EXPERIMENTAL INVESTIGATION OF QUALITY METRICS FORMATION PROCESS DURING PLASTIC MILLING

Composite materials are widely used in mechanical engineering, but at edge cutting machining, in particular, during milling these materials a number of peculiarities arise which must be taken into account at the definition of cutting modes and design-geometrical parameters of cutters. Besides, new composite materials machining does not allow using effectively the recommendations developed earlier. In such a way, to solve such a problem it is necessary to carry out experimental investigations on the analysis of the influence of milling mode characteristics and design-geometrical of a tool upon values of roughness of a surface processed and tool wear. As a cutter for investigations there were taken hardmetal endmilling cutters of TC-8 (tungstencobalt) type, the experimental samples – pipes made of composite material with oblique longi-tudinal-transverse fiber winding (OLTFW). As varied parameters were adopted cutting modes: cutting speed V, m/min, feed S, mm/tooth and milling depth t, mm. During the experiments were controlled the following parameters: tool wear Δ, mkm, roughness of the surface Ra, mkm and a depth of a faulty layer h, mkm. To carry out the experiments there was offered an original design of an assembly milling cutter which allows defining in an experimental way optimum geo-metrical parameters of a tools to achieve output milling parameters specified. On the basis of experiments data there are obtained dependences allowing the estimate of parameter modes influence upon the period of cutter duration at the same time a temperature is affected mostly by a milling depth and a feed on a tooth affects the wear of an end flank.

Quantification of Cloud Microphysical Parameterization Uncertainty Using Radar Reflectivity

Uncertainty in cloud microphysical parameterization—a leading order contribution to numerical weather prediction error—is estimated using a Markov chain Monte Carlo (MCMC) algorithm. An inversion is performed on 10 microphysical parameters using radar reflectivity observations with vertically covarying error as the likelihood constraint. An idealized 1D atmospheric column model with prescribed forcing is used to simulate the microphysical behavior of a midlatitude squall line. Novel diagnostics are employed for the probabilistic investigation of individual microphysical process behavior vis-à-vis parameter uncertainty. Uncertainty in the microphysical parameterization is presented via posterior probability density functions (PDFs) of parameters, observations, and microphysical processes. The results of this study show that radar reflectivity observations, as expected, provide a much stronger constraint on microphysical parameters than column-integral observations, in most cases reducing both the variance and bias in the maximum likelihood estimate of parameter values. This highlights the enhanced potential of radar reflectivity observations to provide information about microphysical processes within convective storm systems despite the presence of strongly nonlinear relationships within the microphysics model. The probabilistic analysis of parameterization uncertainty in terms of both parameter and process activity PDFs suggest the prospect of a stochastic representation of microphysical parameterization uncertainty—specifically the results indicate that error may be more easily represented and estimated by microphysical process uncertainty rather than microphysical parameter uncertainty. In addition, these new methods of analysis allow for a detailed investigation of the full nonlinear and multivariate relationships between microphysical parameters, microphysical processes, and radar observations.