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The wear behavior of cutting tools is highly complex due to combined thermal, mechanical, and chemical loads. As a result, most current tool-wear prediction methodologies are either empirical or highly oversimplified analytical or numerical models of stable abrasive and diffusive wear mechanisms. To predict the complex physics of catastrophic tool edge chipping, which in practice bounds feasible process parameters, this manuscript presents a novel approach for in-situ characterization of tool edge fatigue loads and probabilistic prediction of the likelihood of time to fracture. The results of the analysis suggest encouraging possibilities for more physics-informed and data-driven process design. 

The formation of burrs is among the most significant factors affecting quality and productivity in machining. Burrs are a negative byproduct of machining processes that are difficult to avoid because of a limited understanding of the complex burr formation mechanisms in relation to cutting conditions, including both process parameters and tool condition. Thus, the objective of this work was to characterize burr formation under finish machining conditions via a high-speed, high-resolution in-situ experimental method. Various parameters pertaining to burr geometry such as height, thickness, and initial negative shear angle were measured both during and after cutting. Results showed that varying the conditions of uncut chip thickness, tool-wear, and cutting speed all have a significant effect on burr formation, although certain burr metrics were found to be insensitive with respect to different process conditions because the difference was statistically insignificant. This study provides new insights into the relationships between the workpiece material’s microstructure, machining parameters, and tool condition on both crack formation and propagation/plasticity during burr formation. Using digital image correlation (DIC) and a physics-based process model not previously utilized for burr formation analysis, the displacement and corresponding flow stress were calculated at the exit burr root location. This novel semi-analytical approach revealed that the normalized stress at the exit burr root was approximately equal to the flow stress for a variety of different conditions, indicating the potential for model-based prediction of burr formation mechanics. Finally, this study investigates factors that influence fracture evolution during exit burr formation. It was found that negative exit burrs are a direct result of high strain rate and high uncut chip thickness, which was expected, but also a microstructural size effect and a tool-wear effect, neither of which have been previously reported. By harnessing ultra-high-speed imaging and advanced optical microscopy techniques, this manuscript deals with the fundamentals of burr formation, including new insights into material response at the grain-scale to the loads imposed with both sharp and worn tools. 

Nickel-based superalloys (Ni-alloys) are widely used in flight-critical aeroengine components because of their excellent material properties at high temperatures such as yield strength, ductility, and creep resistance. However, these desirable high-temperature properties also make Ni-alloys very difficult to machine. This paper provides an overview and benchmarking of various constitutive models to provide the process modeling community with an objective comparison between various calibrated material models, to increase the accuracy of process model predictions for machining of Ni-alloys. Various studies involving the Johnson-Cook model and the calibration of its constants in finite element simulations are discussed. Significant discrepancies exist between researchers' approaches to calibrating constitutive models. Moreover, this paper provides a comprehensive overview of pedigreed physical material properties for a range of Ni-alloys. In this context, the variation of thermal properties and thermally induced stresses over machining temperature regimes are modeled for a variety of Ni-alloys. The chemical compositions and applications for a range of relevant Ni-alloys are also explored. Overall, this manuscript identifies the need for more comprehensive analysis and process-specific characterization of thermomechanical properties for difficult-to-machine Ni-alloys to improve machining performance and aeroengine component quality. 

Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.