Predictive Modeling and Correlated Response Optimization of Polymethylmethacrylate (PMMA)-Based Bio-Nano-Composite Material Using a Hybrid Module
Abstract
Polymethylmethacrylate (PMMA) is commonly known as bone cement, having good biocompatibility, mechanical qualities. It is extensively used in the biomedical sector as a synthetic bone material, orthopedic surgery and dental applications. However, some primary machining is required to achieve the tailored shape, size and finish before application in the human body. This study focuses on the machining (drilling) behavior of the developed PMMA-based Hydroxyapatite (PMMA-HA) bio-nano- composites. The machining efficiency and parametric control were estimated using a combined principal component analysis (PCA) module and evaluation based on distance from average solution (EDAS). The Hydroxyapatite (HA) weight percentage (wt.%), spindle speed (SPEED) and tool material (TOOL) viz. HSS, Carbide and TiAlN are chosen according to the Taguchi-based experimental array. The objective is to get the best possible machining responses, such as the material removal rate (MRR), mean surface roughness (Ra) and circularity error (Cer) using the PCA-EDAS hybrid module. The optimal condition is found as the HSS drilling bit, 10%wt.%, SPEED-1428rpm with an improvement of 30.53%, 21.15% and 41.9% in MRR, Ra and C-ERROR, respectively. The microstructural investigation scanning electron microscope (SEM) shows the excellent morphology and quality of the drilled hole in the proposed composites. Also, an X-ray diffraction (XRD) analysis of the prepared sample was done to ensure the proper reinforcement. The flexural test shows a significant expansion in the mechanical property due to the presence of HA in PMMA
