Author(s)

Alfonso Monzamodeth Román-Sedano^1*, Bernardo Campillo^1,2, Fermín Castillo², Osvaldo Flores^1,2

¹Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México, México.
²Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca Morelos, México.

In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screws were extracted due to the observation of slight superficial red rust colorizing on one of the screw implants, visual evidence of probable screw rusting. From the same implant, another screw was extracted simultaneously without visual evidence of rusting. In order to characterize and analyze the different behavior of both screws, the chemical composition was characterized by atomic absorption and energy dispersive X-ray spectroscopy (EDS) coupled to a scanning electron microscope (SEM). Also, the screws were studied by metallography, optical microscopy (OM), Vickers microhardness tests, and SEM analysis. On the other hand, a prospection for alloy chemical composition limits of these types of implants was performed based on the Schaeffler-Delong diagram and the ASTM F-138 standard. To analyze the effect of the chemical composition, heat treatment, microstructure, pitting resistance equivalent number (PRE) and stacking fault energy (SFE), a genetic algorithm (GA) and an artificial neural network (ANN) were used. In accordance with the elemental analysis, the surgical screws do not fulfill the ranges of the chemical composition established by the ASTM F-138 standard. Furthermore, there were found differences between the microstructures of the screws. In regard to the prospection, the results of GA and ANN support the proposed chemical composition region on the Schaeffler-Delong diagram. The corrosion failure was associated with severe plastic deformation and the presence of precipitates. The proposal can minimize the cause of failures in these types of austenitic stainless steel implants.

Implant Screw, Failure Analysis, Genetic Algorithm, Artificial Neural Network, Austenitic Stainless Steel

Román-Sedano, A. , Campillo, B. , Castillo, F. and Flores, O. (2022) Failure Analysis of Austenitic Stainless Steel Implant Screws and Prospection of Chemical Composition Using Artificial Intelligence. World Journal of Engineering and Technology, 10, 98-118. doi: 10.4236/wjet.2022.101006.