Stochastic Modeling of the Interaction of Tool Wear and the Machining Affected Zone in Nickel-Based Superalloys and Application in Dynamic Stability

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In many industrial sectors, e.g., the energy or aerospace industry, Nickel-based alloys are commonly used for various applications due to their high temperature resistance. Unfortunately, this advantage leads to a poor machinability of the material, resulting in high process forces and significant tool wear in milling processes. Due to the complex dependencies between process parameters, process forces, tool wear, and especially the material affected zone when machining Nickel-based alloys, a simulation-based analysis of the processes is required in order to optimize the machining process and to increase the quality of the manufactured parts. Based on a detailed analysis of these dependencies, new models for predicting process forces, tool wear, process dynamics, and the resulting surface topographies will be developed in this project.A special focus of these investigations is set on the analysis and description of the stochastic nature of the tool wear. To analyze the wear and its influence on the process forces systematically, inserts will be worn artificially in order to ensure reproducible process states with defined tool wear, which corresponds to the wear patterns occurring in experiments. These inserts with contrived tool wear will be used in orthogonal cutting tests on a fundamental chip formation machine with controllable process conditions in order to analysis the resulting process forces and to develop appropriate models, before conducting milling experiments on a 5-axis machining center.In addition to the process forces, the tool wear has a significant influence on the process dynamics and the machined surface topographies. In order to predict these surface topographies with respect to tool wear, a model of the worn cutting edges based on triangle meshes will be developed and used to calculate a simplified sweep volume for an efficient simulation of the cutting process. In order to take wear-dependent effects into account when optimizing machining processes, e.g., by a specific design of trochoidal milling paths, the developed models will be integrated into a geometric physics-based simulation system, which can be used to analyze complex machining processes. Taking the stochastic nature of the process into account, probability distributions of the process forces and resulting tool wear can be modeled. Based on the developed models, the extended simulation system will be used to calculate fuzzy stability charts in order optimize milling processes.This project is a collaboration between the Clemson University in South Carolina, USA (funded by the NSF) and the TU Dortmund University.