An in-process based surface recognition system to predict the surface roughness of
machined parts in the end milling process was developed in this research to assure product …

This study presents surface roughness modeling for machined parts based on cutting
parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end …

Surface roughness is influenced by the machining parameters and other uncontrollable
factors resulting from the cutting tool in end milling operations. To perform the in-process …

The objective of this study is to investigate the feasibility of utilizing the signal features in
vibration measurements during the milling process and the cutting parameters for predicting …

This paper describes a new approach for surface roughness recognition (ISRR) systems to
predict surface roughness (Ra) in-process using an accelerometer to measure vibration …

This paper describes a fuzzy-nets approach for a multilevel in-process surface roughness
recognition (FN-M-ISRR) system, the goal of which is to predict surface roughness (Ra) …

In modern manufacturing environments, the quality assurance of machined parts has
attracted great attention from manufacturers. The surface roughness of a workpiece is one of …

This paper presents a system for automated, non-contact, and flexible prediction of surface
roughness of end-milled parts through a machine vision system which is integrated with an …

This paper presents the ANN model for predicting the surface roughness performance
measure in the machining process by considering the Artificial Neural Network (ANN) as the …

Dynamic surface roughness prediction during metal cutting operations plays an important
role to enhance the productivity in manufacturing industries. Various machining parameters …