Digital Twin Feed Drive Identification for Virtual Process Planning

Computer numerical controlled (CNC) machines have become an integral part of the manufacturing industry, allowing companies to increase the accuracy and productivity of their manufacturing lines. The next step to improving and accelerating the development process of a part is to involve virtual prototyping during the design phases. Virtual manufacturing has become an invaluable tool to process planners and engineers in recent years to model the manufacturing environment in a virtual setting to determine the final geometry and tolerances of new parts and processes. For a virtual twin of a CNC machine to be built, the dynamics of the drive and CNC controller must be identified. Traditionally, these identification techniques require several intrusive tests to be run on the machine tool, causing valuable time lost on production machines. In this thesis, three new techniques of developing virtual models of machine tools are discussed. The first model presented is a quasi-static model which is suitable for trajectory tracking error prediction. This technique is used to determine the contributions of the commanded velocity, acceleration, and jerk to the tracking errors of each axis of the machine tool. After determining these contributions, process planners can modify the axis feedrates in a virtual environment during trajectory optimization to find the best parameters for the shortest cycle time. This method was validated using a laser drilling machine tool from Pratt and Whitney Canada (P&WC) and was able to predict the root mean square (RMS) of the tracking error within 2.62 to 11.91 µm. A simple graphical user interface (GUI) was developed so that process planners and engineers can import data collected from the FANUC and Siemens CNC controllers to identify quasi-static models. The second model presented is a single input – single output (SISO) rigid body rapid identification model. In previous literature, a rapid identification method was proposed where a short G-code was run on machine tools, the input and output signals were collected from the controller and the dynamics were reverse engineered from the gathered data. However there were some shortfalls with this older method, the new proposed rapid identification model addresses these by improving parameter convergence and using commanded signal derivatives for identification. Tests were conducted on a five-axis machine tool located at the University of Waterloo (UW) to verify and compare the new rapid identification model to the previous model. It was determined that the model is able to predict the RMS of the tracking errors with 50-76% improvement and maximum contour error discrepancy with 22-35% improvement. Another GUI was developed for the SISO rigid body rapid identification model that allows users to import data collected from different machine tools and identify a model. The third model that is discussed in this thesis is a multi input – multi output (MIMO) model. This model builds upon the SISO rigid body model and is able to capture vibratory and elastic dynamics. Relations between inputs, such as reference and disturbance signals, can be related to a variety of measurable outputs. The model is used to predict the relationship between the inputs of commanded position and disturbance to the outputs of tracking error and velocity of the x- and y- axes of a P&WC five axis milling machine tool. Three different models were identified using this algorithm, two 1-axis 3rd order decoupled models and two 2-axis 6th order coupled model are compared in this thesis. The two 6 th order models have different search spaces, the first has a search space defined from the 3rd order decoupled identified parameters while the second has a more general search space. Overall, the 6th order model with a larger search space was able to predict the RMS and maximum tracking error more closely, with a maximum improvement of 19% for both metrics. However it should be noted that 6th order model with a smaller search space was still able to predict the RMS and maximum tracking error similarly to the 6th order model with the larger search space. The smaller search space configuration can save on computational time which can be advantageous in real world applications. In order to verify that the MIMO rapid identification technique would be able to identify a vibration mode, an experimental setup was designed and machined. A flexure mount with known vibration modes was designed, built and tested to validate Solidworks frequency simulation results. It was concluded that the simulation results were able to estimate the frequencies of the flexure with 95-98% accuracy and with a maximum absolute difference of 2.87 Hz. The flexure was mounted onto the five-axis machine tool at UW to introduce vibratory dynamics. Since there is a flexible mode being introduced at the tool-workpiece interface, the motor encoders would not be able to capture these dynamics, therefore a two-dimensional grid encoder (KGM) and two 3-axis accelerometers (one located on the tool head and the other on the workpiece table) were also placed on the machine tool to record the true tool-workpiece response. The data collected from the accelerometers were corrected for possible roll, pitch and yaw misalignments before synchronizing the accelerometer and KGM data to the motor encoder data. This data was then used to build MIMO rapid identification models with the commanded position (recorded from the motor encoders) and normalized Coulomb disturbance as the inputs to the system and the true tool-workpiece position or acceleration and machine tool feed drive velocity as the outputs to the model. The model estimated from the position measurements from the KGM yielded better results 19-1496% improvement in RMS tracking error prediction over the acceleration based models. The contouring error estimated using the KGM position model also has an improvement of 233-370% over the acceleration models. Using the transfer functions estimated from the accelerometer data, there was a 16-33% improvement in the RMS tracking error prediction and an 11-51% improvement in the maximum tracking error prediction over the KGM acceleration based model. The RMS contour prediction error also improved 4-5% and the maximum contour error prediction improved by 1-6% between the two models. Further development into the MIMO LTI algorithm is currently being done in the laboratory, including research into more complex friction models. It is also recommended to machine an actual part on the five axis machine tool and to measure the contouring error of the part on the coordinate measuring machine to verify the predictions presented in this thesis

