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.

Study on Spindle Vibration and Surface Finish in Turning of Al 7075

2017 ◽  
Vol 261 ◽  
pp. 321-327 ◽  
Author(s):  
Abidin Şahinoğlu ◽  
Şener Karabulut ◽  
Abdulkadir Güllü
Keyword(s):  
Surface Roughness ◽  
Experimental Studies ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Dry Cutting ◽  
Al 7075 ◽  
Spindle Vibration ◽  
Coated Cemented Carbide

In this study, the relationship between the spindle vibration and surface roughness was investigated and the effect of the cutting parameters on surface roughness and spindle vibration during the machining of Aluminum alloy 7075 (Al 7075) were determined. Experimental studies have been carried out on a CNC turning machine using coated cemented carbide cutting tools under dry cutting environment. L64 full factorial design of experiments was used to investigate the optimal machining parameters for spindle vibration and surface roughness. The influences of machining parameters on vibration and surface roughness were evaluated by using analysis of variance (ANOVA) and main effect plots. The results revealed that the feed rate was the most effective cutting parameters on spindle vibration and surface roughness. The machine tool vibration amplitude and surface roughness values were significantly increased with increasing cutting feed. The depth of cut and cutting speed have the least effect on the spindle vibration and indicated an insignificant effect on surface roughness. Mathematical equations were developed to predict the vibration and surface roughness values using the regression analysis.

scholarly journals Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6361
Author(s):  
Manuela-Roxana Dijmărescu ◽  
Bogdan Felician Abaza ◽  
Ionelia Voiculescu ◽  
Maria-Cristina Dijmărescu ◽  
Ion Ciocan
Keyword(s):  
Surface Roughness ◽  
Prediction Models ◽  
End Milling ◽  
Design Method ◽  
Cutting Speed ◽  
Process Time ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Analysis ◽  
Input Variables

The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.

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.

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.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

Evaluation of Process Parameters in Machining of AA6063 Alloy

2018 ◽  
Vol 1148 ◽  
pp. 109-114
Author(s):  
M. Balaji ◽  
C.H. Nagaraju ◽  
V.U.S. Vara Prasad ◽  
R. Kalyani ◽  
B. Avinash
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Process Parameters ◽  
Single Channel ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Roughness Parameters ◽  
Surface Roughness Parameters ◽  
Aa6063 Alloy

The main aim of this work is to analyse the significance of cutting parameters on surface roughness and spindle vibrations while machining the AA6063 alloy. The turning experiments were carried out on a CNC lathe with a constant spindle speed of 1000rpm using carbide tool inserts coated with Tic. The cutting speed, feed rate and depth of cut are chosen as process parameters whose values are varied in between 73.51m/min to 94.24m/min, 0.02 to 0.04 mm/rev and 0.25 to 0.45 mm respectively. For each experiment, the surface roughness parameters and the amplitude plots have been noted for analysis. The output data include surface roughness parameters (Ra,Rq,Rz) measured using Talysurf and vibration parameter as vibration amplitude (mm/sec) at the front end of the spindle in transverse direction using single channel spectrum analyzer (FFT).With the collected data Regression analysis is also performed for finding the optimum parameters. The results show that significant variation of surface irregularities and vibration amplitudes were observed with cutting speed and feed. The optimum cutting speed and feed from the regression analysis were 77.0697m/min and 0.0253mm/rev. for the minimum output parameters. No significant effect of depth of cut on output parameters is identified.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

2020 ◽  
Vol 36 ◽  
pp. 28-46
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Salah Eddine Mechraoui ◽  
Mohamed Athmane Yallese ◽  
Mustapha Temmar
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 316L ◽  
Cutting Power ◽  
Dry Turning ◽  
Optimization Of Cutting Parameters

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

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.

Statistical Analysis of Process Parameters in Drilling of Al/Al2O3p Metal Matrix Composites

2014 ◽  
Vol 14 (3) ◽  
pp. 171-175 ◽  
Author(s):  
Yashvir Singh ◽  
Amneesh Singla ◽  
Ajay Kumar
Keyword(s):  
Surface Roughness ◽  
Statistical Analysis ◽  
Process Parameters ◽  
Metal Matrix ◽  
Design Method ◽  
Experimental Studies ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Drilling Parameters

AbstractThis paper presents a statistical analysis of process parameters for surface roughness in drilling of Al/Al2O3p metal matrix composite. The experimental studies were conducted under varying spindle speed, feed rate, point angle of drill. The settings of drilling parameters were determined by using Taguchi experimental design method. The level of importance of the drilling parameters is determined by using analysis of variance. The optimum drilling parameter combination was obtained by using the analysis of signal-to-noise ratio. Through statistical analysis of response variables and signal-to-noise ratios, the determined significant factors are depth of cut and drill point angle with the contributions of 87% and 12% respectively, whereas the cutting speed is insignificant contributing by 1% only. Confirmation tests verified that the selected optimal combination of process parameter through Taguchi design was able to achieve desired surface roughness.

scholarly journals Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2998 ◽  
Author(s):  
Kubilay Aslantas ◽  
Mohd Danish ◽  
Ahmet Hasçelik ◽  
Mozammel Mia ◽  
Munish Gupta ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Small Diameter ◽  
Cutting Parameters ◽  
Ti6al4v Alloy ◽  
Depth Of Cut ◽  
Turning Process ◽  
Micro Turning

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

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