Machined Surface Error Analysis — A Face Milling Approach

Shi

et al. 2014

MSF

The work-piece surface quality reflects the cutting performance of face-milling cutter. This paper presents the development of an algorithm to predict work-piece surface roughness in face milling operation. The prediction model is based on the face milling cutter fixed square inserts with flat edges. The static prediction model considers the effects of radial and axial run-out error of inserts, feed per tooth, tooth number, cutting edge length, nose radius, main lead angle, and axial depth of cut. The dynamic prediction model considers the effects of the Z-axial relative displacement between the work-piece and cutting teeth caused by forced vibration. By combining the prediction results of static and dynamic models, the surface roughness of the work-piece in face milling is predicted.

Section: Fig 1 Face Milling Systemmentioning

confidence: 99%

Research on Surface Roughness in High Speed Face Milling Part 1: Theory of Prediction Model

Shi

et al. 2014

MSF

“…There are very few literature prediction models for surface flatness obtained by face milling, for example, among them (Gu et al 1997a, b;Liu and Zou 2011;Yi et al 2015). Gu et al (1997a, b) showed in their work a model of plane prediction for face milling operations, taking into account cutting conditions, elastic deformations of the spindle milling cutter and workpiece.…”

Section: Introductionmentioning

confidence: 99%

“…Gu et al (1997a, b) showed in their work a model of plane prediction for face milling operations, taking into account cutting conditions, elastic deformations of the spindle milling cutter and workpiece. Liu and Zou (2011) predicted surface flatness errors in face milling using the traditional method. The work combines the empirical data and simulation results.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth

et al. 2020

The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.

Int J Adv Manuf Technol

“…But measurement of flatness error using elastic deformation and spindle axis tilt could be inflated by inclusion of surface roughness. Liu and Zou [29] predicted the surface flatness errors in the course of face milling, using the traditional trial-and-error method. Their work emphasized on the optimization of machining parameters to minimize surface deformation under machining loads.…”

Section: Introductionmentioning

confidence: 99%

A study of the influence of processing parameters and tool wear on elastic displacements of the technological system under face milling

Pimenov

Guzeev

Mikołajczyk

et al. 2017

A study of the influence of processing parameters and tool wear on the total elastic displacements of the technological system is based on a mathematical model of elastic displacements during face milling. The mathematical model takes into account both plane-parallel and angular displacements of the subsystems 0-"workpiece-device-machine table" and 1-"tool-device-z-head system." The article covers the method of experimental determination of the element compliances of the GF2171S5 milling machine technological system. Angular compliances of the spindle assembly are not listed in the milling machine technical data sheet; therefore, this work gives a method for compliance determination along the coordinate axes of the machine and angular compliances of subsystems of the technological system. The article presents the adequacy assessment of mathematical models of elastic displacements of the technological system under face milling, for different values of tool flank wear on the flank surface. The article also presents the influences of face milling process parameters (workpiece material, cutting speed, cutting depth, the main cutting-edge angle, the cutter overhang to its diameter ratio, feed per tooth, and different values of the tool flank wear on the flank surface) on the total elastic displacements of the technological system, based on the estimated mathematical model of the elastic displacements of the technological system during face milling.