The prediction of cutting force in ball-end milling

Yang, Minyang; Park, Heeduck

doi:10.1016/0890-6955(91)90050-d

Cited by 181 publications

(72 citation statements)

References 6 publications

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“…These systems are inspired by the structure of the brain. Computing with neural networks is no algorithmic (Yang and Park, 1991) and they are trained through examples rather than programmed by software. Some of the key features of ANN's are their processing speed due to their massive parallelism, their proven ability to be trained, to produce instantaneous and correct responses from corrupted inputs once trained, and their ability to generalize information over a wide range.…”

Section: Predictive Surface Roughness Modelingmentioning

confidence: 99%

Implementation of neural network for monitoring and prediction of surface roughness in a virtual end milling process of a CNC vertical milling machine

El-rahman¹,

El-Zahry²,

Mahdy³

2013

J. Eng. Technol. Res.

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This paper presents a real time simulation for virtual end milling process. Alyuda NeuroIntelligence was used to design and implement an artificial neural network. Artificial neural networks (ANN's) is an approach to evolve an efficient model for estimation of surface roughness, based on a set of input cutting conditions. Neural network algorithms are developed for use as a direct modeling method, to predict surface roughness for end milling operations. Prediction of surface roughness in end milling is often needed in order to establish automation or optimization of the machining processes. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling processes. The training of the networks is preformed with experimental machining data. The neural network is used to predict surface roughness of the virtual milling machine to analyze and preprocess pre measured test data. The simulation for the geometrical modeling of end milling process and analytical modeling of machining parameters was developed based on real data from experiments carried out using Prolight2000 (CNC) milling machine. This application can simulate the virtual end milling process and surface roughness Ra (µm) prediction graphs against cutting conditions simultaneously. The user can also analyze parameters that influenced the machining process such as cutting speed, feed rate of worktable.

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Section: Predictive Surface Roughness Modelingmentioning

confidence: 99%

Implementation of neural network for monitoring and prediction of surface roughness in a virtual end milling process of a CNC vertical milling machine

El-rahman¹,

El-Zahry²,

Mahdy³

2013

J. Eng. Technol. Res.

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“…However, force models for cutters other than the face milling cutter and square end mill only began in the 1990's. Yang and Park [3] could have been the first to present a force model for the ball-end mill. In their work, the force model is based on the fundamental mechanics of orthogonal cutting and explicitly considers the effects of the shear angle, shear stress and friction angle.…”

Section: Introductionmentioning

confidence: 99%

Fourier Analysis of Milling Force for General Helical Cutters via Spacetime Convolution, Part 2: Applications for Common Cutters and Model Validation

Cm¹,

Jj²

2015

J Appl Mech Eng

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Part 2 of this study illustrates the applications of the frequency domain force model put forth in Part 1 for three common helical cutters: the square, taper, and ball end mills. The respective geometric and boundary functions required for the evaluation of the force spectra are derived by applying differential geometry to these three types of cutters including cutters of constant helix angle and constant helix lead. By virtue of the explict expression of Fourier coefficients of milling force, the differences between cutting forces generated by two cutters with constant helix angle and constant helix lead can be described quantitatively. In slot (or half slot) milling for the taper end mills with a constant helix angle and constant helix lead, the strategy for selecting axial depths of cut to reduce force pulsation is presented respectively. Also derived are the specific expressions for the average forces of these three helical cutters in common cutting configurations. Moreover, as an inverse application, a linear equation is formulated for the identification of six shearing and ploughing cutting constants from the measured average cutting forces for a general helical cutter. The frequency domain force model and the identification of the cutting constants are finally demonstrated and validated through experiments with all three types of milling cutters.

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“…This leads to discrepancies, in particular, in the direction of defining and determining the undeformed chip thicknesses for the ball-end mill cutting elements. Yang and Park [10] were the first to introduce a cutting force model for ball-end milling. The undeformed chip thickness was determined in the cutter surface normal direction.…”

Section: Introductionmentioning

confidence: 99%

Cutting force prediction for ball-end mills with non-horizontal and rotational cutting motions

Azeem

Feng

2012

Int J Adv Manuf Technol

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Accurate cutting force prediction is essential to precision machining operations as cutting force is a process variable that directly relates to machining quality and efficiency. This paper presents an improved mechanistic cutting force model for multi-axis ball-end milling. Multi-axis ballend milling is mainly used for sculptured surface machining where non-horizontal (upward and downward) and rotational cutting tool motions are common. Unlike the existing research studies, the present work attempts to explicitly consider the effect of the 3D cutting motions of the ballend mill on the cutting forces. The main feature of the present work is thus the proposed generalized concept of characterizing the undeformed chip thickness for 3D cutter movements. The proposed concept evaluates the undeformed chip thickness of an engaged cutting element in the principal normal direction of its 3D trochoidal trajectory. This concept is unique and it leads to the first cutting force model that specifically applies to non-horizontal and rotational cutting tool motions. The resulting cutting force model has been validated experimentally with extensive verification test cuts consisting of horizontal, non-horizontal, and rotational cutting motions of a ball-end mill.

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The prediction of cutting force in ball-end milling

Cited by 181 publications

References 6 publications

Implementation of neural network for monitoring and prediction of surface roughness in a virtual end milling process of a CNC vertical milling machine

Implementation of neural network for monitoring and prediction of surface roughness in a virtual end milling process of a CNC vertical milling machine

Fourier Analysis of Milling Force for General Helical Cutters via Spacetime Convolution, Part 2: Applications for Common Cutters and Model Validation

Cutting force prediction for ball-end mills with non-horizontal and rotational cutting motions

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