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.

Investigation on the Cutting Force of High-Speed Milling of High Volume Fraction of SiCp/Al Composites with PCD Tools

2013 ◽  
Vol 873 ◽  
pp. 350-360
Author(s):  
Song Peng ◽  
Li Jing Xie ◽  
Xi Bin Wang ◽  
Na Xin Fu ◽  
Xing Kuan Shi
Keyword(s):  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Speed ◽  
Cutting Depth ◽  
Milling Force ◽  
High Speed Milling ◽  
Pcd Tools

High-speed milling tests for 65vol.%SiCp/6063Al composites were performed by polycrystalline diamond (PCD) tools, response surface methodology was utilized in this study, and a cutting force model was developed through response surface methodology, which contained some important parameters such as cutting speed, cutting feed rate, cutting depth and cutting width. The analysis of variance (ANOVA) indicated that the proposed mathematical model can adequately describe the relationship between cutting force and cutting process parameters. The results show that cutting depth is the biggest important factor of milling force, and cutting feed rate is the second important factor, cutting speed is the third; milling force would not increase with the increasing of cutting width.

Study on Fractal Characterization Laws of Cutting Force in CNC Turning Aeronautic Aluminium Alloy 7075-T651

2017 ◽  
Vol 748 ◽  
pp. 224-228 ◽  
Author(s):  
Bao Liang Xing ◽  
Jing Wang ◽  
Hui Ying Cao ◽  
Shu Zhong Zhang ◽  
Wei Wei ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Aluminium Alloy ◽  
Cutting Force ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Fractal Dimensions ◽  
Feed Speed ◽  
Machining Parameters ◽  
Cutting Depth ◽  
Main Effect

Based on the experiment of turning aluminium alloy (7075-T651), the relations between the fractal dimensions of cutting forces with machining parameters are studied. Cutting speed, feed speed and cutting depth are considered as the process parameters. The cutting force in turning aluminium alloy operation are measured and the fractal dimension are calculated using the algorithm of correlation dimension. From main effect plots the fractal dimensions of three directions of cutting forces are reduced with the increase of cutting speed, increased with the increase of cutting depth and insignificant with the increase of feed speed. The mathematic models of fractal dimension of cutting force are developed using response surface methodology (RSM). The results of the ANOVA show that cutting speed and cutting depth have remarkable influence to fractal dimension Dx, Dy and Dz.

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.

Fractal Characterization Analysis in CNC Milling Aluminium Alloy

2017 ◽  
Vol 748 ◽  
pp. 212-217 ◽  
Author(s):  
Zheng Mei Zhang ◽  
Bao Liang Xing ◽  
Jing Wang ◽  
Hui Ying Cao ◽  
Shao Hua Li
Keyword(s):  
Fractal Dimension ◽  
Aluminium Alloy ◽  
Cutting Force ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Fractal Dimensions ◽  
Feed Speed ◽  
Machining Parameters ◽  
Cnc Milling ◽  
Cutting Depth

Based on the experiment of milling aluminium alloy (7075-T651), the relations between the fractal dimensions of cutting forces with machining parameters are studied. Cutting speed, feed speed and cutting depth are considered as the process parameters. The cutting force in milling aluminium alloy operation are measured and the fractal dimension are calculated using the algorithm of correlation dimension. From main effect plots the fractal dimensions of three directions of cutting forces are reduced with the increase of cutting speed and increased with the increase of feed speed and cutting depth. The mathematic models of fractal dimension of cutting force are developed using response surface methodology (RSM). The results of the ANOVA show that feed speed and cutting depth have remarkable influence to fractal dimension Dx and Dy, cutting speed and feed speed for Dz.

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.

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.

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.

Experimental Study on Fractal Laws of Cutting Force for Machining Irregular Surface of Granite

2013 ◽  
Vol 797 ◽  
pp. 214-219
Author(s):  
Jin Sheng Zhang ◽  
Zheng Mei Zhang ◽  
Ming Wei Ding ◽  
Huai Chao Wang ◽  
Zhi Wang
Keyword(s):  
Fractal Dimension ◽  
Cutting Force ◽  
Cutting Forces ◽  
Orthogonal Experiment ◽  
Cutting Speed ◽  
Fractal Dimensions ◽  
Feed Speed ◽  
Machining Parameters ◽  
Irregular Surface ◽  
Cutting Depth

Based on orthogonal experiment of machining the irregular surface of Wulian red granite (G3768) with diamond profiling wheel, the relations between the fractal dimensions of cutting forces with machining parameters are studied. Cutting speed, feed speed and cutting depth are considered as the process parameters. The cutting force in machining granite operation are measured and the fractal dimension are calculated using using the algorithm of correlation dimension. From main effect plots the fractal dimensions of three directions of cutting forces are reduced with the increase of cutting speed and increased with the increase of feed speed and cutting depth. The mathematic models of fractal dimension of cutting force are developed by analysis of regression. The results of the ANOVA show that cutting speed and feed speed have remarkable influence to fractal dimensionDx,DyandDz.

scholarly journals Research on tool wear factors for milling wood-plastic composites based on response surface methodology

BioResources ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 151-162
Author(s):  
Weihua Wei ◽  
Yingli Li ◽  
Yuantong Li ◽  
Yiqi Xu ◽  
Changyong Yang
Keyword(s):  
Response Surface Methodology ◽  
Tool Wear ◽  
Response Surface ◽  
Tool Life ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Feed Speed ◽  
Cutting Depth ◽  
Wood Plastic Composites ◽  
Plastic Composites

A high-speed milling experiment on wood-plastic composites was performed using cemented carbide tools, and the resulting wear pattern was studied. The influence of the cutting parameters, the cutting speed, feed speed, and axial cutting depth on the tool wear was studied via response surface methodology, and the influence of the interaction of the cutting parameters on tool wear was analyzed. Three-dimensional surface graphs and contour plots of the tool wear results were established. According to the experimental results, a mathematical model of the tool wear based on the second-order response surface methodology was established, and the model was utilized to verify its feasibility. The results show that the nose width (NW) increases with the increase of the cutting speed and axial cutting depth and decreases with the increase of feed speed. Among the factors affecting tool wear, the cutting speed had the greatest influence, followed by the feed rate, with the axial cutting depth affecting tool wear the least. According to the results of the interaction between the tool wear and the cutting parameters, a low feed speed and small axial cutting depth can be selected to ensure long tool life; for low-speed cutting, a high feed speed and large axial cutting depth can be adopted to ensure tool life while improving machining efficiency.

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