Predictive Models for Cutting Force in Turning Tools Based on Response Surface Methodology

In the present work, dye-free, salt-free coloration in wool fabric was studied. Response surface methodology (RSM) was used to develop predictive models for simulation and optimization of the color index a*b*ΔE. The influence of the viriables (sulfuric acid concentration,dyeing time ,dyeing temperature and concentration of DMBA) were investigated with the help of MINITAB16 software. The modeling methodologies was statisticallyanalysed by the coefficient of determination (R2), Correlation Coefficient values and Correlation Coefficient values. Results indicated excellent performance of experimental data by polynomial regression model.Finally, the corresponding effects of the independent viariables were studied by the analysis of variance (ANOVA).

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.652-654.2191 ◽

2013 ◽

Vol 652-654 ◽

pp. 2191-2195 ◽

Cited By ~ 3

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.

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Multi-Objective Optimization Using Box-Behken of Response Surface Methodology for High-Speed Machining of Inconel 718

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.629.487 ◽

2014 ◽

Vol 629 ◽

pp. 487-492 ◽

Cited By ~ 3

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.

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Cutting force prediction when green machining of thin-walled Ti-6Al-4V under dry and MQL-cutting using response surface methodology and artificial neural networks-algorithm

10.1063/1.5118035 ◽

2019 ◽

Author(s):

M. Yanis ◽

A. S. Mohruni ◽

S. Sharif ◽

I. Yani ◽

Z. Suzen ◽

...

Keyword(s):

Neural Networks ◽

Response Surface Methodology ◽

Artificial Neural Networks ◽

Cutting Force ◽

Response Surface ◽

Cutting Force Prediction ◽

Force Prediction ◽

Green Machining ◽

Thin Walled ◽

Artificial Neural

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Modelling and Analysis of Relationship between Cutting Parameters Surface Roughness and Cutting Forces Using Response Surface Methodology when Hard Turning with Coated Ceramic Inserts

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.686.19 ◽

2016 ◽

Vol 686 ◽

pp. 19-26 ◽

Cited By ~ 2

Author(s):

Ildikó Maňková ◽

Marek Vrabeľ ◽

Jozef Beňo ◽

Mária Franková

Keyword(s):

Surface Roughness ◽

Response Surface Methodology ◽

Cutting Force ◽

Response Surface ◽

Hard Turning ◽

Cutting Speed ◽

Cutting Parameters ◽

Oxide Ceramic ◽

Cutting Force Components ◽

Force Components

Experimental research and modeling in the field of turning hardened bearing steel with hardness of 62 HRC using TiN coated mixed oxide ceramic inserts is presented. The main objective of the article is investigation the relationship between cutting parameters (cutting speed and feed rate) and output machining variables (surface roughness and cutting force components) through the response surface methodology (RSM). The mathematical model of the effect of process parameters on the cutting force components and surface roughness is presented. Moreover, the influence of TiN coating on above mentioned variables was monitored. The design of experiment according to Taguchi L9 orthogonal matrix (32) was applied for trials. Pearson´s correlation matrix was used to examine the dependence between the factors (f, vc) and the machining variables (surface roughness and cutting force components). The results show how much surface roughness and cutting force components is influenced by cutting speed and feed in hard turning with coated ceramics.

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Investigation on the Predictive Model for Burr in Laser Cutting Titanium Alloy

Materials Science Forum ◽

10.4028/www.scientific.net/msf.526.133 ◽

2006 ◽

Vol 526 ◽

pp. 133-138 ◽

Cited By ~ 4

Author(s):

Wen T. Chien ◽

W.C. Hung

Keyword(s):

Response Surface Methodology ◽

Titanium Alloy ◽

Predictive Model ◽

Response Surface ◽

Predictive Models ◽

Laser Cutting ◽

Back Propagation ◽

Cutting Parameters ◽

Back Propagation Neural Network ◽

Burr Height

The purpose of this study is to develop two predictive models for burr height in cutting titanium alloy plates by using Nd:YAG laser. Firstly, Taguchi method has been used to arrange the experimental scheme and analyze the results via analysis of mean . The important laser cutting parameters affecting burr height can be found. It shows that the pressure of assistant gas, the focusing position and the pulsed frequency are the most important cutting parameters in order. Then they have been chosen as the input variables for response surface methodology and used to construct a mathematical equation for predicting burr height. Secondly, the laser cutting parameters and experimental results obtained from conducting the schematic arrangement using Taguchi method and response surface methodology have been treated as training patterns and recalling patterns for the back-propagation neural network. As a result, a predictive model for burr height prediction in laser cutting titanium alloy has been established. To verify the accuracy of above two prediction models, there are 9 sets of experiment have been performed. It shows that the average error for predicting burr height by the mathematical equation derived from response surface methodology is 5.52% and by the predictive model established by back-propagation neural network is 4.51%, respectively. Obviously, both predictive models are good enough for the relational research and practical applications. It can be concluded that the procedure used in this research and the obtaining predictive models can be used practically in correlate industry.

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