Optimization of surface finish in end milling Inconel 718

Computerized machinability data systems are essential for the selection of optimum cutting conditions during process planning, and they form an important component in the implementation of computer integrated manufacturing (CIM) systems.This paper presents a study of the development of a surface roughness model for turning grey cast iron (154 BHN), using tipped carbide tools under dry conditions and a constant depth of cut. The model is developed in terms of cutting speed, feedrate and nose radius of the cutting tool. These variables were investigated using design of experiments and utilization of the response surface methodology (RSM). A first-order equation covering the speed range 110–350 m/min and a second-order generation equation covering the speed range 80–495 m/min are presented. The results are given in terms of mean values with confidence intervals.

Surface Roughness Model for Turning Grey Cast Iron (154 BHN)

Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review

A Bayesian semiparametric proportional hazards model is presented to describe the failure behavior of machine tools. The semiparametric setup is introduced using a mixture of Dirichlet processes prior. A Bayesian analysis is performed on real machine tool failure data using the semiparametric setup, and development of optimal replacement strategies are discussed. The results of the semiparametric analysis and the replacement policies are compared with those under a parametric model.

/pdf/a-bayesian-semiparametric-analysis-of-the-reliability-and-30ejx81l1e.pdf

A Bayesian Semiparametric Analysis of the Reliability and Maintenance of Machine Tools

In this article we present a proportional hazards model for describing machine tool failures. The model enables us to describe the effect of aging as well as the effect of the machining environment on tool life. We discuss inference for the model and show that computable results can be obtained in a fully Bayesian analysis by using approximation techniques. We illustrate the usefulness of our model by applying it to some actual data on machine tool failures.

Assessment of machine tool reliability using a proportional hazards model

Mathematical model type machinability data base systems require suitable model building procedures to estimate the model parameters. The estimation procedure should be capable of using subjective prior information about the models and must also be capable of adapting the model parameters to the particular machining environment for which the data are needed. In this paper, the sequential Maximum A Posteriori (MAP) estimation procedure is proposed as the mathematical tool for performing these functions. Mathematical details of this estimation procedure are presented. The advantages of this method over conventional regression analysis are discussed based on the analysis of an experimental tool life data set. Details regarding the selection of the various initial values needed for starting the sequential procedure are presented. The use of prior information about the models in order to improve the parameter estimates is investigated. The adaptive capability of the procedure is analyzed using simulated tool life data. The results of this analysis indicate that the proposed sequential estimation procedure is a valuable tool for estimating machinability parameters and for the adaptive optimization of machinability data base systems.

Sequential Estimation of Machinability Parameters for Adaptive Optimization of Machinability Data Base Systems

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Sequential Estimation of Machinability Parameters for Adaptive Optimization of Machinability Data Base Systems