Modeling and Analysis of Cutting Forces in Hard Turning T250 Steel Using CBN Tools

2010 ◽  
Vol 154-155 ◽  
pp. 694-700
Author(s):  
Yue Ding ◽  
Xi Bin Wang ◽  
Li Jing Xie ◽  
Hao Yang
Keyword(s):  
Cutting Force ◽  
Analysis Of Variance ◽  
Feed Rate ◽  
Cutting Forces ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Modeling And Analysis ◽  
Feed Force

The objective of this paper is to study the cutting forces in hard turning T250 steel with CBN tools. Experiments based on the Box-Behnken design were conducted to develop the cutting forces models by response surface methodology (RSM). Significance tests of the model are performed by the analysis of variance (ANOVA). It is also discussed the effects of cutting parameters (cutting speed, feed rate and depth of cut) on the cutting force components. The results show that the models can fit experimental data via analysis of variance. The most important cutting parameter is depth of cut, followed by feed rate, while the effect of cutting speed can be neglected. Compared to cutting force and feed force, thrust force is the largest. In addition, the cutting forces generated by the uncoated tool are smaller than by the coated one due to tool wear.

scholarly journals Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6

2018 ◽  
Vol 14 (1) ◽  
pp. 67-76
Author(s):  
Mohanned Mohammed H. AL-Khafaji
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Thickness Ratio ◽  
Depth Of Cut ◽  
Feed Force ◽  
Machining Force ◽  
Force Components

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).

Experimental Investigations of Cutting Parameters Influence on Cutting Forces and Surface Roughness of Quartz Glass

2013 ◽  
Vol 641-642 ◽  
pp. 367-370
Author(s):  
Gui Qiang Liang ◽  
Fei Fei Zhao
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Diamond Wheel ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Feed Force

Abstract In the present study, an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate and depth of cut) on cutting forces (feed force, thrust force and cutting force) and surface roughness in milling of Quartz glas using diamond wheel. The cutting process in the up-cut milling of glass is discussed and the cutting force measured. The cutting force gradually increases with the cutter rotation at the beginning of the cut, and oscillates about a constant mean value after a certain undeformed chip thickness. The results show that cutting forces and surface roughness do not vary much with experimental cutting speed in the range of 55–93 m/min. The suggested models of cutting forces and surface roughness and adequately map within the limits of the cutting parameters considered.

scholarly journals EFFECT OF CUTTING PARAMETERS ON CUTTING FORCES IN TURNING OF CPM 10V STEEL

2021 ◽  
Vol 24 (2) ◽  
pp. 5-8
Author(s):  
Anđelko Aleksić ◽  
◽  
Milenko Sekulić ◽  
Marin Gostimirović ◽  
Dragan Rodić ◽  
...  
Keyword(s):  
Feed Rate ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Physical And Mechanical Properties ◽  
Cutting Parameters ◽  
Radial Force ◽  
Depth Of Cut ◽  
Feed Force ◽  
Cnc Lathe ◽  
Method Analysis

The objective of this paper is to investigate the effect of cutting parameters on cutting forces during turning of CPM 10V steel with coated cutting tool. Machining of CPM 10V steel and finding a suitable tool is very challenging due to its physical and mechanical properties, especially since the machining of this material has not been extensively researched. The experiments were carried out using an Index GU -600 CNC lathe and the cutting forces were measured in process. A three-factorial three-level experimental design was used for the experiments. Statistical method analysis of variance (ANOVA) is applied to study the effects of cutting speed, feed rate, and depth of cut on cutting forces. The results of this study show that depth of cut has the most significant effect on main force and radial force, while feed rate and cutting speed have the most significant effect on feed force. The developed model can be used in the machining industry to predict and analyze cutting parameters for optimal cutting forces.

scholarly journals Analysis of Surface Roughness and Cutting Forces in Hard Turning of 42CrMo4 Steel using Taguchi and RSM Method

Mechanika ◽  
2020 ◽  
Vol 26 (3) ◽  
pp. 231-241 ◽  
Author(s):  
Mustafa ÖZDEMİR ◽  
Mehmet Tuncay KAYA ◽  
Hamza Kemal AKYILDIZ
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Forces ◽  
Tangential Force ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Radial Force ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Feed Force

In this study, effects of cutting speed (V), feed rate (f), depth of cut (a) and tool tip radius (R) on  surface roughness (Ra, Rz, and Rt) and cutting forces (radial force (Fx), tangential force (Fy), and feed force (Fz)) in hard finish turning processes of hardened 42CrMo4 (52 HRC) material was investigated experimentally. Taguchi’s mixed level parameter design (L18) is used for the experimental design (2x1,3x3). The signal-to-noise ratio (S/N) was used in the evaluation of test results.  By using Taguchi method, cutting parameters giving optimum surface roughness and cutting forces were determined. Regression analyses are applied to predict surface roughness and cutting forces. Analysis of variance (ANOVA) is used to determine the effects of the machining parameters on surface roughness and cutting forces. According to ANOVA analysis, the most important cutting parameters were found to be feed rate for surface roughness and depth of cut among cutting forces.  By conducting validation experiments, optimization was seen to be applied successfully.

scholarly journals Validation and optimization of cutting parameters for Ti-6Al-4V turning operation using DEFORM 3D simulations and Taguchi method

2021 ◽  
Vol 8 ◽  
pp. 5
Author(s):  
Japheth Oirere Obiko ◽  
Fredrick Madaraka Mwema ◽  
Michael Oluwatosin Bodunrin
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
Signal To Noise Ratio ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Optimization Of Cutting Parameters ◽  
The Relationship

