scholarly journals Optimization of Multiple Roughness Characteristics for Turning of AISI 1040 Steel under Different Cutting Conditions

2017 ◽  
Vol 10 ◽  
pp. 1-10 ◽  
Author(s):  
Tanveer Haque ◽  
Shubham Kumar ◽  
Devjyoti Upadhaya ◽  
Manik Barman ◽  
Arkadeb Mukhopadhyay
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Medium Carbon Steel ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Medium Carbon ◽  
Relational Analysis ◽  
Optimal Surface ◽  
Grey Relational ◽  
Different Levels

The present work aims to optimize multiple roughness characteristics i.e. centre line average, root mean square and mean line peak spacing roughness parameters for AISI 1040 medium carbon steel for turning operation. The turning parameters considered are feed rate, depth of cut and cutting condition and are varied at three different levels. Since the present investigation considers three process parameters at three different levels, the combinations laid down in Taguchi’s L9orthogonal array is employed to carry out the experiments. Grey relational analysis is used for the optimization. Optimal surface roughness is achieved for a depth of cut of 0.4 mm, feed rate of 0.07 mm/rev and under water cooled cutting condition. Analysis of variance revealed the highest contribution from feed rate in controlling the surface roughness.

scholarly journals Optimum combination of process parameters to optimize Surface Roughness and Chip Thickness during End Milling of Aluminium 6351-T6 Alloy Using Taguchi Grey Relational Analysis

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Reddy Sreenivasulu
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Feed Rate ◽  
End Milling ◽  
Chip Thickness ◽  
Optimum Combination ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

In any machining operations, quality is the important conflicting objective. In order to give assurance for high productivity, some extent of quality has to be compromised. Similarly productivity will be decreased while the efforts are channelized to enhance quality. In this study,  the experiments were carried out on a CNC vertical machining center (KENT and INDIA Co. Ltd, Taiwan make) to perform 10mm slots on Al 6351-T6 alloy work piece by K10 carbide, four flute end milling cutter as per taguchi design of experiments plan by L9 orthogonal array was choosen to determine experimental trials. Furthermore the spindle speed (rpm), the feed rate (mm/min) and depth of cut (mm) are regulated in these experiments. Surface roughness and chip thickness was measured by a surface analyser of Surf Test-211 series (Mitutoyo) and Digital Micrometer (Mitutoyo) with least count 0.001 mm respectively. Grey relational analysis was employed to minimize surface roughness and chip thickness by setting of optimum combination of machining parameters. Minimum surface roughness and chip thickness obtained with 1000 rpm of spindle speed, 50 mm/min feed rate and 0.7 mm depth of cut respectively. Confirmation experiments showed that Gray relational analysis precisely optimized the drilling parameters in drilling of Al 6351-T6 alloy. 

EXPERIMENTAL INVESTIGATION OF COCONUT OIL WITH NANOBORIC ACID DURING MILLING OF INCONEL 625 USING TAGUCHI-GREY RELATIONAL ANALYSIS

2020 ◽  
pp. 2150008
Author(s):  
T. MOHANRAJ ◽  
P. RAGAV ◽  
E. S. GOKUL ◽  
P. SENTHIL ◽  
K. S. RAGHUL ANANDH
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Flank Wear ◽  
Coconut Oil ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Individual Response ◽  
Relational Analysis ◽  
Grey Relational ◽  
Taguchi’S Design Of Experiments

This study is based on Taguchi’s design of experiments along with grey relational analysis (GRA) to optimize the milling parameters to minimize surface roughness, tool wear, and vibration during machining of Inconel-625 while using coconut oil as cutting fluid (CF). The experiments were conducted based on Taguchi’s L9 orthogonal array (OA). Taguchi’s S/N was used for identifying the optimal cutting parameter for individual response. Analysis of variance (ANOVA) was employed to analyze the outcome of individual parameters on responses. The surface roughness was mostly influenced by feed. Flank wear was influenced by speed and the vibration was mostly influenced by the depth of cut as well as speed. The multi-response optimization was done through GRA. From GRA, the optimal parameters were identified. Further, nanoboric acid of 0.5 and 0.9[Formula: see text]wt.% was mixed with coconut oil to enhance lubricant properties. Coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid minimizes the surface roughness and flank wear by 3.92% and 6.28% and reduces the vibration in the [Formula: see text]-axis by 4.85%. The coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid performs better than coconut oil with 0.9[Formula: see text]wt.% of nano boric acid and base oil.

