Advanced composite materials are usually optimized to achieve balance of properties for given range of applications. In recent times, researchers had worked on the sandwich composites by using different foam and metal honeycomb as a core material. In the current project, honeycomb core is prepared by using 3D printed technology. In this case of sandwich composites, cross-linked polyethylene foam and 3D-printed polylactic acid honeycomb as core and GFRP is used as face sheet. The comparison is made between polyethylene foam and 3D printed honeycomb core sandwich composite in the aspect of toughness, strength, and modulus. The present study is to characterize the damages in the sandwich structure for the amount of energy absorbed by the structures such as delamination, indentation, crushing of foams, and debonding of face sheets and core material subjected to free fall impact. The contact force versus time, contact force versus deflection of plates with respect to impact energy levels of 9.3, 16.5, and 25.7 J and impact energy versus time are determined. The current research helps in determination of core materials effecting/absorbing the damage and behavior of sandwich materials subjected to impact loads.

No abstract

Stainless steel is most extensively utilized material in all engineering applications, house hold products, constructions, because it is environment friendly and can be recycled. The principal purpose of this paper is to implement different data science algorithms for predicting stainless steel mechanical properties. Integrating Data science techniques in material science and engineering helps manufacturers, designers, researchers and students in understanding the selection, discovery and development of materials used for various engineering applications. Data science algorithms help to find out the properties of the material without performing any experiments. The Data Science techniques such as Random Forest, Neural Network, Linear regression, K- Nearest Neighbor, Support vector Machine, Decision Tree, and Ensemble methods are used for predicting Tensile Strength by specifying processing parameters of stainless steel like carbon content, sectional size, temperature, manufacturing process. The research here is developed as part of AICTE grant sanctioned under RPS scheme [19] and it aims to implement different data science algorithms for predicting Tensile strength of steel and identifying the algorithm with decent prediction accuracy.