In this study, we show that optimising cutting forces as a machining response gave the most favourable conditions for turning of Ti-6Al-4V alloy. Using a combination of computational methods involving DEFORM simulations, Taguchi Design of Experiment (DOE) and analysis of variance (ANOVA), it was possible to minimise typical machining response such as the cutting force, cutting power and chip-tool interface temperature. The turning parameters that were varied in this study include cutting speed, depth of cut and feed rate. The optimum turning parameter combinations that would minimise the machining responses were established by using the “smaller the better” criterion and selecting the highest value of Signal to Noise Ratio. Confirmatory simulation revealed that using cutting speed of 120 m/min, 0.25 mm depth of cut and 0.1 mm/rev feed rate, the lowest cutting force of 88.21 N and chip-tool interface temperature of 387.24 °C can be obtained. Regression analysis indicated that the highest correlation coefficient of 0.97 was obtained between cutting forces and the turning parameters. The relationship between cutting forces and the turning parameters was linear since first-order regression model was sufficient.

Cutting Temperature and Cutting Forces Investigation Based on the Taguchi Design Method when High-Speed Milling of Titanium Matrix Composites with PCD Tool

2016 ◽  
Vol 836-837 ◽  
pp. 168-174 ◽  
Author(s):  
Ying Fei Ge ◽  
Hai Xiang Huan ◽  
Jiu Hua Xu
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
High Speed ◽  
Volume Fraction ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
High Speed Milling ◽  
Radial Depth

High-speed milling tests were performed on vol. (5%-8%) TiCp/TC4 composite in the speed range of 50-250 m/min using PCD tools to nvestigate the cutting temperature and the cutting forces. The results showed that radial depth of cut and cutting speed were the two significant influences that affected the cutting forces based on the Taguchi prediction. Increasing radial depth of cut and feed rate will increase the cutting force while increasing cutting speed will decrease the cutting force. Cutting force increased less than 5% when the reinforcement volume fraction in the composites increased from 0% to 8%. Radial depth of cut was the only significant influence factor on the cutting temperature. Cutting temperature increased with the increasing radial depth of cut, feed rate or cutting speed. The cutting temperature for the titanium composites was 40-90 °C higher than that for the TC4 matrix. However, the cutting temperature decreased by 4% when the reinforcement's volume fraction increased from 5% to 8%.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

2020 ◽  
Vol 36 ◽  
pp. 28-46
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Salah Eddine Mechraoui ◽  
Mohamed Athmane Yallese ◽  
Mustapha Temmar
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 316L ◽  
Cutting Power ◽  
Dry Turning ◽  
Optimization Of Cutting Parameters

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

Predictive Modeling and Optimization of Cutting Forces Through RSM and Taguchi Techniques in the Turning of ASTM B574 (Hastelloy C-22)

2018 ◽  
pp. 398-417
Author(s):  
İsmail Kırbaş ◽  
Musa Peker ◽  
Gültekin Basmacı ◽  
Mustafa Ay
Keyword(s):  
Feed Rate ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Rake Angle ◽  
Orthogonal Experimental Design ◽  
Depth Of Cut ◽  
Effective Parameters ◽  
Factors Affecting ◽  
The Impact

In this chapter, the impact of cutting parameters (depth of cut, cutting speed, feed, flow, rake angle, lead angle) on cutting forces in the turning process with regard to ASTM B574 (Hastelloy C-22) material has been investigated. Variance analysis has been applied in order to determine the factors affecting the cutting forces. The optimization of the parameters affecting the surface roughness has been obtained using response surface methodology (RSM) based on the Taguchi orthogonal experimental design. The accuracy of the developed models required for the estimation of the force values (Fx, Fy, Fz) is quite successful. In this study, where the R2 value has been used as the criterion/measure, accuracy values of 93.35%, 95.03%, and 95.09% have been achieved for Fx, Fy, and Fz, respectively. As a result of the ANOVA analysis, the most effective parameters for Fx at a 95% confidence interval are depth of cut, feed rate, flow, and rake angle. The most effective parameter for Fy is depth of cut, while the most effective parameters for Fz are depth of cut, feed rate, and flow, respectively.

RSM Based Investigations on the Effects of Cutting Parameters on Surface Integrity during Cryogenic Hard Turning of AISI 52100

2015 ◽  
Vol 15 (3) ◽  
pp. 309-318 ◽  
Author(s):  
Suha K. Shihab ◽  
Zahid A. Khan ◽  
Arshad Noor Siddiquee
Keyword(s):  
Surface Integrity ◽  
Feed Rate ◽  
Hard Turning ◽  
Desirability Function ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machined Surface ◽  
Aisi 52100 ◽  
Model Equations

AbstractEffect of cryogenic hard turning parameters (cutting speed, feed rate, and depth of cut) on surface roughness (Ra) and micro-hardness (µH) that constitute surface integrity (SI) of the machined surface of alloy steel AISI 52100 is investigated. Multilayer hard surface coated (TiN/TiCN/Al2O3/TiN) insert on CNC lathe is used for turning under different cutting parameters settings. RSM based Central composite design (CCD) of experiment is used to collect data for Ra and µH. Validity of assumptions related to the collected data is checked through several diagnostic tests. The analysis of variance (ANOVA) is used to determine main and interaction effects. Relationship between the variables is established using quadratic regression model. Both Ra and µH are influenced principally by the cutting speed and the feed rate. Model equations are found to predict accurate values of Ra and µH. Finally, desirability function approach for multiple response optimization is used to produce optimum SI.

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.

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