Study on the prediction model of surface roughness for side milling operations

2006 ◽  
Vol 29 (9-10) ◽  
pp. 867-878 ◽  
Author(s):  
Ching-Kao Chang ◽  
H.S. Lu
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Side Milling ◽  
Milling Operations

Modelling of surface roughness and studying of optimal machining position in side milling

2021 ◽  
Author(s):  
Jinfeng Bai ◽  
Huiying Zhao ◽  
Lingyu Zhao ◽  
Mingchen Cao ◽  
Duanzhi Duan
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization Algorithm ◽  
Surface Generation ◽  
The Novel ◽  
Dynamic Effects ◽  
Computational System ◽  
Swarm Optimization ◽  
Side Milling ◽  
Milling Operations ◽  
Novel Model

Abstract In this work, a theoretical analysis of surface generation numerical model is presented to predict the surface roughness achieved by side milling operations with cylindrical tools. This work is focused on the trajectory of tools with two teeth by influencing of tool errors such as radial runouts, as well as straightness with dynamic effects. A computational system was developed to simulate roughness topography in contour milling with cylindrical tool. Finally, the PSO (particle swarm optimization) algorithm is employed to find the optimal machining position for the best surface roughness. Experimental data is satisfied with the the novel pretiction model for the tooth’s trajectory, and the the final prediction accuracy is high enough, i.e. that the prediction surface roughness. Low prediction surface roughness error (1.37 ~ 15.04%) and position error (0.95 ~ 1.25 mm) indicate effectiveness of the model built in this work. The novel model may be used to determine the variation in surface roughness.

Development of the cBN Electroplated End-Mill for High Precision Machining of CFRP

2020 ◽  
Author(s):  
Shinnosuke Yamashita ◽  
Tatsuya Furuki ◽  
Hiroyuki Kousaka ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Drop Out ◽  
End Mill ◽  
Machined Surface ◽  
Side Milling ◽  
Reinforced Plastics ◽  
Milled Surface ◽  
Grinding Conditions ◽  
Grinding Tool

Abstract Recently, the demand of carbon fiber reinforced plastics (CFRP) has been rapidly increased in various fields. In most cases, CFRP products requires a finish machining like cutting or grinding. In the case of an end-milling, burrs and uncut fibers are easy to occur. On the other hand, a precise machined surface and edge will be able to obtain by using the grinding tool. Therefore, this research has been developed a novel the cBN electroplated end-mill that combined end-mill and grinding tool. In this report, the effectiveness of developed tool was investigated. First, the developed tool cut the CFRP with side milling. As the result, the cBN abrasives that were fixed on the outer surface of developed tool did not drop out. Next, the end-milled surface of CFRP was ground with the developed tool under several grinding conditions based on the Design of Experiment. Consequently, the optimum grinding condition that can obtain the sharp edge which does not have burrs and uncut fibers was found. However, surface roughness was not good enough. Thus, an oscillating grinding was applied. In addition, the theoretical surface roughness formula in case using the developed tool was formularized. As the result, the required surface roughness in the airplane field was obtained.

Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy

2021 ◽  
Author(s):  
XueTao Wei ◽  
caixue yue ◽  
DeSheng Hu ◽  
XianLi Liu ◽  
YunPeng Ding ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Finite Element ◽  
Prediction Model ◽  
Simulation Model ◽  
Finite Element Simulation ◽  
Simulation Software ◽  
Surface Roughness Prediction ◽  
Contour Shape ◽  
Element Simulation ◽  
Roughness Prediction

Abstract The processed surface contour shape is extracted with the finite element simulation software, and the difference value of contour shape change is used as the parameters of balancing surface roughness to construct the infinitesimal element cutting finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the central composite test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the finite element simulation model and surface roughness prediction model are verified and analyzed through experiment. The research results show that, compared with the experiment results, the maximum error of finite element simulation model and surface roughness prediction model is 30.9% and12.3%, respectively. So, the model in this paper is accurate and will provide the theoretical basis for optimization study of auxiliary milling process of supersonic vibration.

