Process–Structure–Properties in Polymer Additive Manufacturing

Additive manufacturing (AM) methods have grown and evolved rapidly in recent years. AM for polymers is an exciting field and has great potential in transformative and translational research in many fields, such as biomedical, aerospace, and even electronics. Current methods for polymer AM include material extrusion, material jetting, vat polymerisation, and powder bed fusion. With the promise of more applications, detailed understanding of AM—from the processability of the feedstock to the relationship between the process–structure–properties of AM parts—has become more critical. More research work is needed in material development to widen the choice of materials for polymer additive manufacturing. Modelling and simulations of the process will allow the prediction of microstructures and mechanical properties of the fabricated parts while complementing the understanding of the physical phenomena that occurs during the AM processes. In this book, state-of-the-art reviews and current research are collated, which focus on the process–structure–properties relationships in polymer additive manufacturing

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

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Layer-By-Layer Assembly of Nanostructured Composites: Mechanics and Applications.

The development of efficient methods for preparation of nanometer-sized materials and our evolving ability to manipulate the nanoscale objects have brought about a scientific and technological revolution called: nanotechnology. This revolution has been especially driven by discovery of unique nanoscale properties of the nanomaterials which are governed by their inherent size. Today, the total societal impact of nanotechnology is expected to be greater than the combined influences that the silicon integrated circuit, medical imaging, computer-aided engineering, and man-made polymers have had in the last century. Many nanomaterials were also found to possess exceptional mechanical properties. This led to tremendous interest into developing composite materials by exploiting the mechanical properties of these building blocks. In spite of a tremendous volume of work done in the field, preparation of such nanocomposites (NCs) has proven to be elusive due to inability of traditional “top-down” fabrication approaches to effectively harness properties of the nano-scale building blocks. This thesis focuses on preparation of organic/inorganic and solely organic NCs via a bottom-up nano-manufacturing approach called the layer-by-layer (LBL) assembly. Two natural and inexpensive nanoscale building blocks are explored: nanosheets of Na+-montmorillonite clay (MTM) and rod-shaped nanocrystals of cellulose (CNRs). In the first part of the thesis, we present results from systematic study of mechanics of MTM-based NCs. Different compositions are explored with a goal of understanding the nanoscale mechanics. Ultimately, development of a transparent composite with record-high strength and stiffness is presented. In the second part, we present results from LBL assembly of the CNRs. We demonstrate feasibility of assembly and mechanical properties of the resulting films. We also demonstrate preparation of LBL films with anti-reflective properties from tunicate (a sea animal) CNRs. In the final part, we show preparation of high toughness and hierarchically organized NCs using two concepts: “exponential” LBL (e-LBL) assembly and charged polyurethanes. We show preparation of novel e-LBL structures and highly flexible LBL multilayers. We also demonstrate preparation of macro-scale composites from hierarchical, post-assembly consolidation of LBL sheets. This last result represents a potential paradigm change in the practice of LBL assembly by enabling transformation of the thin-films into macro-scale structures.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61712/1/apodsiad_1.pd

Modeling and Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering