Optimization of Surface Roughness in Turning of Ti-6Al-4V Using Response Surface Methodology and TLBO

2015 ◽  
Author(s):  
Neelesh Ku. Sahu ◽  
A. B. Andhare
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Titanium Alloys ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Maximum Error ◽  
Depth Of Cut ◽  
Interaction Variables ◽  
Teaching Learning

Surface roughness is an important surface integrity parameter for difficult to cut alloys such as Titanium alloys (Ti-6Al-4V). In the present work, initially a mathematical model is developed for predicting surface roughness for turning operation using Response Surface Methodology (RSM). Later, a recently developed advanced optimization algorithm named as Teaching Learning Based Optimization (TLBO) is used for further parameter optimization of the equation developed using RSM. The design of experiments was performed using central composite design (CCD). Analysis of variance (ANOVA) demonstrated the significant and non-significant parameters as well as validity of predicted model. RSM describes the effect of main and mixed (interaction) variables on the surface roughness of titanium alloys. RSM analysis over experimental results showed that surface roughness decreased as cutting speed increased whereas it increased with increase in feed rate. Depth of cut had no effect on surface roughness. By comparing the predicted and measured values of surface roughness the maximum error was found to be 7.447 %. It indicates that the developed model can be effectively used to predict the surface roughness. Further optimization of the roughness equation was carried out by TLBO method. It gave minimum surface roughness as 0.3120 μm at the cutting speed of 1704 RPM (171.217 m/min), feed rate of 55.6 mm/min (.033 mm/rev) and depth of cut of 0.7 mm. These results were confirmed by confirmation experiment and were better than that of RSM.

scholarly journals Prediction of surface roughness and cutting force under MQL turning of AISI 4340 with nano fluid by using response surface methodology

2018 ◽  
Vol 5 ◽  
pp. 5 ◽  
Author(s):  
Pralhad B. Patole ◽  
Vivek V. Kulkarni
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Process Parameters ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nano Fluid ◽  
Aisi 4340

This paper presents an investigation into the minimum quantity lubrication mode with nano fluid during turning of alloy steel AISI 4340 work piece material with the objective of experimental model in order to predict surface roughness and cutting force and analyze effect of process parameters on machinability. Full factorial design matrix was used for experimental plan. According to design of experiment surface roughness and cutting force were measured. The relationship between the response variables and the process parameters is determined through the response surface methodology, using a quadratic regression model. Results show how much surface roughness is mainly influenced by feed rate and cutting speed. The depth of cut exhibits maximum influence on cutting force components as compared to the feed rate and cutting speed. The values predicted from the model and experimental values are very close to each other.

Investigating and Modeling the Machinability of Al 6061 Using Response Surface Methodology

2015 ◽  
Vol 761 ◽  
pp. 267-272
Author(s):  
Basim A. Khidhir ◽  
Ayad F. Shahab ◽  
Sadiq E. Abdullah ◽  
Barzan A. Saeed
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Effect Of Temperature ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process

Decreasing the effect of temperature, surface roughness and vibration amplitude during turning process will improve machinability. Mathematical model has been developed to predict responses of the surface roughness, temperature and vibration in relation to machining parameters such as the cutting speed, feed rate, and depth of cut. The Box-Behnken First order and second-order response surface methodology was employed to create a mathematical model, and the adequacy of the model was verified using analysis of variance. The experiments were conducted on aluminium 6061 by cemented carbide. The direct and interaction effect of the machining parameters with responses were analyzed. It was found that the feed rate, cutting speed, and depth of cut played a major role on the responses, such as the surface roughness and temperature when machining mild steel AISI 1018. This analysis helped to select the process parameters to improve machinability, which reduces cost and time of the turning process.

Surface Roughness Prediction Model in Machining of 34CrMo4 Steel with Several Tools Using Response Surface Methodology

2012 ◽  
Vol 445 ◽  
pp. 90-95
Author(s):  
Hamed Barghikar ◽  
Amin Poursafar ◽  
Abbas Amrollahi
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Mean Values ◽  
Roughness Model ◽  
34Crmo4 Steel

The surface roughness model in the turning of 34CrMo4 steel was developed in terms of cutting speed, feed rate and depth of cut and tool nose radius using response surface methodology. Machining tests were carried out using several tools with several tool radius under different cutting conditions. The roughness equations of cutting tools when machining the steels were achieved by using the experimental data. The results are presented in terms of mean values and confidence levels.The established equation and graphs show that the feed rate and cutting speed were found to be main influencing factor on the surface roughness. It increased with increasing the feed rate and depth of cut, but decreased with increasing the cutting speed, respectively. The variance analysis for the second-order model shows that the interaction terms and the square terms were statistically insignificant. However, it could be seen that the first-order affect of feed rate was significant while cutting speed and depth of cut was insignificant.The predicted surface roughness model of the samples was found to lie close to that of the experimentally observed ones with 95% confident intervals.

Investigations on Surface Roughness Measurement in Minimum Quantity Lubrication Turning of Titanium Alloys Using Response Surface Methodology and Box–Cox Transformation

2016 ◽  
Vol 16 (2) ◽  
pp. 75-88 ◽  
Author(s):  
Munish Kumar Gupta ◽  
P. K. Sood ◽  
Vishal S. Sharma
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Feed Rate ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Machining Parameters ◽  
Minimum Quantity ◽  
Box Cox Transformation

AbstractIn the present work, an attempt has been made to establish the accurate surface roughness (Ra, Rq and Rz) prediction model using response surface methodology with Box–Cox transformation in turning of Titanium (Grade-II) under minimum quantity lubrication (MQL) conditions. This surface roughness model has been developed in terms of machining parameters such as cutting speed, feed rate and approach angle. Firstly, some experiments are designed and conducted to determine the optimal MQL parameters of lubricant flow rate, input pressure and compressed air flow rate. After analyzing the MQL parameter, the final experiments are performed with cubic boron nitride (CBN) tool to optimize the machining parameters for surface roughness values i. e., Ra, Rq and Rz using desirability analysis. The outcomes demonstrate that the feed rate is the most influencing factor in the surface roughness values as compared to cutting speed and approach angle. The predicted results are fairly close to experimental values and hence, the developed models using Box-Cox transformation can be used for prediction satisfactorily.

Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN

2020 ◽  
Vol 38 (6A) ◽  
pp. 887-895
Author(s):  
Hind H. Abdulridha ◽  
Aseel J. Haleel ◽  
Ahmed A. Al-duroobi
Keyword(s):  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Rsm Model ◽  
Good Agreement

The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates the goodness of fit for the model and high significance of the model. From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly. However, if the test patterns number will be increased then this error can be further minimized. The proposed RSM and ANN prediction model sufficiently predict Ra accurately. However, ANN prediction model is found to be better compared to RSM model. The artificial neutral network is applied to experimental results to find prediction results for two response parameters. The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which produces flexibility to the manufacturing industries to select the best setting based on applications.

Hard Turning of Hot Work Steel X38CRMOV5-3: Evaluation of Surface Roughness and Current Values Using Cutting Parameters

2020 ◽  
Author(s):  
Ali Kemal Cakir
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Feed Rate ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Dry Machining ◽  
Motor Current ◽  
Design Factors

This study evaluates the surface roughness and current values using cutting parameters in the turning of AISI H11 being hot work tool steel under dry machining conditions. The selected design factors are the depth of cut, feed rate, cutting speed. A design of experiments was used to carry out this research. The obtained results were analyzed to determine the effects of input parameters on the resultant surface roughness, current using the analysis of variance (ANOVA) and the Response Surface Methodology (RSM). The experimental results showed that increasing feed rate increased the surface roughness, and current values. The most effective cutting parameter on all the output parameters was found to be the feed rate on the surface roughness. Also, the motor current values were influenced by the 38,48% depth of cut, 23,98% cutting speed, 25,52% feed rate, respectively.