Investigation of Chip and Surface Roughness when Milling AISI304 Stainless Steel

2017 ◽  
Vol 889 ◽  
pp. 152-158
Author(s):  
K. Kadirgama ◽  
K. Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Feed Rate ◽  
Prediction Models ◽  
Cutting Speed ◽  
Experimental Result ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Axial Depth

Stainless steel was used for many engineering applications. The optimum parameters needs to be identify to save the cutting tool usage and increase productivity. The purpose of this study is to develop the surface roughness mathematical model for AISI 304 stainless steel when milling using TiN (CVD) carbide tool. The milling process was done under various cutting condition which is cutting speed (1500, 2000 and 2500 rpm), feed rate (0.02, 0.03 and 0.04 mm/tooth) and axial depth (0.1, 0.2 and 0.3 mm). The first order model and quadratic model have been developed using Response Surface Method (RSM) with confident level 95%. The prediction models were comparing with the actual experimental results. It is found that quadratic model much fit the experimental result compare to linear model. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. Besides that, it is shown that the influence of cutting speed and feed rate are much higher on surface roughness compare to depth of cut. The optimum cutting speed, feed rate and axial depth is 2500 rpm, 0.0212 mm/tooth and 0.3mm respectively. Besides that, continues chip is produced at cutting speed 2500 rpm meanwhile discontinues chip produced at cutting speed 1500 rpm.

Modelling and Optimization of Machining parameters for Turning Automotive Shafts using RSM and Grey Relational Analysis

2020 ◽  
Vol 12 (4) ◽  
Author(s):  
S. Dinesh ◽  
K. Rajaguru ◽  
K. Saravanan ◽  
R. Yokeswaran ◽  
V. Vijayan
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Input Parameters ◽  
Grey Relational ◽  
Central Composite ◽  
Modelling And Optimization

Automotive shafts require maximum strength with regard to axial, bending and torsional loading to transmit power to various parts of a vehicle. Hence, it is very critical to analyse the manufacturing process and its governing parameters to exercise control over the surface properties of the shaft as it needs to be precisely manufactured in terms of dimensions and the surface roughness. The effect of three input parameters over two responses are considered as two major criteria's for production of shaft. The input parameters are speed, feed and depth of cut whereas the responses are material removal rate and surface roughness. Central Composite design was used and experimental results were analysed with Response Surface Methodology. ANOVA analysis was carried out to identify the most contributing parameter for MRR and SR. Grey Relational Analysis was adopted to identify the most feasible combination of machining parameters for turning process. The optimized parameter is identified as speed of 1000 rpm, 0.15 mm of feed and 0.35 mm of depth of cut using Grey Relational Analysis.

Wear Mechanism of Ti(C, N)-Based Cermet Cutting Tools in the Machining of Normalized Medium Carbon Steel AISI1045

2007 ◽  
Vol 353-358 ◽  
pp. 792-795 ◽  
Author(s):  
Zeng Min Shi ◽  
Yong Zheng ◽  
Wen Jun Liu
Keyword(s):  
Carbon Steel ◽  
Wear Mechanism ◽  
Flank Wear ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Medium Carbon Steel ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Vacuum Sintering ◽  
Medium Carbon