A Summary of NASA Rotary Wing Research: Circa 20082018

The general public may not know that the first A in NASA stands for Aeronautics. If they do know, they will very likely be surprised that in addition to airplanes, the A includes research in helicopters, tiltrotors, and other vehicles adorned with rotors. There is, arguably, no subsonic air vehicle more difficult to accurately analyze than a vehicle with lift-producing rotors. No wonder that NASA has conducted rotary wing research since the days of the NACA and has partnered, since 1965, with the U.S. Army in order to overcome some of the most challenging obstacles to understanding the behavior of these vehicles. Since 2006, NASA rotary wing research has been performed under several different project names [Gorton et al., 2015]: Subsonic Rotary Wing (SRW) (20062012), Rotary Wing (RW) (20122014), and Revolutionary Vertical Lift Technology (RVLT) (2014present). In 2009, the SRW Project published a report that assessed the status of NASA rotorcraft research; in particular, the predictive capability of NASA rotorcraft tools was addressed for a number of technical disciplines. A brief history of NASA rotorcraft research through 2009 was also provided [Yamauchi and Young, 2009]. Gorton et al. [2015] describes the system studies during 20092011 that informed the SRW/RW/RVLT project investment prioritization and organization. The authors also provided the status of research in the RW Project in engines, drive systems, aeromechanics, and impact dynamics as related to structural dynamics of vertical lift vehicles. Since 2009, the focus of research has shifted from large civil VTOL transports, to environmentally clean aircraft, to electrified VTOL aircraft for the urban air mobility (UAM) market. The changing focus of rotorcraft research has been a reflection of the evolving strategic direction of the NASA Aeronautics Research Mission Directorate (ARMD). By 2014, the project had been renamed the Revolutionary Vertical Lift Technology Project. In response to the 2014 NASA Strategic Plan, ARMD developed six Strategic Thrusts. Strategic Thrust 3B was defined as the Ultra-Efficient Commercial VehiclesVertical Lift Aircraft. Hochstetler et al. [2017] uses Thrust 3B as an example for developing metrics usable by ARMD to measure the effectiveness of each of the Strategic Thrusts. The authors provide near-, mid-, and long-term outcomes for Thrust 3B with corresponding benefits and capabilities. The importance of VTOL research, especially with the rapidly expanding UAM market, eventually resulted in a new Strategic Thrust (to begin in 2020): Thrust 4Safe, Quiet, and Affordable Vertical Lift Air Vehicles. The underlying rotary wing analysis tools used by NASA are still applicable to traditional rotorcraft and have been expanded in capability to accommodate the growing number of VTOL configurations designed for UAM. The top-level goal of the RVLT Project remains unchanged since 2006: Develop and validate tools, technologies and concepts to overcome key barriers for vertical lift vehicles. In 2019, NASA rotary wing/VTOL research has never been more important for supporting new aircraft and advancements in technology. 2 A decade is a reasonable interval to pause and take stock of progress and accomplishments. In 10 years, digital technology has propelled progress in computational efficiency by orders of magnitude and expanded capabilities in measurement techniques. The purpose of this report is to provide a compilation of the NASA rotary wing research from ~2008 to ~2018. Brief summaries of publications from NASA, NASA-funded, and NASA-supported research are provided in 12 chapters: Acoustics, Aeromechanics, Computational Fluid Dynamics (External Flow), Experimental Methods, Flight Dynamics and Control, Drive Systems, Engines, Crashworthiness, Icing, Structures and Materials, Conceptual Design and System Analysis, and Mars Helicopter. We hope this report serves as a useful reference for future NASA vertical lift researchers

