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Cobalt-based alloys are extensively used in orthopedic applications for joint replacements due to their wear and corrosion resistance. Corrosion, however, is often associated with fatigue failure in these orthopedic devices. In this study, the effect of boron addition on the corrosion behavior of CoCrMo alloys was studied using linear polarization resistance, potentiodynamic polarization curves, electrochemical impedance spectroscopy, and cyclic voltammetry. The samples were analyzed under as-cast and heat treatment conditions after 21 days of immersion in phosphate-buffered saline (PBS) solution at 37 °C. The boron addition increased the particle content, while the heat treatment promoted enlargement and even distribution of the precipitates throughout the structure. The corrosion resistance was improved by both boron and heat treatments. The best performance was observed for a heat-treated alloy having a very small amount of boron, which had an increased resistance to corrosive attack. Such behavior was attributed to the homogenized microstructure achieved by boron and heat treatment that helped to form a stable passive layer of chromium oxide which endured the 21 days of immersion.

Cobalt-based alloys are widely used in the manufacture of joint prostheses. In this study, the effect of boron additions and heat treatment on the ASTM F75 was evaluated by rotating bending fatigue. The boron ranged from 0.06–1 wt %. The alloys were tested in as-cast and heat-treated conditions. In the as-cast condition, the infinite life was observed at 380 MPa, improving to 433–615 MPa according to the amount of boron added. In the heat treatment condition, the fatigue resistance was improved only in the base alloy. The addition of 0.06 wt % boron and heat treatment led to the same resistance as in the as-cast condition. Adding large amounts of boron combined with heat treatment diminished the fatigue limit. The fracture analysis revealed primarily brittle behaviour with some ductile features even on the same sample; only the heat-treated alloy with 0.06 wt % boron was clearly ductile. This alloy also exhibited notably better toughness to crack propagation.

Automotive components manufacturers use the 5160 steel in leaf and coil springs. The industrial heat treatment process consists in austenitizing followed by the oil quenching and tempering process. Typically, compressive residual stresses are induced by shot peening on the surface of automotive springs to bestow compressive residual stresses that improve the fatigue resistance and increase the service life of the parts after heat treatment. In this work, a high-speed quenching was used to achieve compressive residual stresses on the surface of AISI/SAE 5160 steel samples by producing high thermal gradients and interrupting the cooling in order to generate a case-core microstructure. A special laboratory equipment was designed and built, which uses water as the quenching media in a high-speed water chamber. The severity of the cooling was characterized with embedded thermocouples to obtain the cooling curves at different depths from the surface. Samples were cooled for various times to produce different hardened case depths. The microstructure of specimens was observed with a scanning electron microscope (SEM). X-ray diffraction (XRD) was used to estimate the magnitude of residual stresses on the surface of the specimens. Compressive residual stresses at the surface and sub-surface of about −700 MPa were obtained.

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This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

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