The mechanical properties of carbon fiber-reinforced epoxy composites were identified by adding carbon-based nano-reinforcements, such as multi-wall carbon nanotubes (CNTs) and graphene platelets (GP), into the epoxy matrix by conducting suitable experiments. The main focus of this study is to compare the tensile modulus, tensile strength, flexural modulus, flexural strength, and thermal conductivity of carbon fiber-reinforced epoxy composites with nanoparticle reinforcement. The results revealed that adding CNTs and GP nanoparticles improved the mechanical properties compared to a pure carbon fiber-reinforced plastic composite. However, compared to CNTs, the GP's addition has increased the mechanical properties of the CFRP composite. In addition, scanning electron microscopy (SEM) images were presented to explore the microstructural characterization of carbon fiber-reinforced nanoparticle-reinforced composites. Further, using numerical studies, the transverse modulus, major and minor Poisson’s ratio of the carbon fibre reinforced with CNT and GP particle reinforcement were estimated. The current study is applied to the efficient design of nanoparticle reinforced carbon fibre reinforced composites.
Today, smartphones are being used to manage almost all aspects of our lives, ranging from personal to professional. Different users have different requirements and preferences while selecting a smartphone. There is ‘no one-size fits all’ remedy when it comes to smartphones. Additionally, the availability of a wide variety of smartphones in the market makes it difficult for the user to select the best one. The use of only product ratings to choose the best smartphone is not sufficient because the interpretation of such ratings can be quite vague and ambiguous. In this paper, reviews of products are incorporated into the decision-making process in order to select the best product for a recommendation. The top five different brands of smartphones are considered for a case study. The proposed system, then, analyses the customer reviews of these smartphones from two online platforms, Flipkart and Amazon, using sentiment analysis techniques. Next, it uses a hybrid MCDM approach, where characteristics of AHP and TOPSIS methods are combined to evaluate the best smartphones from a list of five alternatives and recommend the best product. The result shows that brand1 smartphone is considered to be the best smartphone among five smartphones based on four important decision criteria. The result of the proposed system is also validated by manually annotated customer reviews of the smartphone by experts. It shows that recommendation of the best product by the proposed system matches the experts’ ranking. Thus, the proposed system can be a useful decision support tool for the best smartphone recommendation.
The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.