Surface Roughness Prediction Based on Cutting Parameters and Nose Radius in Precision Turning

2007 ◽  
Vol 329 ◽  
pp. 539-544 ◽  
Author(s):  
Ying Chun Liang ◽  
Yuan Sheng Zhai ◽  
H.X. Wang ◽  
Qing Shun Bai ◽  
Y. Zhao
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Ratio Test ◽  
Nose Radius ◽  
Precision Turning ◽  
Proposed Model ◽  
Roughness Prediction ◽  
Optimal Cutting Parameters

In precision turning, the quality of surface finish is an important requirement for machined workpiece. Thus, the choice of optimal cutting parameters is very important for controlling the required surface quality. The focus of the present study is to find a correlation between surface roughness and cutting parameters (feed rate, depth of cut) and nose radius in turning 3J33 maraging steel, and to derive mathematical models for the predicted surface roughness based on both of cutting parameters and nose radius. The experimental design is carried out using the quadratic rotary combination design. The regression analysis shows feed rate and nose radius influence surface roughness significantly. With F-ratio test the proposed model is adequate. The method could be useful in predicting roughness parameters as a function of cutting parameters and nose radius.

Cutting Force Modeling in Precision Turning 3J33 Alloy for Tool with Nose Radius

2009 ◽  
Vol 69-70 ◽  
pp. 167-171
Author(s):  
Yuan Sheng Zhai ◽  
Yu Wang ◽  
Ying Chun Liang
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
Cutting Parameters ◽  
Multiple Correlation Coefficient ◽  
Depth Of Cut ◽  
Ratio Test ◽  
Nose Radius ◽  
Precision Turning ◽  
Force Modeling

Based on experimental results, a predictive model with certain constraints of cutting parameters (feed rate and depth of cut) and nose radius for cutting forces is solved in precision turning 3J33 alloy. The proposed model is adequate with F-ratio test and multiple correlation coefficient of it. Regression analysis shows that depth of cut and feed rate influence the principal cutting force significantly. The goal of this study is to predict cutting forces under certain constraints of cutting parameters and nose radius.

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.

Optimization of Machining Parameters of Valve Steel SUH03 (X45CrSiMo10-2) Using Gray Based Taguchi Method

2015 ◽  
Vol 813-814 ◽  
pp. 376-381 ◽  
Author(s):  
B. Yazhini ◽  
S. Rajeswari ◽  
Sivasakthivel
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Nose Radius ◽  
Valve Steel ◽  
Optimal Cutting Parameters

This paper embarks the machining parameters of Turning by optimization using Taguchi’s approach. The optimization is very essential in order to obtain the expected surface quality. The results of cutting parameters of optimization is seen in the Surface Roughness, Tool wear and MRR of the material. The L18 Orthogonal array has been chosen for the optimization of Valve Steel SUH03.The uncoated carbide inserts were used and the four parameters Speed, Feed, Depth of Cut and Nose Radius has been taken as input parameters. The Signal to Noise ratio and Analysis of Variance software has been analyzed using Minitab software through which the optimal cutting parameters of the best surface roughness, tool wear and MRR has been obtained. The final results have been compared by the Gray relational analysis to find the optimum machining conditions of all the parameters.

Modelling of Surface Roughness in Ultra-High Precision Turning of an RGP Contact Lens Polymer

2017 ◽  
Vol 753 ◽  
pp. 183-187 ◽  
Author(s):  
Muhammad M. Liman ◽  
Khaled Abou-El-Hossein ◽  
Abubakar I. Jumare ◽  
Peter Babatunde Odedeyi ◽  
Abdulqadir N. Lukman
Keyword(s):  
Surface Roughness ◽  
Contact Lens ◽  
Feed Rate ◽  
Research Work ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Optimal Cutting Parameters ◽  
Diamond Cutting Tool

