Mechanical properties of friction induced nanocrystalline pearlitic steel.

Nanocrystalline structured variants of commercially available alloys have shown potential for boosting the mechanical properties of these materials, leading to a reduction in waste and thereby retaining feasible supply chains. One approach towards achieving these nanostructures resides in frictional treatments on manufactured parts, leading to differential refinement of the surface structure as compared to the bulk material. In this work the machining method is considered to be a testing platform for the formation and study of frictional nanostructured steel, assembly of which is stabilized by fast cooling of the produced chip.

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals.

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines.