Effect Analysis and ANN Prediction of Surface Roughness in End Milling AISI H13 Steel

2014 ◽  
Vol 800-801 ◽  
pp. 590-595
Author(s):  
Qing Zhang ◽  
Song Zhang ◽  
Jia Man ◽  
Bin Zhao
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Aisi H13 Steel ◽  
Radial Depth ◽  
Aisi H13

Surface roughness has a significant effect on the performance of machined components. In the present study, a total of 49 end milling experiments on AISI H13 steel are conducted. Based on the experimental results, the signal-to-noise (S/N) ratio is employed to study the effects of cutting parameters (axial depth of cut, cutting speed, feed per tooth and radial depth of cut) on surface roughness. An ANN predicting model for surface roughness versus cutting parameters is developed based on the experimental results. The testing results show that the proposed model can be used as a satisfactory prediction for surface roughness.

Optimization of Cutting Parameters for Improving Surface Roughness during Hard Milling of AISI H13 Steel

2020 ◽  
Vol 831 ◽  
pp. 35-39 ◽  
Author(s):  
The Vinh Do ◽  
Quoc Manh Nguyen ◽  
Minh Tan Pham
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Hard Milling ◽  
Aisi H13 Steel ◽  
Cooling Conditions ◽  
Aisi H13

In metal cutting, surface roughness plays an important role in assessing the quality of processed products. The roughness depends greatly on the selection of machining parameters such as cooling conditions and cutting parameters. For this purpose, cooling conditions including dry, MQL, and Silica-based nanofluid MQL as well as cutting parameters including cutting speed, depth-of-cut and feed-rate were investigated to determine their influence on machining roughness during hard milling of AISI H13 steel. The DOE method developed by G. Taguchi was used to design the experiments. An analysis of the signal-to-noise response and ANOVA were carried to obtain the optimal values of cutting parameters for minimizing surface roughness. The results of the present study show that Silica-based nanofluid MQL, minimum feed-rate, minimum depth-of-cut, and maximum cutting speed is an optimal cutting condition for reducing machining roughness.

An Experimental Study of Cutting Forces in Ball-End Milling of AISI H13

2013 ◽  
Vol 690-693 ◽  
pp. 2403-2407
Author(s):  
Tong Chao Ding
Keyword(s):  
Cutting Forces ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Hard Milling ◽  
Radial Depth ◽  
Aisi H13 ◽  
Coated Carbide

In the present study, an attempt has been made to experimentally investigate the effects of the cutting parameters on cutting forces in hard milling of AISI H13 steel with coated carbide tools. Designed based on Taguchi method, four factor (cutting speed, feed, radial depth of cut and axial depth of cut) four level orthogonal experiments were conducted. Three components of cutting forces were measured during hard milling experiments and then variance analysis was performed. Finally, the linear regression model was established.

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.

Optimal Cutting Parameters for Desired Surface Roughness in End Milling Inconel 718

2010 ◽  
Vol 126-128 ◽  
pp. 911-916 ◽  
Author(s):  
Yuan Wei Wang ◽  
Song Zhang ◽  
Jian Feng Li ◽  
Tong Chao Ding
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Exponential Regression Model ◽  
Optimal Cutting Parameters ◽  
Coated Carbide

In this paper, Taguchi method was applied to design the cutting experiments when end milling Inconel 718 with the TiAlN-TiN coated carbide inserts. The signal-to-noise (S/N) ratio are employed to study the effects of cutting parameters (cutting speed, feed per tooth, radial depth of cut, and axial depth of cut) on surface roughness, and the optimal combination of the cutting parameters for the desired surface roughness is obtained. An exponential regression model for the surface roughness is formulated based on the experimental results. Finally, the verification tests show that surface roughness generated by the optimal cutting parameters is really the minimum value, and there is a good agreement between the predictive results and experimental measurements.

Cutting Force in High-Speed Face Milling AISI H13 Steel

2015 ◽  
Vol 667 ◽  
pp. 35-40
Author(s):  
Xiao Bin Cui ◽  
Jing Xia Guo ◽  
Xiao Yang Wang
Keyword(s):  
Cutting Force ◽  
High Speed ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Face Milling ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Aisi H13 Steel ◽  
Aisi H13 ◽  
Axial Depth

For the purpose of acquiring thorough understanding of the characteristics of cutting force in high and ultra-high-speed face milling of hardened steel, experimental investigations on face milling of AISI H13 steel (46-47 HRC) are conducted in the present study. The cutting speed of 1400 m/min, at which relatively low cutting force and relatively low surface roughness can be obtained at the same time, is considered as a critical value for both mechanical load and surface finish. The Taguchi method is applied to investigate the effects of cutting parameters on cutting force in different speed ranges (below and above 1400 m/min). In different speed ranges, the contribution order of the cutting parameters for the resultant cutting force is the same, namely axial depth of cut, cutting speed and feed per tooth. However, the contributions of cutting speed and feed per tooth increase substantially as the cutting speed surpasses 1400 m/min. Within the range of cutting parameters used in the present study, the optimum cutting conditions for the cutting force are cutting speed 200 m/min, feed per tooth 0.02 mm/tooth and axial depth of cut 0.1 mm.

Optimization of Cutting Parameters for Desirable Surface Roughness in End-Milling Hardened AISI H13 Steel under a Certain Metal Removal Rate

2011 ◽  
Vol 188 ◽  
pp. 307-313 ◽  
Author(s):  
Tong Chao Ding ◽  
Song Zhang ◽  
Z.M. Li ◽  
Yuan Wei Wang
Keyword(s):  
Surface Roughness ◽  
Metal Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Metal Removal Rate ◽  
H13 Steel ◽  
Orthogonal Experiments ◽  
Aisi H13

In this paper, the orthogonal experiments and the optimization experiments with the same metal removal rate are designed to investigate the main effects and primary interaction of cutting parameters on surface roughness and to search the optimal cutting parameter under a certain removal rate when end-milling hardened AISI H13 steel with the PVD coated carbide insert. The empirical model for surface roughness based on the orthogonal experiments and the optimization experiments with the same metal removal rate and the optimal cutting parameter were all verified. Under a certain metal removal rate, the combination of high cutting speed, small axial depth of cut and high feed, small radial depth of cut generates the best surface roughness in hard milling of AISI H13.

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

Cutting Forces and Surface Roughness in High-Speed End Milling of Ti6Al4V

2013 ◽  
Vol 589-590 ◽  
pp. 76-81
Author(s):  
Fu Zeng Wang ◽  
Jun Zhao ◽  
An Hai Li ◽  
Jia Bang Zhao
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Cutting Forces ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Wide Range ◽  
Radial Depth ◽  
Coated Carbide

In this paper, high speed milling experiments on Ti6Al4V were conducted with coated carbide inserts under a wide range of cutting conditions. The effects of cutting speed, feed rate and radial depth of cut on the cutting forces, chip morphologies as well as surface roughness were investigated. The results indicated that the cutting speed 200m/min could be considered as a critical value at which both relatively low cutting forces and good surface quality can be obtained at the same time. When the cutting speed exceeds 200m/min, the cutting forces increase rapidly and the surface quality degrades. There exist obvious correlations between cutting forces and surface roughness.

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