Thermo-mechanical properties prediction of Ni-reinforced Al2O3 composites using micro-mechanics based representative volume elements.

For effective cutting tool inserts that absorb thermal shock at varying temperature gradients, improved thermal conductivity and toughness are required. In addition, parameters such as the coefficient of thermal expansion must be kept within a reasonable range. This work presents a novel material design framework based on a multi-scale modeling approach that proposes nickel (Ni)-reinforced alumina (Al2O3) composites to tailor the mechanical and thermal properties required for ceramic cutting tools by considering numerous composite parameters. The representative volume elements (RVEs) are generated using the DREAM.3D software program and the output is imported into a commercial finite element software ABAQUS. The RVEs which contain multiple Ni particles with varying porosity and volume fractions are used to predict the effective thermal and mechanical properties using the computational homogenization methods under appropriate boundary conditions (BCs). The RVE framework is validated by the sintering of Al2O3-Ni composites in various compositions. The predicted numerical results agree well with the measured thermal and structural properties. The properties predicted by the numerical model are comparable with those obtained using the rules of mixtures and SwiftComp, as well as the Fast Fourier Transform (FFT) based computational homogenization method. The results show that the ABAQUS, SwiftComp and FFT results are fairly close to each other. The effects of porosity and Ni volume fraction on the mechanical and thermal properties are also investigated. It is observed that the mechanical properties and thermal conductivities decrease with the porosity, while the thermal expansion remains unaffected. The proposed integrated modeling and empirical approach could facilitate the development of unique Al2O3-metal composites with the desired thermal and mechanical properties for ceramic cutting inserts.

Dataset and methodology on identification and correlation of secondary carbides with microstructure, wear mechanism, and tool performance for different CERMET grades during high-speed dry finish turning of AISI 304 stainless steel.

The aim of this research is to utilize reverse engineering approach for the identification of the elements and phases available in the commercial CERMET inserts with the help of characterization techniques such as Scanning Electron Microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), and X-Ray Deposition (XRD). Four commercial CERMET inserts were investigated in this research work, and the effect of the composition and phases are related to its tool wear mechanism and performance. Each CERMET insert is used to perform a turning process on a CNC lathe for machining stainless steel (SS) under the dry condition at a fixed cutting length interval. Once it completes machining for a fixed cutting length, the CERMET insert is taken out to investigate its wear mechanism with the help of SEM, EDS, XRD and using a focus-variation microscope (Alicona). A correlation analysis is performed to relate progressive tool wear mechanisms with elements and their relevant phases of various carbides. The approach of correlating wear property with the phase content will contribute to the understanding of the wear mechanism under such extreme machining conditions. It will serve as a reference for the improvement of the performance of these CERMET inserts for such harsh machining conditions by the development of protective coatings for these CERMET inserts based on the identification of the composition and phases that improves tool life and reduces wear. The data related research work can be found at "https://doi.org/10.1016/j.wear.2020.203285" [1].