Thermal and Mechanical Numerical Modeling of Extrusion-based 3d Printed Reinforced Polymers for Selecting Manufacturing Process Parameters

Extrusion-based 3D printing of thermoplastic polymer composites manufactures parts that have nonhomogenous, orthotropic, and process-dependent macro-scale material properties. As a part of the dissertation, research works were carried out to: • improve the interlayer mechanical properties and reduce the orthotropy, • use experimentally homogenized orthotropic material properties to numerically model the mechanical behavior of the non-homogenous orthotropic 3D printed parts, • create an efficient numerical thermal model to predict the process-dependent thermal history of the 3D printed part, and • aid the manufacturing process by selecting a suitable set of processing parameters based on a simplified sequentially coupled thermomechanical model. The dissertation presents four studies that improve the understanding of the mechanical behavior and aid the manufacturing process of the 3D printed thermoplastic polymer composites. Three journal publications that resulted from the research work carried out are listed below: • Bhandari, S., Lopez-Anido, R. A., & Gardner, D. J. (2019). Enhancing the interlayer tensile strength of 3D printed short carbon fiber reinforced PETG and PLA composites via annealing. Additive Manufacturing, 30, 100922. iii • Bhandari, S., Lopez-Anido, R.A., Wang, L. et al. (2020). Elasto-Plastic Finite Element Modeling of Short Carbon Fiber Reinforced 3D Printed Acrylonitrile Butadiene Styrene Composites. JOM 72, 475– 484. • Bhandari, S.; Lopez-Anido, R.A. (2020). Discrete-Event Simulation Thermal Model for Extrusion-Based Additive Manufacturing of PLA and ABS. Materials, 13, 4985

Active Processor Scheduling Using Evolution Algorithms

The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real-world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives. The other coevolutionary algorithm generates difficult allocation problems for testing the capabilities of the allocation EA. The effectiveness of both coevolutionary algorithms for optimizing the allocation EA is examined quantitatively using standard statistical methods. Also the allocation EAs objective tradeoffs are analyzed and compared

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Design and Control of Power Converters 2019

In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc

National freight transport planning: towards a Strategic Planning Extranet Decision Support System (SPEDSS)

This thesis provides a `proof-of-concept' prototype and a design architecture for a Object Oriented (00) database towards the development of a Decision Support System (DSS) for the national freight transport planning problem. Both governments and industry require a Strategic Planning Extranet Decision Support System (SPEDSS) for their effective management of the national Freight Transport Networks (FTN). This thesis addresses the three key problems for the development of a SPEDSS to facilitate national strategic freight planning: 1) scope and scale of data available and required; 2) scope and scale of existing models; and 3) construction of the software. The research approach taken embodies systems thinking and includes the use of: Object Oriented Analysis and Design (OOA/D) for problem encapsulation and database design; artificial neural network (and proposed rule extraction) for knowledge acquisition of the United States FTN data set; and an iterative Object Oriented (00) software design for the development of a `proof-of-concept' prototype. The research findings demonstrate that an 00 approach along with the use of 00 methodologies and technologies coupled with artificial neural networks (ANNs) offers a robust and flexible methodology for the analysis of the FTN problem domain and the design architecture of an Extranet based SPEDSS. The objectives of this research were to: 1) identify and analyse current problems and proposed solutions facing industry and governments in strategic transportation planning; 2) determine the functional requirements of an FTN SPEDSS; 3) perform a feasibility analysis for building a FTN SPEDSS `proof-of-concept' prototype and (00) database design; 4) develop a methodology for a national `internet-enabled' SPEDSS model and database; 5) construct a `proof-of-concept' prototype for a SPEDSS encapsulating identified user requirements; 6) develop a methodology to resolve the issue of the scale of data and data knowledge acquisition which would act as the `intelligence' within a SPDSS; 7) implement the data methodology using Artificial Neural Networks (ANNs) towards the validation of it; and 8) make recommendations for national freight transportation strategic planning and further research required to fulfil the needs of governments and industry. This thesis includes: an 00 database design for encapsulation of the FTN; an `internet-enabled' Dynamic Modelling Methodology (DMM) for the virtual modelling of the FTNs; a Unified Modelling Language (UML) `proof-of-concept' prototype; and conclusions and recommendations for further collaborative research are identified

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science