IST Austria Thesis

Fabrication of curved shells plays an important role in modern design, industry, and science. Among their remarkable properties are, for example, aesthetics of organic shapes, ability to evenly distribute loads, or efficient flow separation. They find applications across vast length scales ranging from sky-scraper architecture to microscopic devices. But, at the same time, the design of curved shells and their manufacturing process pose a variety of challenges. In this thesis, they are addressed from several perspectives. In particular, this thesis presents approaches based on the transformation of initially flat sheets into the target curved surfaces. This involves problems of interactive design of shells with nontrivial mechanical constraints, inverse design of complex structural materials, and data-driven modeling of delicate and time-dependent physical properties. At the same time, two newly-developed self-morphing mechanisms targeting flat-to-curved transformation are presented. In architecture, doubly curved surfaces can be realized as cold bent glass panelizations. Originally flat glass panels are bent into frames and remain stressed. This is a cost-efficient fabrication approach compared to hot bending, when glass panels are shaped plastically. However such constructions are prone to breaking during bending, and it is highly nontrivial to navigate the design space, keeping the panels fabricable and aesthetically pleasing at the same time. We introduce an interactive design system for cold bent glass façades, while previously even offline optimization for such scenarios has not been sufficiently developed. Our method is based on a deep learning approach providing quick and high precision estimation of glass panel shape and stress while handling the shape multimodality. Fabrication of smaller objects of scales below 1 m, can also greatly benefit from shaping originally flat sheets. In this respect, we designed new self-morphing shell mechanisms transforming from an initial flat state to a doubly curved state with high precision and detail. Our so-called CurveUps demonstrate the encodement of the geometric information into the shell. Furthermore, we explored the frontiers of programmable materials and showed how temporal information can additionally be encoded into a flat shell. This allows prescribing deformation sequences for doubly curved surfaces and, thus, facilitates self-collision avoidance enabling complex shapes and functionalities otherwise impossible. Both of these methods include inverse design tools keeping the user in the design loop

New advances in vehicular technology and automotive engineering

An automobile was seen as a simple accessory of luxury in the early years of the past century. Therefore, it was an expensive asset which none of the common citizen could afford. It was necessary to pass a long period and waiting for Henry Ford to establish the first plants with the series fabrication. This new industrial paradigm makes easy to the common American to acquire an automobile, either for running away or for working purposes. Since that date, the automotive research grown exponentially to the levels observed in the actuality. Now, the automobiles are indispensable goods; saying with other words, the automobile is a first necessity article in a wide number of aspects of living: for workers to allow them to move from their homes into their workplaces, for transportation of students, for allowing the domestic women in their home tasks, for ambulances to carry people with decease to the hospitals, for transportation of materials, and so on, the list don’t ends. The new goal pursued by the automotive industry is to provide electric vehicles at low cost and with high reliability. This commitment is justified by the oil’s peak extraction on 50s of this century and also by the necessity to reduce the emissions of CO2 to the atmosphere, as well as to reduce the needs of this even more valuable natural resource. In order to achieve this task and to improve the regular cars based on oil, the automotive industry is even more concerned on doing applied research on technology and on fundamental research of new materials. The most important idea to retain from the previous introduction is to clarify the minds of the potential readers for the direct and indirect penetration of the vehicles and the vehicular industry in the today’s life. In this sequence of ideas, this book tries not only to fill a gap by presenting fresh subjects related to the vehicular technology and to the automotive engineering but to provide guidelines for future research. This book account with valuable contributions from worldwide experts of automotive’s field. The amount and type of contributions were judiciously selected to cover a broad range of research. The reader can found the most recent and cutting-edge sources of information divided in four major groups: electronics (power, communications, optics, batteries, alternators and sensors), mechanics (suspension control, torque converters, deformation analysis, structural monitoring), materials (nanotechnology, nanocomposites, lubrificants, biodegradable, composites, structural monitoring) and manufacturing (supply chains). We are sure that you will enjoy this book and will profit with the technical and scientific contents. To finish, we are thankful to all of those who contributed to this book and who made it possible.info:eu-repo/semantics/publishedVersio

