Impact of Cutting Parameters on Surface Roughness in Milling Aluminum Alloy 6061 Using ANN Models

2011 ◽  
Vol 63-64 ◽  
pp. 412-415 ◽  
Author(s):  
Yu Mei Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Ann Model ◽  
Aluminum Alloy 6061 ◽  
Radial Depth ◽  
Four Levels ◽  
Alloy 6061

The surface roughness is difficult to estimate in machining, especially for weak stiffness workpiece. So, prediction model of surface roughness using artificial neural network (ANN) is developed. This model investigates the effects of cutting parameters during milling Aluminum alloy 6061. The experiments are planned with four factors and four levels for developing the knowledge base for ANN training. Three-dimensional surface plots are generated using ANN model to study the effects of cutting parameters on surface roughness. The analysis reveals that cutting speed and feed rate have significant effects in reducing the surface roughness, while the axial and radial depth of cut has less effect. And the variations of surface roughness are highly non-linear with the cutting parameters.

scholarly journals Effect of Equal Channel Angular Pressing on the Surface Roughness of Solid State Recycled Aluminum Alloy 6061 Chips

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adel Taha Abbas ◽  
Mohamed Adel Taha ◽  
Adham Ezzat Ragab ◽  
Ehab Adel El-Danaf ◽  
Mohamed Ibrahim Abd El Aal
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Solid State ◽  
Equal Channel Angular Pressing ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Aluminum Alloy 6061 ◽  
Angular Pressing ◽  
Alloy 6061

Solid state recycling through hot extrusion is a promising technique to recycle machining chips without remelting. Furthermore, equal channel angular pressing (ECAP) technique coupled with the extruded recycled billet is introduced to enhance the mechanical properties of recycled samples. In this paper, the surface roughness of solid state recycled aluminum alloy 6061 turning chips was investigated. Aluminum chips were cold compacted and hot extruded under an extrusion ratio (ER) of 5.2 at an extrusion temperature (ET) of 425°C. In order to improve the properties of the extruded samples, they were subjected to ECAP up to three passes at room temperature using an ECAP die with a channel die angle(Φ)of 90°. Surface roughness (RaandRz) of the processed recycled billets machined by turning was investigated. Box-Behnken experimental design was used to investigate the effect of three machining parameters (cutting speed, feed rate, and depth of cut) on the surface roughness of the machined specimens for four materials conditions, namely, extruded billet and postextrusion ECAP processed billets to one, two, and three passes. Quadratic models were developed to relate the machining parameters to surface roughness, and a multiobjective optimization scheme was conducted to maximize material removal rate while maintaining the roughness below a preset practical value.

scholarly journals OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
Muhammad Yanis ◽  
Amrifan Saladin Mohruni ◽  
Safian Sharif ◽  
Irsyadi Yani
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Ann Model ◽  
Green Machining ◽  
Radial Depth ◽  
Thin Walled

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. 

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.

scholarly journals Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Aykut Eser ◽  
Elmas Aşkar Ayyıldız ◽  
Mustafa Ayyıldız ◽  
Fuat Kara
Keyword(s):  
Surface Roughness ◽  
Conjugate Gradient ◽  
Prediction Models ◽  
Experimental Studies ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Ann Model ◽  
Aa6061 Alloy

This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

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.

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.

Effect of Various Cooling Strategies on Surface Roughness of High Speed Milling Ti-6Al-4V

2011 ◽  
Vol 697-698 ◽  
pp. 49-52 ◽  
Author(s):  
Xiao Yong Yang ◽  
Cheng Zu Ren ◽  
Guang Chen ◽  
Bing Han ◽  
Y. Wang
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Side Milling ◽  
High Speed Milling ◽  
Cooling Strategies ◽  
Radial Depth

This study focused on the side milling surface roughness of titanium alloy under various cooling strategies and cutting parameters. The experimental results show that the cooling strategies significantly affect the surface roughness in milling Ti-6Al-4V. Surface roughness (Ra) alterations are investigated. Cutting fluid strategy made nearly all the smallest and most stable roughness values. The surface roughness values produced by all cooling strategies are obviously affected by feed, radial depth-of-cut and cutting speed. However, axial depth-of-cut has little influence.

Application of Taguchi’s Method for Study of Machining Parameters on Surface Roughness of 7136 Aluminum Alloy in End Milling

2015 ◽  
Vol 809-810 ◽  
pp. 123-128 ◽  
Author(s):  
Alina Bianca Bonţiu Pop
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Process Parameters ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Future Research ◽  
Depth Of Cut ◽  
Machining Parameters

Starting with the necessity to identify the optimum values of the cutting parameters which are affecting the surface quality, it is appropriate to use the design of experiment techniques to conduct the experiments. Previous researches [1] focused on the investigation of the effects of machining parameters on surface roughness. In this paper, the experiments were conducted based on the established Taguchi’s technique, L8 orthogonal array using Minitab-17 statistical software. Three machining parameters are chosen as process parameters: Cutting Speed, Feed per tooth and Depth of cut. The orthogonal matrix includes these three factors set for analysis, each with 2 levels associated. The level of influence that the process parameters exert on the surface roughness is analyzed by Taguchi method data analysis. In this case the signal to noise ratio was tacked into account. Also, the recommended configuration regarding the optimum values of these parameters was determined as well as the interactions between them, in order to obtain better surface roughness for 7136 aluminum alloy machining. The final results will be used as data for future research.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

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