scholarly journals Analysed statistically Modelling and Optimization of Laser Machining by Response Surface Methodology

2018 ◽  
Vol 153 ◽  
pp. 05005
Author(s):  
Hani Mizhir ◽  
Kamil Jawad ◽  
Zuhair H Obaid
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Quality ◽  
Laser Cutting ◽  
Cutting Speed ◽  
Machining Process ◽  
Good Surface ◽  
Model Response ◽  
Cutting Model

One of the important goals of this research is to predict a relationship between the process input parameters and resultants from surface roughness features through developing a laser cutting model. In most engineering applications, natural sciences and computing; statistical methods, which are one of mathematical branch are widely used for investigating the results. Laser cutting process of stainless steel (2205) is a machining process selected for this study. The technique which adopted here is a response surface methodology (RSM). The main portion for this study is the influence of cutting speed on surface quality. To study the model response, and for statistical approach with further prediction; a mathematical based model has been developed through regression analysis. It’s found that as one of the important results in this research, that cutting speed and surface roughness has a significant rule on the model response. To produce a good surface roughness, it’s approved that the high cutting speed connected with high power regardless of high pressure has a high influence on surface quality.

Study the Effect of Vegetable-Based Lubricant on Surface Roughness during Milling Operation by Using Response Surface Methodology

2014 ◽  
Vol 548-549 ◽  
pp. 336-343
Author(s):  
Aishah Adam Siti ◽  
Yih Loong Yap
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Quality ◽  
Sunflower Oil ◽  
Feed Rate ◽  
Cutting Speed ◽  
Machining Process ◽  
High Cutting Speed ◽  
Milling Operation

Milling is a common machining process with high cutting speed and material removal rate. High cutting speed tends to generate heat at the interface between tool and workpiece. This may reduce the surface quality of the workpiece and reduce the tool life. The application of conventional cutting fluid to reduce friction and heat between tool and workpiece may produce numerous environmental problems. The vegetable-based lubricant as an alternative for measuring the effect on surface quality during milling operation is studied. The relation between machining parameters such as spindle speed, feed rate, depth of cut and lubricants is analyzed by using Analysis of Variance (ANOVA) and Response Surface Methodology (RSM). The optimization of surface quality is analyzed by using Box-Behnken Design of RSM. The research focused on using sunflower oil as lubricant during machining process using mild steel solid block with TiCN coated HSS tools and using synthetic oil as comparison. Surface roughness for using sunflower oil as lubricant is 0.457 μm which lower compared to synthetic oil with 0.679 μm. Feed rate and spindle speed give the most significant effect to the surface roughness during milling operation. The application of vegetable-based oil as lubricant gives better surface quality, prevent tool wear and offer environmental advantages.

scholarly journals Effect of Process Parameters on the Surface Roughness and Kerf Width of Mild Steel during Plasma Arc Cutting Using Response Surface Methodology

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ekhaesomi A Agbonoga ◽  
Oyewole Adedipe ◽  
Uzoma G Okoro ◽  
Fidelis J Usman ◽  
Kafayat T Obanimomo ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Analysis Of Variance ◽  
Process Parameters ◽  
Cutting Speed ◽  
Gas Pressure ◽  
Kerf Width ◽  
Plasma Arc ◽  
Plasma Arc Cutting

This study investigated the effects of process parameters of plasma arc cutting (PAC) of low carbon steel material using analysis of variance. Three process parameters, cutting speed, cutting current and gas pressure were considered and experiments were conducted based on response surface methodology (RSM) via the box-Behnken approach. Process responses viz. surface roughness (Ra) and kerf width of cut surface were measured for each experimental run. Analysis of Variance (ANOVA) was performed to get the contribution of process parameters on responses. Cutting current has the most significant effect of 33.43% on the surface roughness and gas pressure has the most significant effect on  kerf width of  41.99% . For minimum surface roughness and minimum kerf width, process parameters were optimized using the RSM. Keywords: Cutting speed, cutting current, gas pressure,   surface roughness, kerf width

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.

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.

