scholarly journals Optimization and Effects of Machining Parameters on Delamination in Drilling of Pure and Al2O3 / SiO2 Added GFRP Composites

2021 ◽  
Author(s):  
Ali Ünüvar ◽  
Murat Koyunbakan ◽  
Mehmet Bağcı
Keyword(s):  
Response Surface Methodology ◽  
Glass Fiber ◽  
Response Surface ◽  
Epoxy Composite ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Erosion Wear ◽  
Gfrp Composites ◽  
Pure Epoxy ◽  
Drilling Parameters

Abstract The present study concentrates on optimization and the effect of machining parameters on delamination that occurs during drilling operation of pure glass fiber reinforced polymer (GFRP) composites and added GFRP composites which were developed for resistance to erosion wear. Contribution of drilling parameters to delamination was investigated by using Taguchi method and Analysis of Variance (ANOVA). Relationship between machining parameters and delamination was modelled by using response surface methodology. Correlations were established between the machining parameters by quadratic regression using response surface methodology (RSM). Delamination factors in the hole entrance and exit were obtained in drilling of pure glass-fiber epoxy, SiO2 and Al2O3 added GFRP materials using the experimental plan. Delamination factors at the hole exits were found bigger than delamination factors at the hole entrances. The smallest delamination values were obtained in GFRP/Epoxy composite compared to Al2O3 / SiO2 added GFRP composites at the hole exit. In the investigation of machinability of composites, considering the material as a variable, it has been determined that the material has a greater effect on delamination than the cutting parameters. A new machinability index defined and the material having the best machinability of the three materials was Al2O3 added GFRP composite at the entrance. Good machinability was obtained in drilling of pure GFRP/epoxy composite at the hole exit. It has been found that the effect of feed rate on delamination is greater than the cutting speed and the cutting speed has a low effect. Optimization of the multi-objective function created for maximizing the material removal rate, minimizing the delamination was performed, and the optimum drilling parameters were obtained. As a result of the experimental study, it was found that the amount of delamination increased although the low mechanical properties added GFRP composites with the high resistance to erosion wear in accordance with pure epoxy GFRP composites due to the lack of a strong bond between the epoxy and the fibers in AL2O3 and SiO2. It was observed that the delamination amounts of pure epoxy GFRP, Al2O3 added GFRP, and SiO2 added GFRP composites increased respectively, while the compressive and tensile strengths of these three materials decreased.

Analysis and modeling of delamination factor in drilling glass fiber reinforced plastic using response surface methodology

2010 ◽  
Vol 45 (6) ◽  
pp. 727-736 ◽  
Author(s):  
Erol Kilickap
Keyword(s):  
Response Surface Methodology ◽  
Glass Fiber ◽  
Response Surface ◽  
Gfrp Composites ◽  
Glass Fiber Reinforced Plastic ◽  
Fiber Reinforced Plastic ◽  
Delamination Factor ◽  
Glass Fiber Reinforced ◽  
Reinforced Plastic ◽  
Drilling Parameters

This study, through a new approach, presents a comprehensive mathematical model for correlating the interactive and higher order influences of drilling parameters on the delamination factor in drilling glass fiber reinforced plastic (GFRP) composites using response surface methodology. The purpose of this article is to investigate the influence of drilling parameters, such as cutting speed, feed, and point angle on delamination produced when drilling GFRP composite. The damage generated associated with drilling GFRP composites were observed, both at the entrance and exit during the drilling. The experiments are conducted based on Box—Behnken design. Empirical models are developed to correlate and predict the drilling parameters and delamination factor in drilling of GFRP. The developed models for delamination factor at entrance and exit are proposed that agree well with the experiment. The models can be utilized to select the level of drilling parameters. Thus time and cost were noticeably reduced.

scholarly journals Prediction of Machining Characteristics of Hybrid Composites Using Response Surface Methodology Approach

2018 ◽  
Vol 7 (3.1) ◽  
pp. 162 ◽  
Author(s):  
Ramanan. G ◽  
Rajesh Prabha.N ◽  
Diju Samuel.G ◽  
Jai Aultrin. K. S ◽  
M Ramachandran
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Hybrid Composites ◽  
Removal Rate ◽  
Cutting Speed ◽  
Chemical Properties ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Optimal Parameters ◽  
Rsm Technique

This manuscript presents the influencing parameters of CNC turning conditions to get high removal rate and minimal response of surface roughness in turning of AA7075-TiC-MoS2 composite by response surface method. These composites are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by RSM technique. Turning process is studied by response surface methodology design of experiment. The optimal parameters were predicted by RSM technique. The most influencing process parameter predicted from RSM techniques in cutting speed and depth of cut.   

