Multi-parameter identification and control of turned surface textures

Abstract

A method for characterizing turned surface textures corresponding to varying cutting variables leading to differing profile shape is presented with emphasis on a global view of turning machinability parameters. Through a multi-parameter experimental analysis of turned steel specimens, several standardized parameters are developed. The ability of these parameters are tested to distinguish between different profiles in shape or to identify some particular features with respect to cutting factors. It is indicated that texture statistical functions and parameters are the most effective for relative discrimination and control. Based on the results of this study, new maps for turned surface control are developed. With regards to the variability of some of the parameters at different surface measuring sites, it is evident that the amount of scatter is lower when cutting factors offer regular chip formation. Nomenclaturea, b parameters of the beta distribution f feed [mm/rev] m number of peaks in the profile n(0) number of intersections of the profile with the mean line q 1 , q 2 parameters of the Fisher-Pearson distribution system r mean asperity curvature [µm] R a profile average height [µm] R DelA mean inclination of the asperities [ • ] R ku kurtosis of the profile height distribution R lr ratio of the real length to the horizontal length of the profile [%] R p the distance between the maximum height and the mean line of the profile [µm] R q standard deviation of the profile height distribution [µm] R sk skewness of the profile height distribution R sm mean spacing of the asperities at the centre line [µm] R t maximum profile height [µm] v cutting speed [m/min] β auto correlation length [µm] λ a mean wavelength of the profile irregularities [µm] µ mean value of the log-normal distribution σ standard deviation of the log-normal distribution µ mean estimator of a distribution s standard deviation estimator of a distribution

Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models

“…The formation of surface roughness mechanism is very complicated and mainly depends on machining process (Benardos and Vosniakos, 2003;Petropoulos et al, 2006). Hence, it is very difficult to determine the surface roughness through analytical equations.…”

Statistical study of surface roughness in turning of peek composites

“…In addition, the ratios R a /R q and k = (R t À R p )/R t (solidity surface ratio) may be regarded as measures of the form of the turned profiles [14,15]; higher values of the former indicate periodicity (the typical for turning ''arc-chain'' form is dominant) and lower values for k indicate the same characteristic (''emptier'' surfaces).…”

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

“…The formation of surface roughness mechanism is very complicated and mainly depends on machining process [7]. Hence, it is very difficult to determine the surface roughness through analytical equations.…”