Computational and experimental assessment of peacock feather

Beauty attracts People and peacock feathers have got such vibrant beauty because of their colorful appearance, aesthetic appeal, and monetary value. Due to their incredible ability to transform and attract the opposite gender, peacock feathers have acquired impressively attractive qualities over hundreds of years, including the potential to attack and prey. A peacock’s feather is robust enough to withstand aerodynamic stresses, maintain its aerodynamic shape, and be raised to attract the opposing gender or to defend oneself from an intruder. Additionally, it is sufficiently flexible and therefore more durable being compressed, maintaining its fractures. The study of novel bio-inspired designs on bird structure has already been reported in the literature and demonstrated the superior compressive performance of bird’s feather-inspired designs; however, the Micro-CT based simulation of peacock calamus shaft and experimental comparison of compression and flexural properties using a three-point bending test has not yet published in the literature. This study examined the variance in barb length throughout the rachis as well as the barbs’ tensile characteristics at three different rachis locations. In addition to this research, a micro-CT-based computer model of a peacock calamus shaft was developed to forecast the microscopic elastic and strength properties, and the outcomes of the simulation and experiment were compared. The study is divided into two parts: the first to examines the compression and three-point bending in relation to design variables obtained through post-processing of Micro-CT images, and the second to conducts an experimental investigation using the same sample of calamus shaft used for Micro-CT imaging and compares the outcomes of the two approaches. The findings of this study demonstrate that a natural feather’s shaft is superior in design and it is optimized, and that Micro-CT based FEA provides good correlated results that can be utilized to predict the sample’s attributes.