Prediction of Tribological Characteristics of Biomaterials Using Response Surface MethodologyPages 258-265
M. Arulkumar Abstract:
Biomaterials are increasingly important in orthopaedic implants, particularly in Total Joint Replacement (TJR) procedures. Research has been conducted to identify suitable biomaterials for different joint replacements, with Total Hip Joint (THJ) and Total Knee Joint (TKJ) being the most common. Promising biomaterials for TJR include metals such as Ti6Al4V, CoCrMo, and 316L SS, ceramics such as Al₂O₃, and polymers such as UHMWPE. These materials are valued for their high strength-to-weight ratio, biocompatibility, corrosion resistance, and wear resistance. In this study, Response Surface Methodology (RSM) was used to predict the tribological properties of these biomaterials. Experimental trials were conducted using a pin-on-disc tribometer to validate the RSM-based prediction of tribological properties by varying parameters such as applied load, sliding distance, and sliding velocity. The predicted values were then compared with experimental results, showing that RSM could accurately predict the friction behaviour of the biomaterials with 95% accuracy. These findings demonstrate the potential of RSM as a predictive tool in biomaterials research, which can contribute to optimising tribological properties in medical implants and devices. This could lead to advancements in the design and application of orthopaedic implants.
Keywords: Machine learning,
Biomaterials,
Response surface methodology,
Sliding dry wear test,
Coefficient of friction
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