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Machining is a severe plastic deformation process, wherein the workpiece material is subjected to high deformation rates and temperatures. During metal machining, the dynamic recrystallization mechanism causes grain refinement into the sub-micron range. In this study, we investigate the microstructure evolution of oxygen-free high conductivity copper (OFHC Cu) subject to a machining process where the cutting speed and rake angle are controlled to manipulate the process strain, strain rate, and temperatures. Microstructures of the deformed chips are quantified using orientation imaging microscopy and novel statistical descriptors that capture the morphology and local lattice misorientations generated during the several mechanistic stages of the dynamic recrystallization process. Mechanical properties of the resulting chips are quantified using spherical nanoindentation protocols. A multiple output Gaussian process regression model is used to simultaneously model the structure-property evolution, which differs from more common approaches that establish such relationships sequentially. This modeling strategy is particularly attractive since it can flexibly provide both structure and property uncertainty estimates. In addition, the statistical modeling framework allows for the inclusion of multi-fidelity data. The statistical metrics utilized serve as efficient microstructure descriptors, which retain the physics of the observed structures without having to introduce ad hoc microstructure feature definitions. [ABSTRACT FROM AUTHOR]

Abstract Finite element based machining process models are used in research and industry for process design and optimization. These models require a constitutive description of the material behavior to accurately model and predict process responses such as cutting forces, temperatures, and residual stress. Calibration of these models to low-strain uniaxial dynamic compression experiments can be troublesome since the machining process generally imposes much larger strains than uniaxial compression. Calibration of finite element models directly to machining data is generally difficult since the models are computationally expensive and nonlinear optimization methods for estimating the unknown calibration parameters yield non-unique solutions and require many iterations. In this work we utilize a nonstationary Gaussian Process surrogate model to emulate the finite element response and calibrate to experimental orthogonal cutting tests using a Bayesian inference framework. We assume that the material yield behavior can be described by the Johnson-Cook material flow model. We find that the nonstationary Gaussian Process model is an good surrogate for the complex finite element model. Cutting forces measured from orthogonal tube turning experiments were used for calibration. Validation is performed using a separate response variable - the cut chip thickness. Calibration results illustrate a preference for material models with low hardening rates, which alleviates issues such as over-prediction of strain hardening behavior when using the Johnson-Cook material flow model. The Bayesian formulation also captures the uncertainty in the Johnson-Cook parameters, which can be used to quantify the uncertainty in the machining process responses. The methods presented here are general and can be used for more complex constitutive and tribological models for machining and other complex manufacturing processes. Highlights • Bayesian calibration used to infer flow stress directly from machining experiments. • Multi-output Gaussian process model used to emulate expensive finite element model. • Framework quantifies constitutive and tribological parameter uncertainties. • Approach is general and may be used beyond machining for other complex processes. [ABSTRACT FROM AUTHOR]

A composite dual phase internal state variable constitutive model was developed for Ti-6Al-4V. The proposed model includes diffusion assisted grain boundary sliding based physics in addition to a traditional slip-based plasticity. Influence of microstructure on the flow stress is introduced via dislocation density and mean grain size internal state variables. The dislocation density evolves according to a physics based law that considers dislocation nucleation and annihilation processes. Grain refinement is driven by dynamic recrystallization, which is modeled phenomenologically. The model is calibrated with uniaxial stress–strain data that ranges between quasi-static and dynamic rates across a wide range of temperatures. Validation against machining data shows that the model predicts chip segmentation frequency, machining forces, and tool temperatures reasonably well. The newly introduced grain boundary sliding physics was found to dominate deformation following sufficient grain refinement. This deformation mode provides softening at the constitutive level without the need for invoking damage based softening mechanisms. This physical interpretation is something that has not previously been explored in the machining literature. [ABSTRACT FROM AUTHOR]

Finite element simulation of machining is increasingly popular in both industry and academia. Simulation accuracy is, in part, critically dependent on identification of the appropriate material constitutive laws. Calibration and validation of the laws is typically limited to using uniaxial flow stress data and/or machining outputs such as forces, temperatures, and chip morphology features. The spatial distribution of material state (i.e. microstructure, mechanical properties, residual stresses) of the machined chip, which encodes path-dependent process information, is seldom utilized for model validation. In this paper we explore a complimentary validation technique that compares model predictions against measured spherical nanoindentation stress-strain curves. [ABSTRACT FROM AUTHOR]

The accuracy of cutting simulations depends on the knowledge of micro-scale physics included in the constitutive and microstructure evolution models of the cutting process. This paper presents an enhanced physically based material model that accounts for microstructure evolution induced flow softening due to the inverse Hall–Petch effect below a critical grain size. The model's ability to simulate segmented chip formation and grain refinement in the shear bands produced in orthogonal cutting of commercially pure titanium is evaluated through finite element simulations and experiments. Results show good prediction accuracy for the cutting and thrust forces, chip morphology, and segmentation frequency. [ABSTRACT FROM AUTHOR]

Machining is a severe plastic deformation process that subjects materials to high rates of deformation and elevated temperatures. Dynamic recrystallization during severe plastic deformation drives grain refinement into the sub-micron range but the ductility and thermal stability of these structures is poor. In contrast, bimodal microstructures consisting of both fine and coarse grains have been shown to exhibit attractive strength and ductility properties at these temperatures. In this paper, we study the process–structure–property relationships for pure copper subject to a machining process where the cutting speeds are varied. Further, we investigate the role of thermal effects by varying the post-deformation cooling rates. Microstructures generated under these deformation conditions are quantified using angularly resolved chord length statistics. The derived metrics are found to be robust descriptors of the studied microstructures as they automatically capture complex features such as unimodal and bimodal distributions of grain structure, grain constituent length scales, and morphological anisotropy induced by shear deformation. The uniaxial equivalent yield strength of the generated structures are estimated using spherical nanoindentation and inverse modeling techniques. Finally, we present a methodology for identifying the constrained property-process inverse mapping for machining using a Bayesian framework and the established forward process–structure–property mapping. [ABSTRACT FROM AUTHOR]