Development of Surface Roughness Prediction by Utilizing Dynamic Cutting Force Ratio

2014 ◽  
Vol 490-491 ◽  
pp. 207-212 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Kanyakarn Samanmit ◽  
Suthas Ratanakuakangwan
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Surface Roughness Prediction ◽  
Dynamic Cutting Force ◽  
Force Ratio ◽  
Roughness Prediction ◽  
Cnc Turning Process ◽  
Coated Carbide

This paper presents the development of the in-process surface roughness prediction in the CNC turning process of the plain carbon steel with the coated carbide tool by utilizing the dynamic cutting force ratio. The dynamic cutting forces are measured to analyze the relation between the surface roughness and the cutting conditions. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with six factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the dynamic cutting force ratio. The dynamic cutting force ratio can be calculated and obtained by taking the ratio of the corresponding time records of the area of the dynamic feed force to that of the dynamic main force. The relation between the dynamic cutting force ratio and the surface roughness can be proved by the obtained frequency of them in frequency domain which are the same frequency. The proposed model has been proved by the new cutting tests with the high accuracy of 91.04% by utilizing the dynamic cutting force ratio.

Advanced Prediction of Surface Roughness by Monitoring of Dynamic Cutting Forces in CNC Turning Process

2012 ◽  
Vol 239-240 ◽  
pp. 661-669 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Depth Of Cut ◽  
Turning Process ◽  
Cnc Turning ◽  
Roughness Model ◽  
Developed Surface ◽  
Dynamic Cutting Force ◽  
Force Ratio ◽  
Cnc Turning Process

The aim of this research is to investigate the relation between the surface roughness and the dynamic cutting force ratio during the in-process cutting in CNC turning process. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, and the dynamic cutting force ratio. The dynamic cutting force ratio is proposed to predict the surface roughness during the cutting, which can be calculated and obtained by taking the ratio of the corresponding time records of the area of thedynamic feed force to that of the dynamic main force. The in-process relation between dynamic cutting force ratio and surface roughness can be proved by the frequency of the dynamic cutting force which corresponds to the surface roughnessfrequency. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method at 95% confident level. The proposed model has been verified by the new cutting tests. It is understood that the developed surface roughness model can be used to predict the in-process surface roughness with the high accuracy of 90.3% by utilizing the dynamic cutting force ratio.

In-Process Monitoring and Prediction of Surface Roughness in CNC Turning Process

2011 ◽  
Vol 199-200 ◽  
pp. 1958-1966 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Turning Process ◽  
Roughness Model ◽  
Force Ratio ◽  
Cnc Turning Process

The objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been verified to monitor the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It has been proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

Intelligent Monitoring and Estimation of Surface Roughness on CNC Turning

2010 ◽  
Vol 443 ◽  
pp. 376-381 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Monitoring ◽  
Prediction Interval ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Roughness Model ◽  
Force Ratio

In order to realize an intelligent machine tool, an in-process monitoring system is developed to estimate the in-process surface roughness. The objective of this research is to propose a method to estimate the surface roughness during the in-process cutting by utilizing the in-process monitoring of cutting forces. The proposed in-process surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval of the in-process surface roughness model has been also presented to monitor and control the in-process estimated surface roughness at 95% confident level. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be effectively used to monitor and estimate the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy achieved.

Surface Roughness Prediction Using Numerical Scheme and Feedback Control

2015 ◽  
Author(s):  
Michael K. O. Ayomoh ◽  
Khaled A. Abou-El-Hossein ◽  
Sameh F. M. Ghobashi
Keyword(s):  
Surface Roughness ◽  
Feedback Control ◽  
High Carbon Steel ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Experimental Sample ◽  
Surface Roughness Prediction ◽  
Difference Analysis ◽  
Roughness Prediction

This paper proposes a numerical modelling scheme for surface roughness prediction. The approach is premised on the use of 3D difference analysis method enhanced with the use of feedback control loop where a set of adaptive weights are generated. The surface roughness values utilized in this paper were adapted from [1]. Their experiments were carried out using S55C high carbon steel. A comparison was further carried out between the proposed technique and those utilized in [1]. The experimental design has three cutting parameters namely: depth of cut, feed rate and cutting speed with twenty-seven experimental sample-space. The simulation trials conducted using Matlab software is of two sub-classes namely: prediction of the surface roughness readings for the non-boundary cutting combinations (NBCC) with the aid of the known surface roughness readings of the boundary cutting combinations (BCC). The following simulation involved the use of the predicted outputs from the NBCC to recover the surface roughness readings for the boundary cutting combinations (BCC). The simulation trial for the NBCC attained a state of total stability in the 7th iteration i.e. a point where the actual and desired roughness readings are equal such that error is minimized to zero by using a set of dynamic weights generated in every following simulation trial. A comparative study among the three methods showed that the proposed difference analysis technique with adaptive weight from feedback control produced a much accurate output as against the abductive and regression analysis techniques presented in [1].