Experimental Study on Optimizing the Effect of Drilling Parameters on Roundness Error of Holes of Aluminum 7075 Using Response Surface Methodology

2012 ◽  
Vol 184-185 ◽  
pp. 981-987
Author(s):  
Setayesh Hakak Zargar ◽  
Vahid Tahmasbi ◽  
K. Besharati ◽  
Mohammad Farzami
Keyword(s):  
Experimental Study ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Quality ◽  
Cutting Speed ◽  
Drilling Process ◽  
Regression Equations ◽  
Roundness Error ◽  
Drill Diameter ◽  
Aluminum 7075

In the present work, an experimental study has been made to optimize the drilling process parameters. Response surface methodology based on central composite design (CCD) has been used to study and analyze the experiments. Twist drill diameter, cutting speed and feed of drilling were chosen as variables to study the process performance for the responses of the hole surface quality (Ra) and the roundness error on aluminum 7075. Experiments were performed on a newly designed experimental setup developed in the laboratory. The results identified the most important parameters to maximize the hole surface quality and minimize roundness error. Finally, regression equations were obtained to predict the responses for different values of variables.

scholarly journals Analysis and Optimization of Ceramic Cutting Tool In Hard Turning of EN-31 Using Factorial Design

2012 ◽  
pp. 53-58
Author(s):  
Chetan Darshan ◽  
Lakhvir Singh ◽  
APS Sethi
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Analysis Of Variance ◽  
Feed Rate ◽  
Hard Turning ◽  
Flank Wear ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Good Surface ◽  
En 31

Manufacturers around the globe persistently looking for the cheapest and quality manufactured machined components to compete in the market. Good surface quality is desired for the proper functioning of the produced parts. The surface quality is influenced by cutting speed, feed rate and depth of cut and many other parameters. In the present study attempt has been made to evaluate the performance of ceramic inserts during hard turning of EN-31 steel. The analysis of variance is applied to study the effect of cutting speed, feed rate and depth of cut on Flank wear and surface roughness. Model is found to be statically significant using regression model, while feed and depth of cut are the factor affecting Flank wear and feed is dominating factors for surface roughness. The analysis of variance was used to analyze the input parameters and there interactions during machining. The developed model predicted response factor at 95% confidence level.

scholarly journals Research and Modelling of Surface Roughness, Cutting Forces and I-kaz Coefficients for S42C in Turning using Response Surface Methodology

2019 ◽  
Vol 8 (12S2) ◽  
pp. 608-620
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Response Surface ◽  
Cutting Parameters ◽  
Machining Process ◽  
Regression Equations ◽  
Turning Process ◽  
Machining Processes ◽  
Surface Method

This paper presents the optimization in machining processes on the cutting parameters for the S45C in turning process using the response surface method (RSM). The experimental work conducted investigates the influence of cutting parameters on statistical analysis of signals and surface quality. The paper also presents a statistical analysis of signal processing. The cutting force was measured during machining using the Kistler 9129AA dynamometer to monitor the force signals and the data was analyzed using the I-kazTM method of statistical analysis. This statistical analysis was used to assess the effect of force signals during the machining process. The RSM models for Ra and Rz, and Ideveloped with ANOVA and multiple regression equations. The models also were compared and validated with the predicted and measured of Ra and Rz values, and I-kaz coefficients. The optimal configuration of cutting parameters was observed at 200 m/min, 0.1 mm/rev and 0.521 mm with desirability of 95.9%. It is observed that the models developed are suggested to be utilized for predicting surface roughness values and I-kaz coefficients for the machining of S45C steel.

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.

Prediction Modelling of Surface Roughness for Laser Beam Cutting on Acrylic Sheets

2009 ◽  
Vol 83-86 ◽  
pp. 793-800 ◽  
Author(s):  
M.M. Noor ◽  
K. Kadirgama ◽  
M.M. Rahman ◽  
N.M. Zuki N.M. ◽  
Mohd Ruzaimi Mat Rejab ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Laser Beam ◽  
Response Surface ◽  
Response Surface Method ◽  
Laser Cutting ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Power Requirement ◽  
Box Behnken Design ◽  
Surface Method

This paper develops the predicting model on surface roughness of laser beam cutting (LBC) for acrylic sheets. Box-Behnken design based on Response surface method was used to predict the effect of laser cutting parameters including the power requirement, cutting speed and tip distance on surface roughness during the machining. Response surface method (RSM) was used to minimize the number of experiments. It can be seen that from the experimental results, the effects of the laser cutting parameters with the surface roughness were investigated. It was found that the surface roughness is significantly affected by the tip distance followed by the power requirement and cutting speed. Some defects were found in microstructure such as burning, melting and wavy surface. This simulation gain more understanding of the surface roughness distribution in laser cutting. The developed model is suitable to be used in the range of (power 90 to 95, cutting speed 700 to 1100 and tip distance 3 to 9) to predict surface roughness.

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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