Analytical Models and Control Design Approaches for a 6 DOF Motion Test Apparatus

Wind tunnels play an indispensable role in the process of aircraft design, providing a test bed to produce valuable, accurate data that can be extrapolated to actual flight conditions. Historically, time-averaged data has made up the bulk of wind tunnel research, but modern flight design necessitates the use of dynamic wind tunnel testing to provide time-accurate data for high frequency motion. This research explores the use of a 6 degree of freedom (DOF) motion test apparatus (MTA) in the form of a robotic arm to allow models inside a subsonic wind tunnel to track prescribed trajectories to obtain time-accurate force and moment coefficients. Specifically, different control laws were designed, simulated, and integrated into a 2 DOF model representative of the elbow pitch and wrist pitch joints of the MTA system to decrease positional tracking error for a desired end-effector trajectory. Stability of the closed-loop systems was proven via Lyapunov analysis for all of the control laws, and the control laws proved to decrease tracking error during the trajectory case studies. An adaptive sliding mode control scheme was chosen as most suitable to simulate on the 6 DOF model due to the small tracking error as compared to the other control schemes and the availability of parameters of the actual MTA system when subject to the time-varying aerodynamics of the wind tunnel

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Conceptual Design, Structural Analysis, and Design Space Exploration of a Vacuum Lighter than Air Vehicle

The research detailed in this study investigates an internal vacuum as well as its optimal structural design, utilizing currently available materials, as an alternative to traditional gases to create and sustain buoyancy in lighter than air vehicles. To date, the consideration of a vacuum lighter than air vehicle has been limited to three sources of literature, the earliest of which dates back to 1663. This study will initially summarize and review this literature. We will then combine finite element analysis, dimensional analysis, design of experiments, and response surface methodology studies to explore the feasibility, and the functional design of a vacuum lighter than air vehicle constrained by modern technology and materials. The process developed herein allows a designer to perform a broad scope initial structural response design space investigation based on user defined constraints to determine if and where structurally feasible regions or points lie. This research then specifically analyzes two cylindrical pultruded rod geometric frame designs with membrane skins stretched over the top vacuum lighter than air vehicle designs. The first being an icosahedron frame and skin structure proposed by Metlen at the Air Force Institute of Technology

Trade integration, global capital flows and the link to institutional quality from a North-South perspective

