Titanium and its alloys are widely used in prosthetic dentistry, due to their biocompatibility, excellent mechanical and anti-corrosion behavior. However, delayed fracture of dental prosthetics is frequently encountered. Mechanisms leading to fracture are not generic but are strongly related to the particular environmental (quality of biological fluids) and mechanical loading conditions (mastication habits, presence of prosthetic metallic/ceramic components) in the patients' oral cavity. In this study, a commercially pure titanium implant-screw system has failed after 15 years of operation in the oral cavity of an old female. The system was retrieved in three pieces: the upper part of the implant, part of the abutment screw, and the apical part of the implant to which a part of the screw was embedded. This is considered as a rare case, where the whole dental assembly was retrieved after fracture allowing the extensive fractographic analysis of the conjugate pieces and the establishment of a thorough in-vivo failure scenario. Scanning electron microscopy observations performed on all three retrieved parts indicated a synergistic effect of distinct mechanisms, which led to total failure under extrinsic common fatigue loading. The principal mechanism was the propagation of a main crack, which was previously initiated in the body of the implant and affected by a wedging mechanism due to Ca/P aggregates developed within the crack. Because of the strong fixation between the implant and the abutment screw, this main crack was transferred to the latter causing eventually total failure of the assembly.
The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels.
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