Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

scholarly journals Use of Multi-Objective Optimization Technique (Taguchi-GRA Approach) in Dry Hard Turning of Inconel 625

2020 ◽  
Vol 12 (2) ◽  
pp. 133-142
Author(s):  
Chinmaya PADHY ◽  
Pariniti SINGH
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Removal Rate ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Grey Relational

Current developments in manufacturing industries consider developing a suitable optimization technique for achieving improved machining performance. This study investigates the optimum values of machining parameters required namely –cutting speed (v), feed rate (f) and depth-of-cut (d) during dry hard turning of Inconel 625 with the aim of enhancing the productivity by minimizing surface roughness (Ra), cutting force (Fc), whereas maximizing material removal rate(MRR). This kind of multi-response process variable (MRP) problems usually known as multi-objective optimizations (MOOs) are solved with the help of Taguchi- Grey Relational Approach (T-GRA). Thus, here is a study conducted to apply Taguchi and Grey relational analysis to optimize multiple performance characteristics during dry hard turning of Inconel -625. As a result, the attained process variables, viz., cutting speed (60 m/min), feed rate (0.3 mm/rev), depth- of- cut (0.25mm) lead to value of optimum response variables –mean cutting force (340 N), surface roughness (0.998 μm) and material removal rate (0.786 mm3/min). In this setup, PVD coated carbide tool inserts were used for dry hard machining (turning) operation.

scholarly journals Hard Turning ASSESSMENT on EN31 Steel in Dry and Wet Cooling Environments USING Grey-Fuzzy Hybrid Optimization APPROACH

2021 ◽  
Vol 13 (2) ◽  
pp. 55-62
Author(s):  
Saswat Khatai ◽  
◽  
Ramanuj Kumar ◽  
Ashok Kumar Sahoo ◽  
◽  
...  
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Physical Vapor Deposition ◽  
Hard Turning ◽  
Removal Rate ◽  
Hybrid Optimization ◽  
Optimization Approach ◽  
Dry Cutting ◽  
Coated Carbide

In recent years, machining of hard-to-cut metals by hard turning process is an embryonic technology for machining industry and research development. Hard turning is generally defined as the material removal process of hardened steel having hardness greater than 45 HRC.  The current research presents a comparative hard turning investigation on EN 31 (56 ± 1 HRC) grade steel using physical vapor deposition (PVD) coated carbide tool under dry and wet cooling. The selection of a better cooling strategy among dry and wet cooling was based on the value of obtained surface roughness (Ra) and material removal rate (MRR) in hard turning. Wet cooling exhibited better performance over dry cutting as lower Ra and greater MRR are achieved with wet cooling. Further, considering Taguchi L16 orthogonal array, hard turning experiments were executed in wet cooling and responses like surface roughness (Ra), material removal rate (MRR), and diameter error were studied. Further, the Grey-fuzzy hybrid optimization tool was employed and found improved results relative to the alone grey relational analysis as about 9 % less Ra and 2.612 times more MRR is noticed at the grey fuzzy optimal set of parameters.

scholarly journals Optimization of Multiple Response Using Taguchi-WPCA In ST 60 Tool Steel Turning Process With Minimum Quantity Cooling Lubrication (MQCL) Method

2018 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Dian Ridlo Pamuji ◽  
Muhammad Abdul Wahid ◽  
Abdul Rohman ◽  
Achmad As’ad Sonief ◽  
Moch Agus Choiron
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Orthogonal Array ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Turning Process ◽  
Feeding Speed

A research was conducted for the optimization of the turning process st 60 tool steel with multiple performance characteristics based on the orthogonal array with Taguchi-WPCA method. Minimum Quantity Cooling Lubrication (MQCL) metode was applied as a coolant. The experimental studies were conducted under varying the cutting speed, feeding, depth of cut and type of coolant. The optimized multiple performance characteristics were surface roughness, and material removal rate. An orthogonal array, signal-to-noise ratio, grey relational analysis, weighted pricipal component analysis and analysis of variance were employed to study the multiple performance characteristics. Experimental results show that cutting speed gives the highest contribution for minimize of surface roughness and maximize of material removal rate, followed by feeding speed, type of coolant and depth of cut. The minimum of surface roughness and maximize of material removal rat could be obtained by using the values of cutting speed, feeding speed,  depth of cut and type of coolant of 172.95 m/minute, 0.053 mm/rev, 0.25 mm, and vegetable oil as a coolant respectively.