Multi-Objective Optimization Using Box-Behken of Response Surface Methodology for High-Speed Machining of Inconel 718

2014 ◽  
Vol 629 ◽  
pp. 487-492 ◽  
Author(s):  
Mohd Shahir Kasim ◽  
Che Hassan Che Haron ◽  
Jaharah Abd Ghani ◽  
E. Mohamad ◽  
Raja Izamshah ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Tool Life ◽  
Feed Rate ◽  
Inconel 718 ◽  
High Speed ◽  
Cutting Speed ◽  
High Speed Milling

This study was carried out to investigate how the high-speed milling of Inconel 718 using ball nose end mill could enhance the productivity and quality of the finish parts. The experimental work was carried out through Response Surface Methodology via Box-Behnken design. The effect of prominent milling parameters, namely cutting speed, feed rate, depth of cut (DOC), and width of cut (WOC) were studied to evaluate their effects on tool life, surface roughness and cutting force. In this study, the cutting speed, feed rate, DOC, and WOC were in the range of 100 - 140 m/min, 0.1 - 0.2 mm/tooth, 0.5 - 1.0 mm and 0.2 - 1.8 mm, respectively. In order to reduce the effect of heat generated during the high speed milling operation, minimum quantity lubrication of 50 ml/hr was used. The effect of input factors on the responds was identified by mean of ANOVA. The response of tool life, surface roughness and cutting force together with calculated material removal rate were then simultaneously optimized and further described by perturbation graph. Interaction between WOC with other factors was found to be the most dominating factor of all responds. The optimum cutting parameter which obtained the longest tool life of 60 mins, minimum surface roughness of 0.262 μm and resultant force of 221 N was at cutting speed of 100 m/min, feed rate of 0.15 mm/tooth, DOC 0.5 m and WOC 0.66 mm.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

scholarly journals Modeling and Analysis for Surface roughness in Machining EN-31 steel using Response Surface Methodology

2011 ◽  
pp. 33-38
Author(s):  
L B Abhang ◽  
M Hameedullah
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Confidence Level ◽  
Feed Rate ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Cutting Velocity ◽  
En 31 ◽  
Tool Nose Radius

This paper utilizes the regression modeling in turning process of En-31 steel using response surface methodology (RSM) with factorial design of experiments. A first-order and second-order surface roughness predicting models were developed by using the experimental data and analysis of the relationship between the cutting conditions and response (surface roughness). In the development of predictive models, cutting parameters of cutting velocity, feed rate, depth of cut, tool nose radius and concentration of lubricants were considered as model variables, surface roughness were considered as response variable. Further, the analysis of variance (ANOVA) was used to analyze the influence of process parameters and their interaction during machining. From the analysis, it is observed that feed rate is the most significant factor on the surface roughness followed by cutting speed and depth of cut at 95% confidence level. Tool nose radius and concentration of lubricants seem to be statistically less significant at 95% confidence level. Furthermore, the interaction of cutting velocity/feed rate, cutting velocity/ nose radius and depth of cut/nose radius were found to be statistically significant on the surface finish because their p-values are smaller than 5%. The predicted surface roughness values of the samples have been found to lie close to that of the experimentally observed values.

scholarly journals Optimization of Cutting Parameters on Turning Process Based on Surface Roughness Using Response Surface Methodology

2011 ◽  
Vol 117-119 ◽  
pp. 1561-1565
Author(s):  
Muhammad Yusuf ◽  
Mohd Khairol Anuar Ariffin ◽  
N. Ismail ◽  
S. Sulaiman
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Process ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Optimization Of Cutting Parameters

This paper describes effect of cutting parameters on surface roughness for turning of aluminium alloy 7050 using carbide cutting tool with dry cutting condition. The model is developed based on cutting speed, feed rate and depth of cut as the parameters of cutting process. The selection of cutting process was based on the design of experiments Response Surface Methodology (RSM). The objective of this research is finding the optimum cutting parameters based on surface roughness. The relation between cutting parameters and surface roughness were discussed.

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