This doctoral thesis is a cumulative dissertation containing three essays. In the first essay, I create a panel data set of North-South preferential trade agreements (PTAs) building on the comprehensive database on the design of trade agreements (DESTA). I analyze the effects of the depth and number of PTAs signed on the quality of institutions in developing countries, the global South, measured as the political risk component investment profile of the ICRG database. I show that the system GMM is the appropriate estimator to apply for my empirical analysis to account for various sources of endogeneity. I show that signing deep North-South PTAs positively affects institutions in the South. The results differ with respect to the type of agreement and region. The second essay deals with the determinants of PTAs focusing on institutional distance as a driving factor and regarding PTAs as an instrument to compensate for missing institutions. I argue that the effect of institutional distance is specifically important (1) in a North-South trade relationship where institutional distance is particularly large and (2) if countries trade a large share of contract-intensive goods. For this analysis I create a panel data set including a large number of developing countries and a variable to measure the difference of the share of bilateral contract-intensive exports and show that a linear probability model for discrete choice panel data is a suitable estimator to be used. I address endogeneity using an instrument variable (IV) approach. I show that institutional distance promotes the formation of PTAs. Comparing this effect for North-North, North-South, and South-South country pairs reveals that the positive effect of institutional distance on the probability of PTA formation is specifically high for the formation of North-South PTAs. Furthermore, I find that the effect is nonlinear and that trading contract-intensive goods reinforces the positive effect of institutional distance for the formation of North-South PTAs and may offset negative effects. Robustness checks with regard to the underlying sample reveal that the effect of institutional distance is driven by North-South relationships involving the EU. Essay 3 is dedicated to global investment flows and aims at deriving a global model to determine the factors of foreign direct investment (FDI) by considering investment flows between and within North and South. We empirically estimate and assess global FDI models, namely the gravity and knowledge capital (KK) model, based on the new CDIS data set by the IMF, which includes a large number of developing and transition countries. This allows us to detect potential vertical motives for FDI and to address the global trend of increasing FDI from and to the global South. We find the gravity model to achieve the best theory-consistent out-of-sample prediction, particularly when parameter heterogeneity of South and North FDI is allowed for. Controlling for surrounding market potential is important to recover the horizontal effect of the gravity model. Including institutional, cultural, or financial factors does not improve the model performance distinctly although results for those variables are mostly in line with theory.Diese Dissertation ist eine kumulative Dissertation bestehend aus drei Essays. Im ersten Aufsatz erstelle ich einen Panel-Datensatz aus Nord-Süd Präferenzhandelsabkommen (PTAs), der auf den umfassenden Datensatz Design of Trade Agreements Database (DESTA) aufbaut. Auf Basis des Panels analysiere ich die Auswirkungen der Tiefe und der Anzahl der unterzeichneten PTAs auf die Qualität