CHIP FORMATION DURING THERMAL FRICTION TURN-MILLING

This paper presents the results of chip formation studies in the processing of 30KhGSA steel by thermofriction turn-milling. When studying the process in this work there are presented the results of studying chip formation when the processing of chip formation there is used the metallographic method. Chip root area investigated. The dependence of the chip shrinkage coefficient on the cutting speed and feed was also investigated. It is established that with increasing supply S the value of the chip shrinkage coefficient K decreases. The higher the chip shrinkage factor, the more work will be required to cut the chips and the more complex the processing process.

Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions

In this study, we investigate the technical application of the regularized regression method Lasso for identifying systematic biases in decadal precipitation predictions from a high-resolution regional climate model (CCLM) for Europe. The Lasso approach is quite novel in climatological research. We apply Lasso to observed precipitation and a large number of predictors related to precipitation derived from a training simulation, and transfer the trained Lasso regression model to a virtual forecast simulation for testing. Derived predictors from the model include local predictors at a given grid box and EOF predictors that describe large-scale patterns of variability for the same simulated variables. A major added value of the Lasso function is the variation of the so-called shrinkage factor and its ability in eliminating irrelevant predictors and avoiding overfitting. Among 18 different settings, an optimal shrinkage factor is identified that indicates a robust relationship between predictand and predictors. It turned out that large-scale patterns as represented by the EOF predictors outperform local predictors. The bias adjustment using the Lasso approach mainly improves the seasonal cycle of the precipitation prediction and, hence, improves the phase relationship and reduces the root-mean-square error between model prediction and observations. Another goal of the study pertains to the comparison of the Lasso performance with classical model output statistics and with a bivariate bias correction approach. In fact, Lasso is characterized by a similar and regionally higher skill than classical approaches of model bias correction. In addition, it is computationally less expensive. Therefore, we see a large potential for the application of the Lasso algorithm in a wider range of climatological applications when it comes to regression-based statistical transfer functions in statistical downscaling and model bias adjustment.

Definition of cutting force constituents at turning depending on stress-strain properties of material worked

Cited in the reference literature, empirical formula is usually acceptable for standard materials processed. When new materials have to repeat the experiments again. The article presents the analytical relationships derived from the generalization of the work of Russian and American scientists, in particular: VF Bobrov, NI Lvov, VS Kushner, a Merchant ME, Astakhov, V.P. These relationships relate the values of the components of the cutting force with time and the actual yield strength of the material being processed, making it much easier to calculate the components of the experiment of cutting force in turning based on the radius of curvature of the cutting edge and tool wear on the rear surface. Shrinkage factor chips taken approximately equal to one. Experimental verification showed acceptability found dependences for practical use, particularly important in the processing of materials of new brands.