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.

Optimization of Cutting Parameters during Turning of AISI-310 Using Response Surface Methodology

2016 ◽  
Vol 854 ◽  
pp. 45-51
Author(s):  
S. Nandhakumar ◽  
R. Vijayakumar ◽  
Senthil Padmavathy ◽  
N. Nagasundaram
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Parameters Optimization ◽  
Depth Of Cut ◽  
Optimization Of Cutting Parameters

Design of Experiments is employed to study the stimulus of cutting parameters such as feed rate, spindle speed, depth of cut in the turning operation of AISI-310 and optimizing the value of those parameters for getting the higher material removal rate (MRR) and minimal surface roughness. A prediction model has been developed by using the above influencing parameters. For the purpose of parameters optimization we investigate the parameters using Response Surface Methodology (RSM). It is shown that feed rate is the main parameter in influencing the surface roughness, which is being followed by spindle speed and depth of cut. It is found that surface roughness and feed rate were directly proportional to each other for some extent. The confirmation tests were carried out to with the optimum set of parameters and are verified with test results. The comparison of above two results were found to be good with maximum error within 5% on comparing it with the predicted model.

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

Down Milling Cutting Parameters Optimization Utilizing the Two Level Full Factorial Design Approach

2016 ◽  
Vol 863 ◽  
pp. 57-61
Author(s):  
Jailani Ismail ◽  
Martini Muhamad ◽  
Saiful Bahri Mohamed ◽  
A. Mohd ◽  
Wan Noor Fatihah Mohamad ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Factorial Design ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Full Factorial Design ◽  
Spindle Speed ◽  
Experimental Methodology ◽  
Depth Of Cut ◽  
Cutting Parameters Optimization ◽  
Full Factorial

The direction of feeding the work piece and cutter rotation determines the type of machining mode either it is up milling or down milling. Each of this machining mode affects the quality of machined surface produced. This paper described the experimental design of down milling operation on a stack of multidirectional CFRP/Al2024. Three cutting parameters were considered namely, spindle speed (N), feed rate (fr) and depth of cut (dc). Two level full factorial design was utilized to plan systematic experimental methodology. The analysis of variance (ANOVA) was used to analyse the influence and the interaction factors associated to surface quality. The results show that the depth of cut is the most significant factor for Al2024, and for CFRP the spindle speed and feed rate are significant. Surface roughness of CFRP is found to be at 0.594 μm at the setting of N = 11750 rpm, fr = 750 mm/min and dc = 0.255 mm. Meanwhile for Al2024, the surface roughness is found to be at 0.32 μm. The validation test showed average deviation of predicted to actual value surface roughness is 3.11% for CFRP and 3.43% for Al2024.

Evaluation of Surface Roughness and Tool Wear in High Speed Machining of Inconel 718

2019 ◽  
Vol 943 ◽  
pp. 66-71
Author(s):  
Moola Mohan Reddy ◽  
Viviana Yong Chai Nie
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Feed Rate ◽  
Inconel 718 ◽  
High Speed ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
High Speed Machining ◽  
Super Alloy

This research work considered the high speed milling operation of Inconel 718 using a 4 flute solid carbide end mill tool without the use of coolant. Inconel 718 is a Nickel based Heat Resistance Super Alloy (HRSA) that is vastly used in the aerospace industries due to its excellent corrosion resistance and good mechanical properties. However, Inconel 718 is considered as a difficult-to-cut super alloy, which poses several problems when machining the material. The aim of this work is to investigate the effect and the influence of cutting parameters (feed rate, spindle speed, and depth of cut) on the quality of the machined surface as well as to evaluate the tool wear after machining. This evaluation of the surface roughness was done using a CNC milling machine at various parameters range for the values of feed rate (50-150 mm/min), spindle speed (2000-4000 RPM), and depth of cut (0.05-0.1 mm). The experiment was designed using Response Surface Analysis Method with Central Composite Design (CCD) to optimize the experimentation. The resulting tool wear and surface roughness after high speed machining were then analysed using ANOVA to determine the cutting parameters which is most affecting the surface roughness.

scholarly journals Effect of Turning Parameters and Reinforcement on the Surface Roughness of Hybrid Aluminium Metal Matrix Composite

2019 ◽  
Vol 9 (1) ◽  
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Feed Rate ◽  
Metal Matrix ◽  
Matrix Material ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Pure Aluminium ◽  
Machining Time

Since last two decades, technology has changed rapidly in each industry from consumer goods to aerospace. To quench the thrust of technology advancement of industries, the research community has to develop the novel materials. As a part of the ongoing process of material development and machinability study, present work was carried out to find out the effect of various cutting parameters on surface roughness and machining time of aluminium Hybrid Metal Matrix Composites (AHMMCs). In this study, pure aluminium was used as a matrix material while; B4C, Mg, Ti and Graphite were used as reinforcement. It was observed that 2202 RPM spindle speed, 0.18 mm/rev feed rate and 1.1 mm depth of cut are the optimum turning parameters with PCD insert. While machining of same composites using carbide insert, optimum spindle speed, feed rate and depth of cut are 2091 RPM, 0.19 mm/rev and 1.1 mm respectively. Moreover, PCD insert provides better surface finish compared to carbide coated insert for same cutting conditions. Moreover, the effect of reinforcement particles presence on the surface finish was also investigated and it was concluded that reinforcement presence reduces the surface roughness considerably. Particularly presence of Mg and graphite reduces surface finish significantly.

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.

Investigation of Surface Roughness and Material Removal Rate for High Speed Micro End Milling on PMMA

2015 ◽  
Vol 1115 ◽  
pp. 12-15
Author(s):  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
Abdul Rahman Mohamed ◽  
Md. Sazzad Hossein Chowdhury
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Micro End Milling

Micro end milling is one of the most important micromachining process and widely used for producing miniaturized components with high accuracy and surface finish. This paper present the influence of three micro end milling process parameters; spindle speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR). The machining was performed using multi-process micro machine tools (DT-110 Mikrotools Inc., Singapore) with poly methyl methacrylate (PMMA) as the workpiece and tungsten carbide as its tool. To develop the mathematical model for the responses in high speed micro end milling machining, Taguchi design has been used to design the experiment by using the orthogonal array of three levels L18 (21×37). The developed models were used for multiple response optimizations by desirability function approach to obtain minimum Ra and maximum MRR. The optimized values of Ra and MRR were 128.24 nm, and 0.0463 mg/min, respectively obtained at spindle speed of 30000 rpm, feed rate of 2.65 mm/min, and depth of cut of 40 μm. The analysis of variance revealed that spindle speeds are the most influential parameters on Ra. The optimization of MRR is mostly influence by feed rate. Keywords:Micromilling,surfaceroughness,MRR,PMMA

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