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The effects of process parameters on micro-texture evolution of Al alloy 7075 in machining under extreme conditions were investigated. For the first time, the process parameters in machining are related to the micro-texture evolution below the machined surface, through a computational modeling framework verified by robust experimentation. Finite element analyses (FEA) were performed to obtain the mechanical loads that the material experiences due to chip removal. The viscoplastic self-consistent (VPSC) approach was used to follow the evolution in texture due to the applied loads. The most outstanding texture component, which was seen in the case of increased feed rate, is closely followed by the texture evolution model. The observed discrepancy in experimental and computational results of texture evolution in a few studied cases is believed to be due to the activation of other microstructural evolution phenomena rather than dislocation slip, as a result of the ultra-high levels of exerted strain and strain rates to the machined surface. [ABSTRACT FROM AUTHOR]

This work investigates the effects of turning process parameters on recrystallization behavior in Al alloy 7075. To realize this purpose, samples were machined under different cutting speeds and material feed rates at two extreme levels. Microscopy imaging reveals that activation of dynamic recrystallization or grain growth depends on the combination of applied cutting parameters. Increasing the cutting speed intensifies recrystallization, while the feed rate governs the grain growth. Adjusting the cutting parameters enables one to obtain a desired average grain size below the machined surface, up to a ~180 μm depth. The average grain size of the initial material was 31.6 μm. The imposed processing parameters successfully yielded average grain sizes in the range from 19 to 44 μm. Additionally, a computational framework work consisting of finite-element analysis (FEA) coupled with kinetic-based modeling of recrystallization was developed, which is capable of following the trend of change in the average grain size and acceptably predicts the evolved average grain size. [ABSTRACT FROM AUTHOR]