Ti(C, N)-based cermet cutting tools were prepared by vacuum sintering and tested in dry machining of normalized medium carbon steel (AISI1045) at various combinations of cutting speed (Vc), feed rate (f), and depth of cut (ap). And the wear mechanism was investigated in detail using scanning electron microscopy (SEM) and electron-probe microanalysis (EPMA). Comparing to the cemented carbide YT15 and cermet TN20, the newly fabricated cermet tools exhibited better performance and higher wear resistance. For Ti(C, N)-based cermet tools, the wear mechanism was predominantly controlled by the flank wear under all cutting condition. It was found that the removal of the ceramic grain and abrasive wear were the main source of tools failure. In addition, adhesion and oxidation were also observed.

scholarly journals PREDICTION OF TANGENTIAL CUTTING FORCE IN END MILLING OF MEDIUM CARBON STEEL BY COUPLING DESIGN OF EXPERIMENT AND RESPONSE SURFACE METHODOLOGY

1970 ◽  
Vol 40 (2) ◽  
pp. 95-103 ◽  
Author(s):  
Md. Anayet Patwari ◽  
A.K.M. Nurul Amin ◽  
Waleed F. Faris
Keyword(s):  
Response Surface Methodology ◽  
Carbon Steel ◽  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Medium Carbon Steel ◽  
Depth Of Cut ◽  
Medium Carbon

The present paper discusses the development of the first and second order models for predicting the tangential cutting force produced in end-milling operation of medium carbon steel. The mathematical model for the cutting force prediction has been developed, in terms of cutting parameters cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). All the individual cutting parameters affect on cutting forces as well as their interaction are also investigated in this study. The second order equation shows, based on the variance analysis, that the most influential input parameter was the feed rate followed by axial depth of cut and, finally, by the cutting speed. Central composite design was employed in developing the cutting force models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The adequacy of the predictive model was verified using ANOVA at 95% confidence level. This paper presents an approach to predict cutting force model in end milling of medium carbon steel using coated TiN insert under dry conditions and full immersion cutting.Keywords: Tangential Cutting Forces; RSM; coated TiN; model.DOI: 10.3329/jme.v40i2.5350Journal of Mechanical Engineering, Vol. ME 40, No. 2, December 2009 95-103

The Factors Influenced to Surface Finish in Milling for Hardened Medium Carbon Steel: JIS S50C

2011 ◽  
Vol 383-390 ◽  
pp. 7133-7137 ◽  
Author(s):  
Komson Jirapattarasilp ◽  
Sittichai Kaewkuekool ◽  
Worapong Phongphatrawut
Keyword(s):  
Surface Roughness ◽  
Carbon Steel ◽  
Surface Quality ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Medium Carbon Steel ◽  
Medium Carbon ◽  
High Cutting Speed ◽  
Full Factorial Experimental Design

The aim of this research was to study factors, which was influenced on surface roughness in vertical milling of hardened medium carbon steel. The specimen was medium carbon steel grade JIS S50C that was hardened at 56± 2 HRC. Full factorial experimental design was conducted by 3 factors and 3 levels (33 design) with 2 replications. Studied factors were consisted of cutting speed, feed rate, and air coolant pressure. The results revealed that influenced factor affected to surface roughness was cutting speed and feed rate which showed significantly different. Higher cutting speed would cause on better surface quality. On the other hand, poorer surface quality was produced by higher feed rate. However, factors interaction were found between cutting speed with air coolant pressure and feed rate with air coolant pressure that significantly influenced to surface roughness. The interaction of high cutting speed with high air coolant pressure would be better quality of surface finish and lower feed rate with high air coolant pressure would be better surface quality.

