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.

Investigation of Surface Roughness during Turning Process Based on Response Surface Methodology

2010 ◽  
Vol 135 ◽  
pp. 243-248 ◽  
Author(s):  
Shu Han ◽  
Qing Long An ◽  
Ming Chen ◽  
Gang Liu ◽  
Yun Shan Zhang
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Cutting Parameters ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Machining Time ◽  
The Mathematical Model ◽  
Carbide Insert ◽  
Alloy Cast

The purpose of this study was to analyze the effects of cutting parameters on the surface roughness (Ra) when turning of alloy cast iron using uncoated carbide insert under dry cutting condition. The mathematical model for the surface roughness was developed by response surface methodology (RSM).Response surface contours were constructed and used for determining the optimum cutting conditions to reduce machining time without increasing the surface roughness.

scholarly journals Influence of Cutting Parameters on Surface Roughness for Wet and Dry Turning Process

2011 ◽  
Vol 471-472 ◽  
pp. 233-238 ◽  
Author(s):  
Muhammad Yusuf ◽  
Khairol Anuar ◽  
Napsiah Binti Ismail ◽  
Shamsuddin Sulaiman
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Fractional Factorial ◽  
Cutting Process ◽  
Depth Of Cut ◽  
Turning Process ◽  
Dry Cutting ◽  
Dry Turning ◽  
Coated Carbide

This paper presents a study of the quality of a surface roughness model for mild steel with coated carbide cutting tool on turning process. The experiments were carried out under wet and dry cutting conditions. The model is developed based on cutting speed, feed and depth of cut as the parameters of cutting process. This research applies the fractional factorial design of experiment approach to studied the influence of cutting parameters on surface roughness. The measured results were collected and analyzed using commercial software package called Minitab. Analysis of variances is used to examine the influence of turning factors and factor interactions on surface roughness. The result indicated that, there are inherent differences in surface roughness between wet and dry cutting process with the same parameters process model. Analysis of variance was found that feed parameter is the most significant cutting parameter, which influences the surface roughness. The most significant interactions were found between cutting speed and feed parameters for dry turning process. Therefore is a significant effect of using combination of the fluid for cooling the cutting operation.

Optimization of Cutting Parameters during Turning of AISI-310 Using Response Surface Methodology

2016 ◽  
Vol 854 ◽  
pp. 45-51
Author(s):  
S. Nandhakumar ◽  
R. Vijayakumar ◽  
Senthil Padmavathy ◽  
N. Nagasundaram
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Parameters Optimization ◽  
Depth Of Cut ◽  
Optimization Of Cutting Parameters

Design of Experiments is employed to study the stimulus of cutting parameters such as feed rate, spindle speed, depth of cut in the turning operation of AISI-310 and optimizing the value of those parameters for getting the higher material removal rate (MRR) and minimal surface roughness. A prediction model has been developed by using the above influencing parameters. For the purpose of parameters optimization we investigate the parameters using Response Surface Methodology (RSM). It is shown that feed rate is the main parameter in influencing the surface roughness, which is being followed by spindle speed and depth of cut. It is found that surface roughness and feed rate were directly proportional to each other for some extent. The confirmation tests were carried out to with the optimum set of parameters and are verified with test results. The comparison of above two results were found to be good with maximum error within 5% on comparing it with the predicted model.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

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

Effects of Cutting Condition on Surface Roughness when Turning Untreated and Sb-Treated Al-11%Si Alloys Using PVD Coated Tools

2013 ◽  
Vol 315 ◽  
pp. 413-417 ◽  
Author(s):  
Mohsen Marani Barzani ◽  
Mohd Yusof Noordin ◽  
Ali Akhavan Farid ◽  
Saaed Farahany ◽  
Ali Davoudinejad
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Manufacturing Processes ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Fabrication Cost ◽  
Coated Tools ◽  
Experimental Trials ◽  
Cutting Speeds

Surface roughness is an important output in different manufacturing processes. Its characteristic affects directly the performance of mechanical components and the fabrication cost. In this current work, an experimental investigation was conducted to determine the effects of various cutting speeds and feed rates on surface roughness in turning the untreated and Sb-treated Al-11%Si alloys. Experimental trials carried out using PVD TIN coated inserts. Experiments accomplished under oblique dry cutting when three different cutting speeds have been used at 70, 130 and 250 m/min with feed rates of 0.05, 0.1 and 0.15 mm/rev, whereas depth of cut kept constant at 0.05 mm. The results showed that Sb-treated Al-11%Si alloys have poor surface roughness in comparison to untreated Al-11%Si alloy. The surface roughness values reduce with cutting speed increment from 70 m/min to 250 m/min. Also, the surface finish deteriorated with increase in feed rate from 0.5 mm/rev to 0.15 mm/rev.

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.

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.

Investigations on the Effect of CNC Dry Hard Turning Process Parameters on Surface Integrity: A Multi-performance Characteristics Optimization

2014 ◽  
Vol 14 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Suha K. Shihab ◽  
Zahid A. Khan ◽  
Aas Mohammad ◽  
Arshad Noor Siddiquee
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
Feed Rate ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Micro Hardness ◽  
Dry Cutting

AbstractThe cutting parameters such as the cutting speed, the feed rate, the depth of cut, etc. are expected to affect the two constituents of surface integrity (SI), i.e., surface roughness and micro-hardness. An attempt has been made in this paper to investigate the effect of the CNC hard turning parameters on the surface roughness average (Ra) and the micro-hardness (μh) of AISI 52100 hard steel under dry cutting conditions. Nine experimental runs based on an orthogonal array of the Taguchi method were performed and grey relational analysis method was subsequently applied to determine an optimal cutting parameter setting. The feed rate was found to be the most influential factor for both the Ra and the μh. Further, the results of the analysis of variance (ANOVA) revealed that the cutting speed is the most significant controlled factor for affecting the SI in the turning operation according to the weighted sum grade of the surface roughness average and micro-hardness.

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.

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