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.

Modeling and Experimental Study on Cutting Force of Diamond Circular Saw in Cutting Granite Using Response Surface Methodology

2013 ◽  
Vol 652-654 ◽  
pp. 2191-2195 ◽  
Author(s):  
Zheng Mei Zhang ◽  
Hai Wen Xiao ◽  
Gui Zhen Wang ◽  
Shu Zhong Zhang ◽  
Shu Qin Zhang
Keyword(s):  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Feed Speed ◽  
Machining Parameters ◽  
Cutting Depth ◽  
First Order ◽  
Circular Saw

Based on experiment of sawing Wulian red granite with diamond circular saw, the relations between the cutting force with machining parameters are studied. Cutting speed, feed rate and cutting depth are considered as the process parameters. The cutting force in sawing granite operation are measured and the experimental results are then analyzed using response surface methodology. From the analysis, it is seen that the cutting force Fx , Fy and Fz are reduced with the increase of cutting speed and increased with the increase of feed rate and cutting depth, and the mathematical models of the cutting force are developed. By ANOVA for the cutting force models, It is concluded that the models are significant at 95% confidence level and the significant effects are the first-order of cutting speed, feed speed, cutting depth and the quadratic of cutting depth.

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.

Surface Roughness Prediction in the End Milling Parameters of Aluminum Nitride Ceramic by Response Surface Methodology

2010 ◽  
Vol 154-155 ◽  
pp. 626-633
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
End Milling ◽  
Cubic Boron Nitride ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Roughness Model ◽  
Roughness Prediction ◽  
Grinding Tool

The present experimental study aimed to examine the selected machining parameters on Surface roughness in the machining of alumina nitride ceramic. The influence of cutting speed and feed rate were determined in end milling by using Cubic boron nitride grinding tool. The predictive surface roughness model has been developed by response surface methodology. The response surface contours with respect to input parameters are presented with the help of Design expert software. The adequacy of the model was tested by ANOVA.

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.

Multi-process parameter optimization in face milling of Ti6Al4V alloy using response surface methodology

2016 ◽  
Vol 232 (9) ◽  
pp. 1590-1602 ◽  
Author(s):  
Abhineet Saini ◽  
Parveen Chauhan ◽  
BS Pabla ◽  
SS Dhami
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Chemical Reactivity ◽  
Cutting Speed ◽  
Face Milling ◽  
Ti6al4v Alloy ◽  
Machining Parameters ◽  
Specific Strength ◽  
Optimization Of Process Parameters ◽  
Titanium Alloy Ti6al4v

Titanium alloy, Ti6Al4V, is an exceptional material with several desirable properties, namely, high specific strength, high corrosion and heat resistance, which make it a promising contender in number of demanding applications. However, it has poor machinability, resulting from low thermal conductivity, high chemical reactivity with tool and spring effect during cutting. These properties lead to reduced tool life during machining, due to which its usage is limited despite excellent mechanical properties. Therefore, optimization of process parameters using response surface methodology in face milling of Ti6Al4V alloy with uncoated carbide tools has been investigated experimentally in this work. This article is focused on developing mathematical relation between input factors and response parameters, namely, surface roughness (Ra), tool wear (Tw) and tool vibration (Tv). The machining parameters are optimized for minimum Ra, Tw and Tv values. The optimal parameters are validated experimentally which showed a good agreement with the predicted results. The feed rate was found to be the most influential parameter affecting Ra and Tv, whereas cutting speed is the most effective in influencing Tw.

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