Browsing by Author "Khan, K. A."
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Item Open Access Experimental assessment of multiple contact wear using airborne noise under dry and lubricated conditions(SAGE Publications (UK and US), 2017-03-29) Khan, Muhammad; Basit, K.; Khan, S. Z.; Khan, K. A.; Starr, Andrew G.The generation of wear and airborne noise is inevitable in the mechanical contacts of the machine components. This paper addresses the effectiveness of the airborne noise data in estimating the wear on a disc under multi-contact conditions. A pin-on-disc rig was employed to study the role of noise parameters on the evolution of the wear area. When a pin slides on the disc, the airborne noise is generated and subsequently a sound signal is obtained. These signals, for various sets of experiments, were recorded using a digital microphone. A Matlab code was developed and employed to estimate the noise parameters from the recorded sound. Noise parameters including values of voltage RMS, noise counts and amplitudes of dominant frequencies were used to analyse the variation in the disc wear at different time intervals. These parameters were found to be effective in the determination of the wear damage evaluation under different loads without lubrication.Item Open Access Micromechanical modeling of 8-harness satin weave glass fiber-reinforced composites(SAGE Publications (UK and US), 2017-03-01) Choudhry, R. S.; Khan, K. A.; Khan, Muhammad; Hassan, A.This study introduces a unit cell-based finite element micromechanical model that accounts for correct post cure fabric geometry, in situ material properties and void content within the composite to accurately predict the effective elastic orthotropic properties of 8-harness satin weave glass fiber-reinforced phenolic composites. The micromechanical model utilizes a correct post cure internal architecture of weave, which was obtained through X-ray microtomography tests. Moreover, it utilizes an analytical expression to update the input material properties to account for in situ effects of resin distribution within yarn (the yarn volume fraction) and void content on yarn and matrix properties. This is generally not considered in modeling approaches available in literature and in particular, it has not been demonstrated before for finite element micromechanics models of 8-harness satin weave composites. The unit cell method is used to obtain the effective responses by applying periodic boundary conditions. The outcome of the analysis based on the proposed model is validated through experiments. After validation, the micromechanical model was further utilized to predict the unknown effective properties of the same composite.Item Open Access Piezoelectric metamaterial with negative and zero Poisson's ratios(American Society of Civil Engineers, 2019-09-28) Khan, K. A.; Al-Mansoor, S.; Khan, S. Z.; Khan, Muhammad A.This study presents the finite element–based micromechanical modeling approach to obtain the electromechanical properties of the piezoelectric metamaterial based on honeycomb (HC) cellular networks. The symmetry of the periodic structure was employed to derive mixed boundary conditions (MBCs) analogous to periodic boundary conditions (PBCs). Three classes of hexagonal HC cellular networks, namely, a conventional HC (CHC), a re-entrant HC (RE), and a semi-re-entrant HC (SRE) were considered. The representative volume elements (RVEs) of these three classes of cellular materials were created, and finite element analyses were carried out to analyze the effect of orientation of the ligament on their effective electromechanical properties and their suitability in specific engineering applications. The longitudinally poled piezoelectric HC cellular networks showed an enhanced behavior as compared to the monolithic piezoelectric materials. Moreover, longitudinally poled HC cellular networks demonstrated that, as compared to the bulk constituent, their hydrostatic figure of merit increased and their acoustic impedance decreased by one order of magnitude, respectively, indicating their applicability for the design on hydrophones. Moreover, results showed that cellular metamaterial with tunable electromechanical characteristics and a variety of auxetic behaviors such as negative, positive, or zero Poisson’s ratios could be developed. Such novel HC network-based functional cellular materials are likely to facilitate the design of light-weight devices for various next-generation sensors and actuators.Item Open Access Review of advanced techniques for manufacturing biocomposites: non-destructive evaluation and artificial intelligence assisted modeling(Springer, 2022-08-30) Preethikaharshini, J.; Naresh, K.; Rajeshkumar, G.; Arumugaprabu, V.; Khan, Muhammad A.; Khan, K. A.Natural fiber reinforced polymer composites (NFRPCs) are being widely used in aerospace, marine, automotive, and healthcare applications due to their sustainability, low cost and ecofriendly nature. The NFRPCs manufactured through conventional, and computer controlled intelligent manufacturing techniques may contain internal and external defects. Traditionally, the microstructure of NFRPCs at different stages of manufacturing was obtained using destructive techniques which have stringent sample size restrictions and may cause decrease in residual properties of composites due to destructive scanning. However, these complications can be overcome by using non-destructive evaluation (NDE) and artificial intelligence (AI) techniques. This review highlights the impact of NDE and AI on the improvement of emerging manufacturing systems. We have discussed the classification of biocomposites, their manufacturing techniques, recyclability and strategies to improve mechanical properties. Further, the use of different types of contact and non-contact NDE techniques in understanding the microstructural variations during manufacturing, machining and the parameters that effects the mechanical performance of NFRPCs are discussed. The use of NDE images in developing the geometrical and computational models of NFRPCs are presented. We have highlighted the importance of AI technology in enhancing the quality of NDE images, improving the microstructural information before post-processing the data, and minimizing the analysis time, and identifying the defects and damages in NFRPCs. In the end, we presented the application of NDE techniques and AI technology in efficient generation of digital material twins of NFRPCs, which will be useful to design next generation biocomposites.