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.
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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