Study on Machinability of Nano Date Palm Particulate Bio-Composites

Recently, Natural Fiber Reinforced Composites (NFRCs) have created significant opportunities in present markets because of their biodegradability, low density, sustainability, and satisfactory mechanical properties as engineering materials that are rapidly developed for several applications. NFRCs have been studied by several researchers by analyzing the effect of adding various types of natural fibers extracted from diverse sources to polymeric matrices materials using alternative methods. Furthermore, in real-industrial applications, the near-net shape of products requires going through various procedures, such as milling, drilling, turning, etc., that are necessary during manufacturing phases before the final assembly. Despite the importance of this, there is limited research work that has investigated issues associated with NFRCs machinability. Therefore, this study first aims to determine the machinability performance of a newly Nano bio-composite material to ensure its suitability for industrial applications. Moreover, it will develop an estimation model to predict the machining output responses during turning operation, which will assist manufacturers in producing their products effectively. This study will use experimental and statistical approaches to investigate the machining performance of Nano natural filler bio-composites. It will also include in-depth examinations of the inputs that determine the quality of such materials' machinability. It will suggest how to select the process parameters required to achieve better manufacturing quality and productivity during dry turning. When studying the machinability, three input parameters for turning operation will be considered, which are feed rate, depth of cut, and spindle speed. The performance of machinability will be determined by considering the following output responses, surface finish, material removal rate, and roundness. Therefore, Response Surface Methodology (RSM) will be applied to optimize the machining input parameters. Further, Artificial Neural Network (ANN) approach will be used to predict the output responses for given input machining parameters.