scholarly journals Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network

2020 ◽  
Vol 10 (11) ◽  
pp. 3941 ◽  
Author(s):  
Yung-Chih Lin ◽  
Kung-Da Wu ◽  
Wei-Cheng Shih ◽  
Pao-Kai Hsu ◽  
Jui-Pin Hung
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Predictive Models ◽  
Prediction Accuracy ◽  
End Milling ◽  
Cutting Parameters ◽  
Milling Process ◽  
Cutting Depth

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 milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.

scholarly journals Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints

2021 ◽  
Vol 11 (5) ◽  
pp. 2137 ◽  
Author(s):  
Tian-Yau Wu ◽  
Chi-Chen Lin
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Pso Algorithm ◽  
Milling Process ◽  
Machining Parameters ◽  
Elman Neural Network ◽  
Swarm Optimization

The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

A Prediction Model of Surface Roughness in Micro End Milling

2015 ◽  
Vol 727-728 ◽  
pp. 354-357
Author(s):  
Mei Xia Yuan ◽  
Xi Bin Wang ◽  
Li Jiao ◽  
Yan Li
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Feed Rate ◽  
End Milling ◽  
Orthogonal Experiment ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Cutting Depth ◽  
Micro End Milling

Micro-milling orthogonal experiment of micro plane was done in mesoscale. Probability statistics and multiple regression principle were used to establish the surface roughness prediction model about cutting speed, feed rate and cutting depth, and the significant test of regression equation was done. On the basis of successfully building the prediction model of surface roughness, the diagram of surface roughness and cutting parameters was intuitively built, and then the effect of the cutting speed, feed rate and cutting depth on the small structure surface roughness was obtained.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

Investigating the Effect of Cutting Speed Variation on Surface Roughness of 7136 Aluminum Alloy in End Milling

2015 ◽  
Vol 809-810 ◽  
pp. 129-134 ◽  
Author(s):  
Alina Bianca Bonţiu Pop ◽  
Mircea Lobonţiu
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Milling Process ◽  
Future Research ◽  
Cutting Depth ◽  
Speed Variation

Surface quality is affected by various processing parameters and inherent uncertainties of the metal cutting process. Therefore, the surface roughness anticipation becomes a real challenge for engineers and researchers. In previous researches [1] I have investigated the feed rate influence on surface roughness and manufacturing time reduction. The 7136 aluminum alloy was machined by end milling operation using standard tools for aluminum machining. The purpose of this paper is to identify by experiments the influence of cutting speed variation on surface roughness. The scientific contribution brought by this research is the improvement of the end milling process of 7136 aluminum alloy. This material is an aluminum alloy developed by Universal Alloy Corporation and is used in the aircraft industry to manufacture parts from extruded profiles. The research method used to solve the problem is experiment. A range of cutting speeds was used while the cutting depth and the feed per tooth were constrained per minimum and maximum requirements defined for the given cutting tool. The experiment was performed by using a 16 mm End milling cutter, holding two indexable cutting inserts. The machine used for the milling tests was a HAAS VF2 CNC. The surface roughness (response) was measured by using a portable surface roughness tester (TESA RUGOSURF 20 Portable Surface Finish Instrument). Following the experimental research, results were obtained which highlight the cutting speed influence on surface roughness. Based on these results we created roughness variation diagrams according to the cutting speed for each value of feed per tooth and cutting depth. The final results will be used as data for future research.

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