der Institutionen von Entwicklungsländern, dem globalen "Süden", gemessen durch das "Investitionsprofil" des Political Risk Index des International Country Risk Guide (ICRG). Die Verwendung des System GMM zeigt sich als geeigneter Schätzer für die empirische Analyse, um verschiedene Quellen der Endogeneität zu berücksichtigen. Die Ergebnisse unterstützen die Hypothese, dass sich die Unterzeichnung tiefer Nord-Süd-PTAs positiv auf die Institutionen im "Süden" auswirkt. Die Ergebnisse unterscheiden sich in Bezug auf die Art der Abkommen und die Region der Entwicklungsländer. Der zweite Aufsatz befasst sich mit den Determinanten von Handelsabkommen. Der Fokus der Analyse liegt auf der institutionellen Distanz als Faktor und betrachtet Handelsabkommen als rechtliches Instrument zur Kompensation fehlender Institutionen. In meiner empirischen Analyse untersuche ich die Hypothese, dass die Wirkung der institutionellen Distanz insbesondere wichtig ist, wenn (1) Länderpaare in einer Nord-Süd-Handelsbeziehung stehen, in der die institutionelle Entfernung besonders groß ist und (2) Länder einen hohen Anteil vertrags-intensiver Güter handeln. Im Rahmen meiner Analyse erstelle ich einen Panel-Datensatz, der eine große Anzahl von Entwicklungsländern umfasst und eine Variable zur Messung der Distanz des Anteils bilateraler vertrags-intensiver Exporte. Ich zeige, dass ein lineares Wahrscheinlichkeitsmodell für Paneldaten mit diskreter Outcome-Variable ein geeigneter Schätzer ist. Potentielle Endogeneität berücksichtige ich im Rahmen einer geeigneten Instrumentierung. Die Ergebnisse zeigen, dass institutionelle Distanz die Bildung von Handelsabkommen fördert. Ein Vergleich dieses Effekts für Nord-Nord-, Nord-Süd- und Süd-Süd-Länderpaare zeigt, dass der positive Effekt der institutionellen Distanz auf die Wahrscheinlichkeit der Bildung von Handelsabkommen, speziell für die Bildung von Nord-Süd-Handelsabkommen, besonders hoch ist. Darüber hinaus stützen die Ergebnisse meine Annahme, dass der Effekt nichtlinear ist und dass der Handel vertrags-intensiver Gütern den positiven Effekt der institutionellen Entfernung für die Bildung von Nord-Süd-PTA verstärkt und negative Auswirkungen ausgleichen kann. Die Robustheitsprüfungen in Bezug auf die zugrunde liegende Stichprobe zeigen, dass die Auswirkungen der institutionellen Entfernung auf die Nord-Süd-Beziehungen insbesondere auf die Beteiligung der EU zurückzuführen sind. Essay 3 widmet sich den globalen Investitionsströmen und zielt darauf ab, ein globales Modell abzuleiten, um die Faktoren ausländischer Direktinvestitionen (FDI) zu bestimmen, indem die Investitionsströme zwischen und innerhalb des globalen "Nordens" und "Südens" berücksichtigt werden. Wir schätzen und bewerten empirisch globale FDI-Modelle, das Gravity- und Knowledge-Capital-Modell (KK), basierend auf dem neuen CDIS-Datensatz des IWF, der eine große Anzahl von Entwicklungs- und Transformationsländern umfasst. Dies ermöglicht es uns, potenzielle vertikale Motive für FDI zu erkennen und dem globalen Trend steigender FDI-Ströme aus und in den Süden zu erklären. Die Analyse zeigt, dass die Verwendung des Gravity-Modells zu den besten theorie-konsistenten Out-of-Sample-Vorhersagen führt, insbesondere wenn heterogene Faktoren in Süd- und Nord-FDI berücksichtigt werden. Die Berücksichtigung des umgebenden Marktpotenzials trägt insbesondere dazu bei, horizontale Effekte des Gravity-Modells aufzuzeigen. Die Einbeziehung institutioneller, kultureller oder finanzieller Faktoren verbessert die Modellleistung nicht wesentlich, obwohl die Ergebnisse für diese Variablen meist der Theorie entsprechen

