Research on Surface Roughness Prediction Model of Ultrasonic Assisted Grinding by Response Surface Method

2021 ◽  
Vol 324 ◽  
pp. 66-71
Author(s):  
Hua Dong Yu ◽  
Mao Xun Wang ◽  
Jin Kai Xu ◽  
Le Tong ◽  
Guang Jun Chen ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Response Surface ◽  
Surface Quality ◽  
Feed Rate ◽  
Machining Parameters ◽  
Ultrasonic Assisted Grinding ◽  
Surface Method ◽  
Ultrasonic Assisted ◽  
Roughness Prediction

In this paper, through a series of grinding experiments with different machining parameters on the surface of the workpiece, the surface roughness under different machining parameters are obtained The surface roughness prediction model is constructed by the response surface method. The effects of feed rate, amplitude, and spindle speed on the surface roughness are analyzed. The results show that the surface quality of ultrasonic-assisted grinding is better than that of conventional grinding. Amplitude has the most prominent effect on the improvement of surface quality, followed by the spindle speed. The feed rate has little effect on the surface roughness. The model can predict 93.71% of the experimental results and the prediction error of the model is lower than 5%.

scholarly journals Influence of process parameters on surface roughness and forming time of Al-1100 sheet in incremental sheet metal forming

2019 ◽  
Vol 13 (2) ◽  
pp. 4911-4927
Author(s):  
Swagatika Mohanty ◽  
Srinivasa Prakash Regalla ◽  
Yendluri Venkata Daseswara Rao
Keyword(s):  
Surface Roughness ◽  
Response Surface ◽  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Feed Rate ◽  
Metal Forming ◽  
Wall Angle ◽  
Surface Method ◽  
Incremental Sheet Metal Forming

Product quality and production time are critical constraints in sheet metal forming. These are normally measured in terms of surface roughness and forming time, respectively. Incremental sheet metal forming is considered as most suitable for small batch production specifically because it is a die-less manufacturing process and needs only a simple generic fixture. The surface roughness and forming time depend on several process parameters, among which the wall angle, step depth, feed rate, sheet thickness, and spindle speed have a greater impact on forming time and surface roughness. In the present work, the effect of step depth, feed rate and wall angle on the surface roughness and forming time have been investigated for constant 1.2 mm thick Al-1100 sheet and at a constant spindle speed of 1300 rpm. Since the variable effects of these parameters necessitate multi-objective optimization, the Taguchi L9 orthogonal array has been used to plan the experiments and the significance of parameters and their interactions have been determined using analysis of variance (ANOVA) technique. The optimum response has been brought out using response surfaces. Finally, the findings of response surface method have been validated by conducting additional experiments at the intermediate values of the parameters and these results were found to be in agreement with the predictions of Taguchi method and response surface method.

Optimization of Machining Parameters on Surface Roughness in EDM of Ti-6Al-4V Using Response Surface Method

2011 ◽  
Vol 213 ◽  
pp. 402-408 ◽  
Author(s):  
M.M. Rahman ◽  
Md. Ashikur Rahman Khan ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Surface Roughness ◽  
Response Surface ◽  
Response Surface Method ◽  
Surface Finish ◽  
Machining Parameters ◽  
Surface Method ◽  
Superior Surface ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

This paper presents the influence of EDM parameters in terms of peak ampere, pulse on time and pulse off time on surface roughness of titanium alloy (Ti-6Al-4V). A mathematical model for surface finish is developed using response surface method (RSM) and optimum machining setting in favor of surface finish are evaluated. Design of experiments (DOE) techniques is implemented. Analysis of variance (ANOVA) has been performed to verify the fit and adequacy of the developed mathematical models. The acquired results yield that the increasing pulse on time causes fine surface till a certain value and then deteriorates the surface finish. It is investigated that about 200 µs pulse off time produce superior surface finish. These results lead to desirable surface roughness and economical industrial machining by optimizing the input parameters.

Study on the Critical Cutting Thickness and the Precision Cutting Experiments of Isotropic Pyrolyric Graphite

2011 ◽  
Vol 228-229 ◽  
pp. 458-463
Author(s):  
Ming Hai Wang ◽  
Hu Jun Wang ◽  
Zhong Hai Liu
Keyword(s):  
Surface Roughness ◽  
High Temperature ◽  
Prediction Model ◽  
Feed Rate ◽  
Cutting Speed ◽  
Carbon Composite ◽  
Cutting Depth ◽  
Brittle Ductile Transition ◽  
Roughness Prediction ◽  
Turbine Shaft

Isotropic pyrolyric graphite (IPG) is a new kind of brittle material, it not only has the general advantages of ordinal carbonaceous materials such as high temperature resistance, lubrication and abrasion resistance, but also has the advantages of impermeability and machinability that carbon/carbon composite doesn’t have. So it can be used for sealing the aeronautics and astronautics engines turbine shaft and the ethylene high-temperature equipment. The mechanism of this material removal during the precision cutting was analyzed by using the theory of strain gradient. The critical cutting thickness of IPG was calculated for the first time. Furthermore, the cutting process parameters such as cutting depth and feed rate which corresponding to the scale of brittle-ductile transition deformation of IPG was calculated. The prediction model of surface roughness in precision cutting of IPG was developed based on the Genetic algorithm. Using the surface roughness prediction model, the study investigates the influence of the cutting speed, the feed rate and the cutting depth on surface roughness in precision turning process was researched.

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.

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