Development of Surface Roughness Prediction for Steel in CNC Turning by Using Response Surface Method

2011 ◽  
Vol 335-336 ◽  
pp. 921-926
Author(s):  
Siriwan Chanphong ◽  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Response Surface ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Plain Carbon Steel ◽  
Cutting Condition ◽  
Surface Roughness Prediction ◽  
Roughness Prediction ◽  
Coated Carbide

This research presents the development of the surface roughness prediction in the turning process of the plain carbon steel with the coated carbide tool by using the response surface analysis with the Box-Behnken design. The effects of cutting parameters on the cutting force and the cutting temperature are investigated. The cutting force and the cutting temperature are measured to help analyze the relation between the surface roughness and the cutting conditions. The models of cutting force ratio and the cutting temperature are also proposed based on the experimental data. The surface plots are constructed to determine the optimum cutting condition referring to the minimum surface roughness.

Examining the machinability of 38MnVS6 microalloyed steel, cooled in different mediums after hot forging with the coated carbide and ceramic tool

2021 ◽  
pp. 095440622098449
Author(s):  
Barış Özlü ◽  
Halil Demir ◽  
Mustafa Türkmen ◽  
Süleyman Gündüz
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Microalloyed Steel ◽  
Cutting Tools ◽  
Experimental Studies ◽  
Hot Forging ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ceramic Tool ◽  
Coated Carbide

In this study, the effect of the microstructure, hardness, and cutting speed on main cutting force and surface roughness in medium carbon microalloyed steel cooled in different mediums after hot forging, was investigated. As-received sample, which was not hot forged, and the samples cooled in the sand, air, oil, and polymerized water after hot forging were used for the experimental studies. The machinability tests were performed via turning method by using coated carbide and coated ceramic cutting tools with five cutting speed (120, 150, 180, 210, and 240 m/min), constant feed rate (0.04 mm/rev), and constant depth of cut (0.6 mm). The microstructure examinations of the samples were carried out and their hardness values were determined. Also, the wear of cutting tools were examined with scanning electron microscope. In the experimental study, it was revealed that the microstructure, hardness and cutting speed had a significant effect on the surface roughness values of the samples cooled in dissimilar environments following forging. Moreover, the samples cooled in air and polymerized water, whose hardness increased depending on the increase in the cooling rate, had the highest cutting force after machining by using the coated carbide and ceramic tool.

scholarly journals Dry turning of X2CrNi18-09 using coated carbide tools: modelling and optimization of multiple performance characteristics

Mechanika ◽  
2019 ◽  
Vol 25 (6) ◽  
pp. 487-500
Author(s):  
Septi Boucherit ◽  
Sofiane Berkani ◽  
Mohamed Athmane Yallese ◽  
Abdelkrim Haddad ◽  
Salim Belhadi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Removal Rate ◽  
Desirability Function ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Work Piece ◽  
Machining Parameters ◽  
Coated Carbide

The present paper investigates the cutting parameters pertaining to the turning of X2CrNi18-09 austenitic stainless steel that are studied and optimized using both RSM and desirability approaches. The cutting tool inserts used are the CVD coated carbide. The cutting speed, the feed rate and the depth of cut represent the main machining parameters considered. Their influence on the surface roughness and the cutting force are further investigated using the ANOVA method. The results obtained lead to conclude that the feed rate is the surface roughness highest influencing parameter with a contribution of 89.69%.The depth of cut and the feed rate are further identified as the most important parameters affecting the cutting force with contributions of 46.46% and 39.04% respectively. The quadratic mathematical models presenting the progression of the surface roughness and the cutting force and based on the machining parameters considered (cutting speed, feed rate and depth of cut) were obtained through the application of the RSM method. They are presented and compared to the experimental results. Good agreement is found between the two sections of the investigation. Furthermore, the flank wear of the CVD-coated carbide tool (GC2015) is found to increase with both cutting speed and cutting time. A higher tool life represented by t=44min is observed at cutting speed, feed rate and depth of cut of 280m/min,0.08mm/rev and 0.2mm respectively. Moreover and at low cutting speeds, the formation of micro weld is noticed and leads to an alteration of the surface roughness of the work piece. Finally, optimizing the machining parameters with the objective of achieving an improved surface roughness was accomplished through the application of the Desirability Function approach. This enabled to finding out the optimal parameters for maximal material removal rate and best surface quality for a cutting speed of 350m/min, a feed rate of 0.088 mm/rev and a depth of cut of 0.9mm.  

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

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

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