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Failure Analysis of Austenitic Stainless Steel Implant Screws and Prospection of Chemical Composition Using Artificial Intelligence

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World Journal of Engineering and Technology 2022年第1期10卷 98-118页

作者： Alfonso Monzamodeth Román-Sedano bernardo campillo Fermín Castillo Osvaldo Flores Facultad de Química Universidad Nacional Autónoma de México Ciudad de México México Instituto 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.

关键词： Implant Screw Failure Analysis Genetic Algorithm Artificial Neural Network Austenitic Stainless Steel

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Effect of initial microstructure on austenite formation kinetics in high-strength experimental microalloyed steels

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International Journal of Minerals,Metallurgy and Materials 2015年第12期22卷 1304-1312页

作者： Edgar López-Martínez Octavio Vázquez-Gómez Héctor Javier Vergara-Hernández bernardo campillo Faculty of Chemistry Metallurgical Engineering Department Autonomous National University of Mexico National Council for Science and Technology Metallurgy Science Postgraduate Programme Morelia Institute of Technology Institute of Physical Sciences Autonomous National University of Mexico

Austenite formation kinetics in two high-strength experimental microalloyed steels with different initial microstructures comprising bainite-martensite and ferrite-martensite/austenite microconstituents was studied during continuous heating by dilatometric analysis. Austenite formation occurred in two steps：（1） carbide dissolution and precipitation and （2） transformation of residual ferrite to austenite. Dilatometric analysis was used to determine the critical temperatures of austenite formation and continuous heating transformation diagrams for heating rates ranging from 0.03°C.s^-1 to 0.67°C.s^-1. The austenite volume fraction was fitted using the Johnson-Mehl-Avrami-Kolmogorov equation to determine the kinetic parameters k and n as functions of the heating rate. Both n and k parameters increased with increasing heat- ing rate, which suggests an increase in the nucleation and growth rates of austenite. The activation energy of austenite formation was determined by the Kissinger method. Two activation energies were associated with each of the two austenite formation steps. In the first step, the austenite growth rate was controlled by carbon diffusion from carbide dissolution and precipitation; in the second step, it was controlled by the dissolution of residual ferrite to austenite.

关键词： non-isothermal kinetics microalloyed steel austenite heating dilatometry

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