Contact lens manufacture requires high accuracy and surface integrity. Surface roughness an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. This research work is therefore aimed at developing a predictive surface roughness model and investigate a finish cutting conditions of ONSI-56 contact lens polymer with a monocrystalline diamond cutting tool. In this work, a novel surface roughness prediction model, in which the feed rate, cutting speed and depth of cut are considered is developed. This combined process was successfully modeled using a Box–Behnken design (BBD) with response surface methodology (RSM). The effects of feed rate, cutting speed and depth of cut were investigated. Analysis of variance (ANOVA) showed that the proposed quadratic model effectively interpreted the experimental data with coefficients of determination of R2 = 0.89 and adjusted R2 = 0.84. The worse surface value was obtained at high feedrate and low spindle speed.

scholarly journals Effect of cutting parameters on surface roughness in turning of annealed AISI-1020 steel

2018 ◽  
Vol 27 (47) ◽  
pp. 109
Author(s):  
Omar Zurita ◽  
Verónica Di-Graci ◽  
María Capace
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimum Parameters ◽  
Proposed Model ◽  
Roughness Analysis ◽  
Regression Techniques ◽  
Predicted Values

This study focuses on the effects of cutting parameters such as cutting speed (Vc), feed rate (f), and depth of cut (d) on roughness induced on the surface of annealed AISI 1020 steel when machined by turning using carbide insert tools. The results indicated that surface roughness increased when feed rate increased and cutting speed decreased. Depth of cut slightly influenced roughness. Analysis of variance and multiple regression techniques were used to formulate a quantitative equation for estimating predicted values of roughness as functions of the cutting parameters. The results showed that cutting speed is the most influencing parameter on surface roughness (69.35%), followed by feed rate (30.13%), while depth of cut failed to affect the responses substantially (0.52%). The proposed model can be used to select the optimum parameters for minimum surface roughness in metal turning.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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

SURFACE ROUGHNESS OF MAGNESIUM ALLOY AZ91D IN HIGH SPEED MILLING

2016 ◽  
Vol 78 (6-9) ◽  
Author(s):  
Mohd Shahfizal Ruslan ◽  
Kamal Othman ◽  
Jaharah A.Ghani ◽  
Mohd Shahir Kassim ◽  
Che Hassan Che Haron
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Parameters ◽  
Experimental Result ◽  
Depth Of Cut ◽  
Polishing Process ◽  
High Speed Cutting ◽  
Radial Depth

Magnesium alloy is a material with a high strength to weight ratio and is suitable for various applications such as in automotive, aerospace, electronics, industrial, biomedical and sports. Most end products require a mirror-like finish, therefore, this paper will present how a mirror-like finishing can be achieved using a high speed face milling that is equivalent to the manual polishing process. The high speed cutting regime for magnesium alloy was studied at the range of 900-1400 m/min, and the feed rate for finishing at 0.03-0.09 mm/tooth. The surface roughness found for this range of cutting parameters were between 0.061-0.133 µm, which is less than the 0.5µm that can be obtained by manual polishing. Furthermore, from the S/N ratio plots, the optimum cutting condition for the surface roughness can be achieved at a cutting speed of 1100 m/min, feed rate 0.03 mm/tooth, axial depth of cut of 0.20 mm and radial depth of cut of 10 mm. From the experimental result the lowest surface roughness of 0.061µm was obtained at 900 m/min with the same conditions for other cutting parameters. This study revealed that by milling AZ91D at a high speed cutting, it is possible to eliminate the polishing process to achieve a mirror-like finishing.

scholarly journals Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2998 ◽  
Author(s):  
Kubilay Aslantas ◽  
Mohd Danish ◽  
Ahmet Hasçelik ◽  
Mozammel Mia ◽  
Munish Gupta ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Small Diameter ◽  
Cutting Parameters ◽  
Ti6al4v Alloy ◽  
Depth Of Cut ◽  
Turning Process ◽  
Micro Turning

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

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