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The titanium alloy Ti-6Al-4V represents a significant metal portion of state-of-the-art aircraft structural and engine components. When critical structural components in the aerospace industry are manufactured with the objective to reach high reliability levels, surface integrity is one of the most relevant parameters used for evaluating the quality of machined surfaces. The residual stresses and the surface alteration induced by machining titanium alloys are critical due to safety and sustainability issues. In this paper, a series of end milling experiments was conducted to comprehensively characterize the surface integrity at various milling conditions. The experimental results have shown that the surface roughness value increases with the feed and the cutting velocity. However, the residual stress state in the surface layer zone is influenced by the variation of the process control variables. Here, compressive residual stresses occur both in cutting and in feed direction. In addition, a new type of sensory tool holder is presented, which should enable the indirect measurement of residual stresses during the milling process.

The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm.