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The rapid wear and premature failure of the cutting tool are prone to happen due to increased forces during machining difficult-to-cut materials such as Inconel 718. The application of alternative toolpath such as trochoidal milling has significantly improved tool life and reduced the overall cycle time of the process. The wear pattern of the tool has a direct impact on the cutting forces, which increases with tool deterioration. The cutting forces in milling are modeled through the mechanistic force model and can be designated through a set of force coefficients, i.e. cutting and edge representing the shearing and ploughing phenomenon of metal removal. It has been established in the literature that tool wear has a considerable effect on the value of edge force coefficients. This paper aims to determine the in-process edge force coefficients for the trochoidal toolpath and correlates them with the corresponding flank wear area. The proposed correlation will further assist in predicting the level of flank wear area based on the force values in trochoidal milling. 

Nickel-based superalloys belong to a category of material employed for extreme conditions and exhibit high strength properties at elevated temperatures that result in poor machinability. Machining such di cult-to-cut materials like Inconel 718 leads to a high rate of tool wear, and therefore trochoidal milling toolpath is used to improve productivity and tool life. The current study analyzes the evolution of the flank wear area of the tool during trochoidal milling of Inconel 718 using an image processing methodology. It is attempted by performing experimental studies until tool failure occurs at several cutting conditions. The machining is performed through several iterations on an identical cutting path, and the number of iterations to failure is recorded. The microstructural image of a flank wear area is captured upon each iteration and processed using an image processing algorithm. It is realized that the evaluation of flank wear area can be an e ective parameter to analyze tool wear. Also, the image processing methodology works e ectively and can be extended during real-time machining. 

We consider the question of learning the natural parameters of a k parameter \textit{minimal} exponential family from i.i.d. samples in a computationally and statistically efficient manner. We focus on the setting where the support as well as the natural parameters are appropriately bounded. While the traditional maximum likelihood estimator for this class of exponential family is consistent, asymptotically normal, and asymptotically efficient, evaluating it is computationally hard. In this work, we propose a computationally efficient estimator that is consistent as well as asymptotically normal under mild conditions. We provide finite sample guarantees to achieve an l2 error of α in the parameter estimation with sample complexity O(poly(k/α)) and computational complexity O(poly(k/α)). To establish these results, we show that, at the population level, our method can be viewed as the maximum likelihood estimation of a re-parameterized distribution belonging to the same class of exponential family. 

We consider learning a sparse pairwise Markov Random Field (MRF) with continuous valued variables from i.i.d samples. We adapt the algorithm of Vuffray et al. (2019) to this setting and provide finite- sample analysis revealing sample complexity scaling logarithmically with the number of variables, as in the discrete and Gaussian settings. Our approach is applicable to a large class of pairwise MRFs with continuous variables and also has desirable asymptotic properties, including consistency and normality under mild conditions. Further, we establish that the population version of the optimization criterion employed by Vuffray et al. (2019) can be interpreted as local maximum likelihood estimation (MLE). As part of our analysis, we introduce a robust variation of sparse linear regression à la Lasso, which may be of interest in its own right. 

Heart failure (HF) is a major cause of morbidity and mortality, and one of the leading causes of hospitalization. Early detection of HF symptoms and pro-active management may reduce adverse events. Passive accelerometer data from smartphones may reflect behavioral and physiologic changes due to HF and thus could enable prediction of HF decompensation.