Measurement, analysis and metrologic evaluation of physical magnitudes and geometrical features that show alteration with regard to friction in hip prosthesis

Hip implants that work under load of almost eightfold of body weight during daily activities are subjected to severe friction. Experiments realized in both in-vivo medium and laboratory conditions show alteration of specifications of materials in hip joint and lubricating liquid in the environment caused by temperature rise which is a consequence of friction. Joint connector surfaces are subjected to separation and creep with the rise in temperature and tissue that surrounds implant is harmed. As a consequence, implant service life ends before anticipated lifespan because of aseptic loosening. Although there have been extensive literature research that aim on enhancing the wear and friction behaviors of implant materials, desired success on prolongation of service life of joint connector members has not been achieved. Measurement of friction and frictional temperature rise that occur between articulating surfaces and understanding wear behaviors of materials will help construct more successful artificial joints in terms of design, material, lubrication and similar factors. This study was based on this aim and wear and frictional behaviors of ultra high molecular weight polyethylene (UHMWPE), cross-linked UHMWPEPE, vitamin E blended UHMWPE and vitanin E blended and cross-linked UHMWPE acetabular insert materials and CoCrMo femur head material have been researched. Wear behaviors of materials have been determined using standard pin-on-disc test device. Frictional temperature rise on material surfaces has been measured on custom made experimental setup, which was designed and manufactured in order to simulate walking cycle of hip joint. In addition, the effects of machined surface dimples with different diameters on acetabular insert material surfaces on wear and friction behavior of materials have been researched. Results have been modeled mathematically and using artificial neural networks.