Engineering Commons^™

Mechanical Interfacial Locking And Multiscale Modeling Of Reinforced Thermoplastic Composites For Structural Applications, Anmol Kothari

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The ever-growing pressure of reducing the adverse impact of transportation systems on environment has pushed industries towards fuel-efficient and sustainable solutions. While several approaches have been used to improve fuel efficiency, the light-weighting of structural components has proven broadly effective. In this regard, reinforced thermoplastic composites (RTPC), owing to their high recyclability, higher impact strength and fast cycle times, have become competitive candidates at an industrial scale. However, to implement RTPC toward large scale structural applications several challenges pertaining to material design and manufacturing effects need to be addressed. To this end, a computational study is carried out to address …

Investigation And Characterization Of New Optically Stimulated Luminescence (Osl) Dosimetric Materials, Linyu Pan

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Optically stimulated luminescence (OSL) dosimeters have attracted increasing attention due to advantages over TL dosimeters, including no thermal quenching and higher sensitivity. Nevertheless, currently, there are only two commercially available OSL dosimeters, BeO and Al₂O₃:C. An OSL dosimeter requires low effective atomic number (Z_eff < 16), at least one type of recombination center and one type of trap, and UV~blue emission. In this dissertation, motivated by the search of new OSL dosimetric materials, a systematic investigation of alumina (Z_eff = 11.3) and magnesium aluminate spinel (Z_eff = 11.2) based materials was performed in terms of the engineering of the recombination centers and electronic traps. Microstructural characterization was executed by means of X-ray diffraction (XRD) and Raman spectroscopy. Luminescence was characterized under X-ray excitation (radioluminescence; RL) at ambient and …

Deep Learning-Guided Prediction Of Material’S Microstructures And Applications To Advanced Manufacturing, Jianan Tang

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Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss.

In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) …

Understanding Process-Structure-Property-Performance Relationships Of Thermoplastic Olefins (Tpo) Foams Through A Novel Manufacturing To Response Pathway, Sai Aditya Pradeep

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The global transportation industry is the second highest contributor to climate change. As a result, there has been a concerted effort to spearhead research in economical lightweighting technologies, as every 10 % reduction in weight will lead to to 6 – 8 % improvement in fuel efficiency. Additionally, the recent push for electrification and the emphasis on Corporate Average Fuel Economy (CAFE) standards have seen original equipment manufacturers (OEM’s) dive into lightweighting of materials to improve overall range and/or fuel-efficiency. Thermoplastic Olefins (TPOs) have in recent years carved out a niche in the automotive industry due to advantages such as …