Study of Surface Roughness and MRR in Turning of SiC Reinforced Al Alloy Composite Using Taguchi Design Method, ANN and PCA Approach under MQL Cutting Condition

2020 ◽  
Vol 1158 ◽  
pp. 115-131
Author(s):  
Md. Rezaul Karim ◽  
Rifat Ahasan Siddique ◽  
Farhana Dilwar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Orthogonal Array ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Principal Component ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Experimental Values

This paper emphases on the effect of various machining constraint on surface roughness and material removal rate in turning SiC reinforced Al alloy composite through taguchi orthogonal array based experimental analysis which has been further optimized using principal component analysis (PCA). Experimental investigation has been conducted under minimum quality lubricant (MQL) cutting environment. Palm oil has been used as lubricant where flow rate and pressure were kept at 120 ml/hr and 8 bar. The whole experiment has been designed using L25 orthogonal array having three input parameters and five different level to measure surface roughness and material removal rate. Taguchi S/N ratio-based optimization has been implemented where smaller the better criteria has been used for surface roughness whereas larger the better criteria has been used for material removal rate. From Analysis of variance, it is observed that cutting speed and feed rate are the most prominent factor for surface roughness. Nevertheless, Depth of cut and cutting speed are the most dominant factor for material removal rate. While comparing the predicted output values with experimental values, MAPE value is found in the range of 0.23 % for surface roughness and 0.045 % for material removal rate which is in very much tolerable range. Correlation coefficient value for experimental values of the resultant output is 0.98286 and 0.99869 respectively which signifies the effectiveness of the whole experiment. Subsequently, machining parameters were optimized using PCA technique. To attain satisfactory response values, depth of cut, cutting speed and feed rate need to be at 0.85 mm, 396 m/min and 0.16 mm/rev respectively. By applying the model, surface roughness of 0.7257 μm and MRR of 53856 mm3/min can be obtained. Keywords: SiC reinforced Al alloy; Turing; Minimum Quality Lubricant; Surface Roughness; MRR; Taguchi orthogonal array; Principal component analysis

scholarly journals Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Arun Kumar Parida ◽  
Bharat Chandra Routara
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Optimal Process ◽  
Level 3 ◽  
Level 1

Taguchi’s design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (Ra). The machining is conducted with Taguchi L9 orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v3-f2-d3. Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v1-f1-d3. Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

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

Effects of Cutting Condition on Surface Roughness when Turning Untreated and Sb-Treated Al-11%Si Alloys Using PVD Coated Tools

2013 ◽  
Vol 315 ◽  
pp. 413-417 ◽  
Author(s):  
Mohsen Marani Barzani ◽  
Mohd Yusof Noordin ◽  
Ali Akhavan Farid ◽  
Saaed Farahany ◽  
Ali Davoudinejad
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Manufacturing Processes ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Fabrication Cost ◽  
Coated Tools ◽  
Experimental Trials ◽  
Cutting Speeds

Surface roughness is an important output in different manufacturing processes. Its characteristic affects directly the performance of mechanical components and the fabrication cost. In this current work, an experimental investigation was conducted to determine the effects of various cutting speeds and feed rates on surface roughness in turning the untreated and Sb-treated Al-11%Si alloys. Experimental trials carried out using PVD TIN coated inserts. Experiments accomplished under oblique dry cutting when three different cutting speeds have been used at 70, 130 and 250 m/min with feed rates of 0.05, 0.1 and 0.15 mm/rev, whereas depth of cut kept constant at 0.05 mm. The results showed that Sb-treated Al-11%Si alloys have poor surface roughness in comparison to untreated Al-11%Si alloy. The surface roughness values reduce with cutting speed increment from 70 m/min to 250 m/min. Also, the surface finish deteriorated with increase in feed rate from 0.5 mm/rev to 0.15 mm/rev.

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