The study of crystal growth using molecular dynamics is very difficult due to the slow rate of growth and the complex intermolecular interactions involved. In order to perform molecular dynamics simulations of crystal growth from aqueous solution accurately, both interactions within the crystal and interactions between the crystal molecules and water need to be described correctly. In this study, we have investigated and compared various force fields for their applicability in the molecular dynamics study of R-glycine crystallization. The force fields that have been investigated include Charmm27, general AMBER, OPLS-AA/L, and Gromos53a6. As the electrostatic interactions are expected to play a significant role in the properties of bulk glycine crystal and solution, five other charge sets obtained from first principles calculations have also been investigated. Simulations in the bulk crystal and aqueous solution environments have been carried out, and results for the R-glycine lattice energy, solution densities, and self-diffusivities are compared to available experimental results. The solution enthalpy has also been determined and is found to be a good indicator of the applicability of the force field for crystal growth studies. The general AMBER force field, coupled with charges derived from calculations using the Complete Neglect of Differential Overlap method, is found to be the optimal force field, resulting in significant crystal growth at the (010) face of the R-glycine crystal from a slightly supersaturated glycine solution. Such simulations provide insights into the mechanism of crystal growth at the molecular level. We observe that R-glycine crystal growth involves monomeric growth units that attach to the existing crystal face in the correct molecular orientation. This work also opens up possibilities to systematically investigate the various factors that affect crystal growth through simulations, such as temperature, concentration, solvent, impurities, etc., so as to gain a better overall understanding of the crystal growth process.

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Fiber reinforced thermoplastic composites are gaining popularity in many industries due to their short consolidation cycles, among other advantages over thermoset-based composites. Computer aided manufacturing processes, such as filament winding and automated fiber placement, have been used conventionally for thermoset-based composites. The automated processes can be adapted to include in situ consolidation for the fabrication of thermoplastic-based composites. In this paper, a detailed literature review on the factors affecting the in situ consolidation process is presented. The models used to study the various aspects of the in situ consolidation process are discussed. The processing parameters that gave good consolidation results in past studies are compiled and highlighted. The parameters can be used as reference points for future studies to further improve the automated manufacturing processes.

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Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantage of this customizability, the design process for fiber-reinforced polymer composite components needs to be improved. Machine learning methods have been identified as potential techniques capable of handling the complexity of the design problem. In this review, the applications of machine learning methods in various aspects of structural component design are discussed. They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. A design automation framework for performance-optimized fiber-reinforced polymer composite components is also proposed. The proposed framework aims to provide a comprehensive and efficient approach for the design and optimization of fiber-reinforced polymer composite components. The challenges in building the models required for the proposed framework are also discussed briefly.