Parameter Estimation for Physics-Based Electrochemical Model Parameterization and Degradation Tracking

Physics-based electrochemical models are useful tools for optimizing battery cell and material design, managing battery use, and understanding physical phenomena, all of which are key in enabling adoption of batteries to electrify transportation, grid storage, and other high carbon emission industries. Fitting these models to experiments can be a useful approach to determine missing parameters that may be difficult to identify experimentally. In this dissertation, two use cases of this approach — model parameterization and degradation tracking — are explored. An introduction to the need for batteries and an overview of challenges in the field is presented in Chapter 1. Of these challenges, those that can be addressed by battery modeling solutions are discussed in further detail. An overview of continuum level physics-based electrochemical models is provided, and the case is made for the utility of parameter estimation. In Chapter 2, an extension of a published model for lithium trivandate cathodes for lithiumion batteries is outlined. While the original model described (de)lithiation and phase change in the cathode, the new model describes simultaneous lithiation of the original phase, lithiation of the newly formed phase, and phase change. Parameters associated with the thermodynamics and kinetics of charge transfer and lithium transport in the second phase are estimated directly from experimental data. This study serves as an example of using the model fitting approach to determine model parameters that would be difficult to isolate and measure experimentally. Chapter 3 explores a similar concept of model parameterization, this time focusing on the electrode tortuosity. Tortuosity is a hard to quantify parameter that describes how tortuous of a path lithium ions must travel through an electrode or separator. Because there are several experimental measurement techniques suggested in the literature that do not always provide consistent results, an approach to fit the tortuosity to a standard rate capability experiment is introduced. The Bayesian approach returns uncertainties in tortuosity estimates, which can be used to predict a range of outcomes for high-rate performance. Covariance between parameters in the model and their impact on uncertainties in tortuosity is also discussed. Beyond model parameterization, parameter estimation can also be useful in the context of tracking degradation by fitting a physics-based model over the course of cycling and interpreting the evolution of the parameter estimates. In Chapter 4, this idea is explored by fitting the model developed in Chapter 2 to cycling of an LVO cell. Parameter estimates are interpreted in conjunction with traditional tear down and electrochemical analysis to identify root causes of degradation for this cell. Depending on the number of parameters being simultaneously estimated, it can become an onerous task to fit model parameters, especially if the physics-based model cannot easily be enclosed in an efficient optimization algorithm. To this end, machine learning (ML) can be useful. If a ML model is trained offline on synthetic data generated by a battery model to map the observable electrochemical data to parameters in the battery model, the ML model can be deployed to estimate parameters from experiment. These models can be referred to as inverse ML models, since they perform the inverse task of a "forward" physics based model. The procedure described above is implemented in Chapter 5. Interpretable ML models are trained on published synthetic data generated by equivalent circuit models. Pseudo-OCV (slow charge, C/25) full cell voltage curves are passed into the inverse ML models to estimate degradation modes in lithium ion batteries and classify which electrode limits cell capacity. These models are useful in diagnosing the state of the battery at any given time. Accuracies of the inverse ML models are evaluated on independent test sets also composed of synthetic data and are published to benchmark future diagnostic studies. The insights derived from the trained ML models in terms of which features in the full cell voltage curves are predictive of the degradation modes are compared to expert insights. In chapter 6, the robustness of the inverse ML approach towards model-experiment disagreement is probed. If the experiment does not directly map onto the protocol used to generate the synthetic training data for the ML model, or if the model itself is inherently a poor descriptor of experiment, the inverse ML model will inevitably return inaccurate estimates. In this chapter, a feed forward neural network (NN) is employed as the inverse ML model. In two case studies of model-experiment disagreement, the NN returns biased parameter estimates. A simple data augmentation procedure is introduced to mitigate these biases. Chapter 7 ties together the understanding developed in the previous chapters by applying more robust neural networks to estimate parameters for LVO cells cycled at different rates. This study demonstrates how to interpret parameter estimates in conjunction with cycling data to gain mechanistic insight into degradation. A complex map of coupled degradation hypotheses is reduced to a smaller subset of possible mechanisms for two exemplary LVO cells, and parameter estimates for a larger set of LVO cells are discussed. The framework presented in this study synergistically combines experiment, physics-based modeling, and machine learning to better understand degradation phenomena

Empirical and Kinetic Models for the Determination of Pharmaceutical Product Stability

Drug stability is one of the vital subjects in the pharmaceutical industry. All drug products should be kept stable and protected against any chemical, physical, and microbiological degradation to ensure their efficacy and safety until released for public use. Hence, stability is very important to be estimated or predicted. This work involved studying the stability of three different drug agents using three different mathematical models. These models included both empirical models (linear regression and artificial neural network), and mechanistic (kinetic) models. The stability of each drug in the three cases studied was expressed in terms of concentration, hardness, temperature and humidity. The predicted values obtained from the models were compared to the observed values of drug concentrations obtained experimentally and then evaluated by calculating the mean of squared. Among the models used in this work, the mechanistic model was found to be the most accurate and reliable method of stability testing given the fact that it had the smallest calculated errors. Overall, the accuracy of these mathematical models as indicated by the proximity of their stability measurements to the observed values, led to the assumption that such models can be reliable and time-saving alternatives to the analytical techniques used in practice