression model predicts severity of certain material defects.Comment: 16 pages, 4 figure

In situ process quality monitoring and defect detection for direct metal laser melting

AbstractQuality control and quality assurance are challenges in direct metal laser melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that leverage existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. In one methodology, a Bayesian approach attributes measurements to one of multiple process states as a means of classifying process deviations. In a second approach, a least squares regression model predicts severity of certain material defects.</jats:p

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

AbstractThe oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract</jats:p