Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks

2007 ◽  
pp. 713-718
Author(s):  
Dong Woo Kim ◽  
Young Jae Shin ◽  
Kyoung Taik Park ◽  
Eung Sug Lee ◽  
Jong Hyun Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
High Speed ◽  
Milling Process ◽  
High Speed Milling ◽  
Artificial Neural

Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks

2007 ◽  
Vol 364-366 ◽  
pp. 713-718 ◽  
Author(s):  
Dong Woo Kim ◽  
Young Jae Shin ◽  
Kyoung Taik Park ◽  
Eung Sug Lee ◽  
Jong Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
High Speed ◽  
Back Propagation Algorithm ◽  
High Speed Milling ◽  
Artificial Neural ◽  
Cutting Path

The objective of this research was to apply the artificial neural network algorithm to predict the surface roughness in high speed milling operation. Tool length, feed rate, spindle speed, cutting path interval and run-out were used as five input neurons; and artificial neural networks model based on back-propagation algorithm was developed to predict the output neuron-surface roughness. A series of experiments was performed, and the results were estimated. The experimental results showed that the applied artificial neural network surface roughness prediction gave good accuracy in predicting the surface roughness under a variety of combinations of cutting conditions.

scholarly journals Model of Dependence Between the Parameters of Surface Roughness and the Parameters of Form and Location During the Machining by Drills with the use of Artificial Neural Networks

2021 ◽  
Author(s):  
Zdravko Krivokapić ◽  
Radoslav Vučurević ◽  
Predrag V. Dasic ◽  
Petar Ivanković
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
High Speed ◽  
High Speed Steel ◽  
Machined Surface ◽  
Drilling Operation ◽  
Artificial Neural ◽  
Twist Drills

Abstract This paper presents a model of dependence between the parameters of surface roughness and the parameters of cylindricity and eccentricity in drilling operation for the enhancing steel EN 42CrMo4, hardness 28 HRC, with twist drills DIN 338 made of high-speed steel EN HS6-5-2, with normal blade. The quality of machining, besides the accuracy of measures, completely determined with the values of the parameters of the surface roughness and the parameters of form and location, hence this paper is oriented to the creating models between parameters of the quality of a machined surface and parameters of deviations from form and position. By the developing models based on artificial neural networks and using experimental results, it is possible to analyse the quality of machining on the basis of parameters of a surface roughness.

scholarly journals Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Adnan Rachmat Anom Besari ◽  
Ruzaidi Zamri ◽  
Md. Dan Md. Palil ◽  
Anton Satria Prabuwono
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Surface Defects ◽  
Vision System ◽  
Real Data ◽  
Surface Measurement ◽  
Vision Sensor ◽  
Skilled Workers ◽  
Artificial Neural

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks

scholarly journals Properties of the Surface Layer After Trochoidal Milling and Brushing: Experimental Study and Artificial Neural Network Simulation

2019 ◽  
Vol 10 (1) ◽  
pp. 75
Author(s):  
Monika Kulisz ◽  
Ireneusz Zagórski ◽  
Jakub Matuszak ◽  
Mariusz Kłonica
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Cutting Speed ◽  
Mg Alloy ◽  
Polar Component ◽  
Maximum Height ◽  
Roughness Parameters ◽  
Surface Geometry ◽  
Artificial Neural

The aim of this study was to investigate the effect of milling and brushing cutting data settings on the surface geometry and energy parameters of two Mg alloy substrates: AZ91D and AZ31. In milling, the cutting speed and the trochoidal step were modified (vc = 400–1200 m/min and str = 5–30%) to investigate how they affect selected 2D (Rz, Rku, Rsk, RSm, Ra) and 3D (Sa, Sz, Sku, Ssk) roughness parameters. The brushing treatment was carried out at constant parameters: n = 5000 rev/min, vf = 300 mm/min, ap = 0.5 mm. The surface roughness of specimens was assessed with the Ra, Rz, and RSm parameters. The effects of the two treatments on the workpiece surface were analyzed comparatively. It was found that the roughness properties of the machined surface may be improved by the application of a carbide milling cutter and ceramic brush. The use of different machining data was also shown to impact the surface free energy and its polar component of Mg alloy specimens. Complementary to the results from the experimental part of the study, the investigated machining processes were modelled by means of statistical artificial neural networks (the radial basis function and multi-layered perceptron). The artificial neural networks (ANNs) were shown to perform well as a tool for the prediction of Mg alloy surface roughness parameters and the maximum height of the profile (Rz) after milling and brushing.

Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

2014 ◽  
Vol 592-594 ◽  
pp. 2733-2737 ◽  
Author(s):  
G. Harinath Gowd ◽  
K. Divya Theja ◽  
Peyyala Rayudu ◽  
M. Venugopal Goud ◽  
M .Subba Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Artificial Neural ◽  
End Milling Process

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

scholarly journals Application of ANN in Milling Process: A Review

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Problems ◽  
Milling Process ◽  
Machining Processes ◽  
Traditional Methods ◽  
Artificial Neural

In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

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