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.

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.

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%.

In-Process Prediction of Surface Roughness in Grinding Process by Monitoring of Cutting Force Ratio

2014 ◽  
Vol 627 ◽  
pp. 29-34 ◽  
Author(s):  
Vichaya Thammasing ◽  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Spindle Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Average Surface Roughness ◽  
Average Surface ◽  
Developed Surface ◽  
Force Ratio

The purpose of this research is to develop the models to predict the average surface roughness and the surface roughness during the in-process grinding by monitoring the cutting force ratio. The proposed models are developed based on the experimentally obtained results by employing the exponential function with four factors, which are the spindle speed, the feed rate, the depth of cut, and the cutting force ratio. The experimentally obtained results showed that the dimensionless cutting force ratio is usable to predict the surface roughness during the grinding process, which can be calculated and obtained by taking the ratio of the corresponding time records of the cutting force Fy in the spindle speed direction to that of the cutting force Fz in the radial wheel direction. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method at 95% confident level. The experimentally obtained models have been verified by the new cutting tests. It is proved that the developed surface roughness models can be used to predict the in-process surface roughness with the high accuracy of 93.9% for the average surface roughness and 92.8% for the surface roughness.

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.

scholarly journals Assessments of Process Parameters on Cutting Force and Surface Roughness during Drilling of AA7075/TiB2 In Situ Composite

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1726
Author(s):  
S. Parasuraman ◽  
I. Elamvazuthi ◽  
G. Kanagaraj ◽  
Elango Natarajan ◽  
A. Pugazhenthi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Basic Class ◽  
Tib2 Particles ◽  
The Impact ◽  
Loss Of Strength

Reinforced aluminum composites are the basic class of materials for aviation and transport industries. The machinability of these composites is still an issue due to the presence of hard fillers. The current research is aimed to investigate the drilling topographies of AA7075/TiB2 composites. The samples were prepared with 0, 3, 6, 9 and 12 wt.% of fillers and experiments were conducted by varying the cutting speed, feed, depth of cut and tool nose radius. The machining forces and surface topographies, the structure of the cutting tool and chip patterns were examined. The maximum cutting force was recorded upon increase in cutting speed because of thermal softening, loss of strength discontinuity and reduction of the built-up-edge. The increased plastic deformation with higher cutting speed resulted in the excess metal chip. In addition, the increase in cutting speed improved the surface roughness due to decrease in material movement. The cutting force was decreased upon high loading of TiB2 due to the deterioration of chips caused by fillers. Further introduction of TiB2 particles above 12 wt.% weakened the composite; however, due to the impact of the microcutting action of the fillers, the surface roughness was improved.

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.

Surface Roughness Prediction Model in Machining of 34CrMo4 Steel with Several Tools Using Response Surface Methodology

2012 ◽  
Vol 445 ◽  
pp. 90-95
Author(s):  
Hamed Barghikar ◽  
Amin Poursafar ◽  
Abbas Amrollahi
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Mean Values ◽  
Roughness Model ◽  
34Crmo4 Steel

The surface roughness model in the turning of 34CrMo4 steel was developed in terms of cutting speed, feed rate and depth of cut and tool nose radius using response surface methodology. Machining tests were carried out using several tools with several tool radius under different cutting conditions. The roughness equations of cutting tools when machining the steels were achieved by using the experimental data. The results are presented in terms of mean values and confidence levels.The established equation and graphs show that the feed rate and cutting speed were found to be main influencing factor on the surface roughness. It increased with increasing the feed rate and depth of cut, but decreased with increasing the cutting speed, respectively. The variance analysis for the second-order model shows that the interaction terms and the square terms were statistically insignificant. However, it could be seen that the first-order affect of feed rate was significant while cutting speed and depth of cut was insignificant.The predicted surface roughness model of the samples was found to lie close to that of the experimentally observed ones with 95% confident intervals.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

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

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is 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.

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