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The tribo-electrochemical behavior of two new martensitic stainless steels in a 3% NaCl solution has been investigated. Different electrochemical and surface analysis techniques (Scanning Electron Microscopy, Focused Ion Beam) were discussed to analyse the influence of the effect of the electrochemical conditions on friction and wear, and to elucidate involved wear mechanisms (plastic deformation, plastic shakedown and low-cycle fatigue). The selected stainless steels degrade through a delamination type of wear mechanism. The effects of the applied potential on wear are related to the formation of a passive film which alters the mechanical behavior of the surface and subsurface of the materials to promote wear. A coefficient of friction below 0.6 promotes nanowear, and a transition was observed when the coefficient of friction exceeded that value.

This paper focuses on the tribological characterization of new martensitic stainless steels by two different tribological methods (scratch and dry wear tests) and their comparison to the austenitic standard stainless steel AISI 316L. The scratch test allows obtaining critical loads, scratch friction coefficients, scratch hardness and specific scratch wear rate, and the dry wear test to quantify wear volumes. The damage has been studied by ex situ scanning electron microscopy. Wear resistance was related to the hardness and the microstructure of the studied materials, where martensitic stainless steels exhibit higher scratch wear resistance than the austenitic one, but higher hardness of the martensitic alloys did not give better scratch resistance when comparing with themselves. It has been proved it is possible to evaluate the scratch wear resistance of bulk stainless steels using scratch test. The austenitic material presented lower wear volume than the martensitic ones after the dry wear test due to phase transformation and the hardening during sliding.

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The main objective of this paper is to develop a signal processing strategy using vibratory signals in order to provide an efficient tool wear monitoring system able to increase machining performance. The method is based on the changes in the vibration signatures acquired during the turning operation over the tool life. Several signal processing techniques based on time and frequency domain analysis are proposed in order to extract a large number of indicators of the cutting tool state as variance, kurtosis, skewness, and coherence function. In this work, one of the innovative results is the tracking of tool wear by variance and coherence estimation. All of these indicators are correlated and validated by using white light interferometry measurements. This paper focuses on the technologies used in monitoring conventional cutting operations and presents important findings related to this field.