A model to compute the elastic modulus and tensile properties of 3D printed Carbon Fiber Reinforced Polymers (CFRP) is presented. The material under consideration is Carbon Fiber Reinforced Nylon (CFRN) produced in a Fused Deposition Modeling (FDM) process. A relationship between the nylon raster in each layer and the carbon fiber volume fraction was devised with the help of a scanning electron microscope (SEM). Thirteen groups with different layer configurations and carbon-fiber percentages were formulated and tested to obtain the elastic modulus and tensile strength. This study focused only on the properties along the printed fiber direction. The results from these tests were analyzed within the rule of mixtures framework. The results suggest that the rule of mixtures can be successfully applied to unidirectional CFRP fabricated using additive manufacturing.

An experimental procedure to determine the elastic modulus and tensile strength of kevlar reinforced nylon composites is discussed. Thermal Gravimetric Analysis (TGA), has been performed to determine the volume fraction of fiber and matrix components. TGA is a robust method to determine the volume fraction. It is also less labor intensive as compared to other methods. Samples with varying kevlar-nylon layer ratio were additively manufactured using fused deposition modelling (FDM) based on ASTM D3039 standards. MarkForged Mark X7 3D printer was used to manufacture samples. Elastic and tensile tests of the samples were conducted. The relation between volume fraction and elastic modulus of the composite can indeed be fit into the rule of mixtures model. However, its applicability for ultimate tensile strength for high fiber ratio composites has been put to question. The direction of fibers in the additively manufactured samples has been kept parallel to the loading direction. In this paper we will give the readers a deeper understanding of how additively manufactured composite samples behave under loading, further facilitating the design process for materials produced by additive manufacturing.

Over the past several years, microgrids have been setup in remote villages in developing countries such as India, Kenya and China to boost the standards of living of the less privileged citizens, mostly by private companies. However, these systems succumb to increase in demand and maintenance issues over time. A method for scaling the capacity of solar powered microgrids is presented in this paper. The scaling is based on both the needs of the owner and those of the consumers. Data acquired from rural villages characterizes the electrical use with respect to time. Further, it employees a Long-Short Term Memory (LSTM) deep learning model that can help the owner predict future demand trends. This is followed by a model to determine the optimum increase in capacity required to meet the predicted demand. The model is based on empowering the owner to make informed decisions and the equity of energy distribution is the key motivation for this paper. The models are applied to a village in Eastern India to test its applicability. Acknowledging the highly varying nature of demand for electricity and its applications, we propose a rule-based adaptive power management strategy which can be tailored specifically in accordance to the preference of the communities. This will ensure a fair distribution of power for everyone using the system, thereby making it applicable anywhere in the world. We propose to incorporate social and demographic conditions of the user in the optimization to ensure that the profit of the owner does not outweigh the needs of the users.

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