Research on Surface Roughness Prediction Model for High-Speed Milling Inclined Plane of Hardened Steel

2010 ◽  
Vol 97-101 ◽  
pp. 2044-2048 ◽  
Author(s):  
Yuan Ling Chen ◽  
Bao Lei Zhang ◽  
Wei Ren Long ◽  
Hua Xu
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
High Speed ◽  
Hardened Steel ◽  
Prediction Errors ◽  
Inclined Plane ◽  
Workpiece Surface ◽  
Factors Influencing ◽  
Generalized Regression Neural Networks ◽  
Roughness Prediction

As the factors influencing the workpiece surface roughness is complexity and uncertainty, according to orthogonal experimental results, the paper established Empirical regression prediction model and generalized regression neural networks (GRNN) for prediction of surface roughness when machining inclined plane of hardened steel in high speed , moreover, compared their prediction errors. The results show that GRNN model has better prediction accuracy than empirical regression prediction model and can be better used to control the surface roughness dynamically.

Effects of Cutting Parameters on the Surface Roughness of Ti6Al4V Titanium Alloys in Side Milling

2011 ◽  
Vol 175 ◽  
pp. 289-293 ◽  
Author(s):  
Hao Liu ◽  
Chong Hu Wu ◽  
Rong De Chen
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Side Milling ◽  
Milling Parameters ◽  
Fine Grain

Side milling Ti6Al4V titanium alloys with fine grain carbide cutters is carried out. The influences of milling parameters on surface roughness are investigated and also discussed with average cutting thickness, material removal rate and vibration. The results reveal that the surface roughness increases with the increase of average cutting thickness and is primarily governed by the radial cutting depth.

INVESTIGATION OF WIPER INSERTS EFFECTS IN TURNING AND MILLING PROCESSES

2021 ◽  
pp. 2150111
Author(s):  
MURAT KIYAK
Keyword(s):  
Surface Roughness ◽  
Cost Saving ◽  
Tool Wear ◽  
Feed Rate ◽  
Turning Process ◽  
Nose Radius ◽  
Turning Operations ◽  
Effective Factors ◽  
Milling Operations ◽  
The Many

The surface roughness is a crucial factor in machining methods. The most effective factors on surface roughness are feed rate and tool nose radius. Due to the many advantages of wiper (multi-nose radius) inserts, their importance and use has been increasing recently. The purpose of this paper is to investigate the effect of wiper inserts on surface roughness and tool wear. In this study, conventional inserts and wiper inserts were experimentally compared separately in milling and turning operations. Compared to conventional inserts, the surface roughness values obtained using wiper inserts improved by 33% in turning operations and approximately 40% in milling operations. It was observed that the production time in the turning process was reduced by about 25% in the case of using wiper inserts compared to the use of conventional inserts. In milling, this ratio was determined to be approximately 43% due to the fact that it has multiple cutting edge. It has been observed that the use of wiper inserts in machining methods creates a significant time and cost saving advantage.

scholarly journals Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

2019 ◽  
Vol 105 (5-6) ◽  
pp. 2151-2165 ◽  
Author(s):  
Adel Taha Abbas ◽  
Danil Yurievich Pimenov ◽  
Ivan Nikolaevich Erdakov ◽  
Tadeusz Mikolajczyk ◽  
Mahmoud Sayed Soliman ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Unit Volume ◽  
High Strength ◽  
Face Milling ◽  
Production Costs ◽  
The Other ◽  
Cutting Conditions ◽  
Milling Operations ◽  
Artificial Neural ◽  
Pareto Method

Abstract Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and the construction industry. The various milling operations for these components have key performance indicators: accuracy, surface roughness (Ra), and machining time for removal of a unit volume min/cm3 (Tm). The specified surface roughness values for machining each component is achieved based on the prototype specifications. However, poor adherence to specifications can result in the rejection of the machined parts, implying extra production costs and raw material wastage. An algorithm using an artificial neural network (ANN) with the Edgeworth-Pareto method is presented in this paper to optimize the cutting parameter in CNC face-milling operations. The set of parameters are adjusted to improve surface roughness and minimal unit-volume material removal rates, thereby reducing production costs and improving accuracy. An ANN algorithm is designed in Matlab, based on a 3–10-1 Multi-Layer Perceptron (MLP), which predicts the Ra of the workpiece surface to an accuracy of ± 5.78% within the range of the experimental angular spindle speed, feed rate, and cutting depth. An unprecedented Pareto frontier for Ra and Tm was obtained for the finished grade-H steel workpiece using an ANN algorithm that was then used to determine optimized cutting conditions. Depending on the production objective, one or the other of two sets of optimum machining conditions can be used: the first one sets a minimum cutting power, while the other sets a maximum Tm with a slight increase (under 5%) in milling costs.

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