Experimental investigation on mechanical performance and drilling behavior of hybrid polymer composites through statistical and machine learning approach

Abstract

The current study focuses on fabricating partially biodegradable composites added with nettle and grewia optiva fibers in epoxy. The mechanical properties of various fiber reinforcement combinations, such as tensile, impact, and flexural strength were evaluated. One of the main issues when drilling natural fiber-reinforced polymer composites is delamination damage. Therefore, the drilling ability of the hybrid composites was investigated by various drilling operation conditions: drill diameter (4, 6, 8 mm), feed rate (0.125, 0.212, 0.3 mm/rev) and spindle speed (400, 600, 800 rev/min). The experimental investigation was carried out using a twist drill at dry and ambient temperatures. The response surface methodology (RSM) was adopted during the investigation and the contribution of feed rate (65.31%) was found as the dominant factor, followed by spindle speed (35.83%) and drill diameter (10.72%) to influence the delamination factor of hybrid composites. The grey relation analysis was further applied to the experimental results to rank the experiments. Scanning electron microscopy was used to examine the fractured surface of tested samples and delamination damage caused by drilling operations. The developed composites offered a maximum tensile strength (34.3 MPa), impact strength (11.13 J) and flexural strength (23.91 MPa) observed in the hybrid composites for a reinforcement combination of 5% nettle and 15% grewia optiva fibers. The prediction models developed by RSM and artificial neural network (ANN) were matched with the investigated results and ANN was noticed to be more accurate than the RSM. The research work will be beneficial for the industries involved in the development of structural panels reinforced with nettle and grewia optiva fibers.