The Influence of Cutting Parameters of Turning Process on the Oil Palm Wood Using Carbide Tool

2013 ◽  
Vol 415 ◽  
pp. 666-671
Author(s):  
Surasit Rawangwong ◽  
Jaknarin Chatthong ◽  
Worapong Boonchouytan
Keyword(s):  
Surface Roughness ◽  
Oil Palm ◽  
Feed Rate ◽  
Cutting Speed ◽  
Experimental Result ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Turning Process ◽  
Main Factors

This research study aimed to investigate the effect of main factors on the surface roughness in oil palm wood turning process for manufacturing furniture parts using carbide tools. The main factors, namely, cutting speed, feed rate and depth of cut were investigated for the optimum surface roughness in furniture manufacturing process. The result of preliminary trial shown that the depth of cut had no effect on surface roughness. Moreover, the experiment was found that the factors affecting a surface roughness were cutting speed and feed rate, with having tendency for reduction of roughness value at lower feed rate and greater cutting speed, Therefore in the turning process of oil palm wood, it was possible to determine a cutting condition by means of the equation Ra = 19.8-0.00742 Cutting Speed+3.98 Feed rate, This equation can be best used with limitation of cutting speed at 122-450 m/min, feed rate at 0.1-0.5 mm/rev and depth of cut does not over 1 mm,. To confirm the experiment result, a comparison between the equation value and an actual value by estimating a prediction error value was calculate with the surface roughness and margin of error does not over 10%. The experimental result reveals the mean absolute percentage error (MAPE) of the equation of surface roughness is 3.24%, which is less than the predicted error value and it is acceptable.

Optimization of Surface Roughness in End Milling of Medium Carbon Steel under the Influence of Magnets Attached to the Machine Spindle

2012 ◽  
Vol 576 ◽  
pp. 115-118
Author(s):  
A.K.M. Nurul Amin ◽  
Syidatul Akma ◽  
Maizatul Akma ◽  
M.D. Arif
Keyword(s):  
Surface Roughness ◽  
Carbon Steel ◽  
Metal Cutting ◽  
Permanent Magnets ◽  
End Milling ◽  
Cutting Speed ◽  
Medium Carbon Steel ◽  
Depth Of Cut ◽  
Machining Accuracy ◽  
Medium Carbon

One of the major technological parameters in metal cutting is surface roughness. It is an unavoidable, and often, unwanted by-product of all machining operations, especially end milling. Surface roughness is directly related to product quality and performance, operation cost, machining accuracy, and chatter. Today’s stringent customer requirements demand machined parts with minimum (mirror finished) products. Hence, the prediction, modeling, and optimization of surface roughness is a quintessential concern in machining research and industrial endeavor. This research was undertaken in order to determine whether end milling of medium carbon steel performed using chosen ranges of cutting parameter and under magnetic field generated by permanent magnets could effectively improve surface roughness. A small central composite design with five levels and an alpha value of 1.4142, in Response Surface Methodology, was used in developing the relationship between cutting speed, feed, and depth of cut, with surface roughness. Design-Expert 6.0 software was utilized to develop the quadratic empirical mathematical model for surface roughness. The experiments were performed under two different conditions. The first condition was cutting under normal conditions, while the other one was cutting under the application of magnetic fields from two permanent magnets. Medium carbon steel was used as the work material. The resultant average surface roughness was found to be reduced by a maximum of 59% due to magnet application.

Modeling the Surface Roughness for Fine Turning of AISI304 Stainless Steel

2007 ◽  
Vol 364-366 ◽  
pp. 644-648 ◽  
Author(s):  
Wei Shin Lin
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Prediction Model ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Variable Surface ◽  
Fine Turning

High ductility, high strength, high work hardening rate and low thermal conductivity of stainless steels are the main factors that make their machinability difficult. In this study, determination of the optimum cutting condition has been aimed at when fine turning an AISI 304 austenitic stainless steel using ceramic cutting tools. The cutting speeds for the turning test were from 80 to 320 m / min, feed rates were from 0.04 to 0.10 mm / rev and the depth of cut was fixed at 0.1 mm. According to the test results, we can find that the values of surface roughness were decreased when the cutting speed was increasing, and decrease with the decrease of feed rate. But when the cutting speed was greater than 360 m / min, or the feed rate was smaller than 0.02 mm / rev，the surface roughness would be deteriorated because of the chatter phenomenon. In this paper, a polynomial network is adopted to construct a prediction model on surface roughness for fine turning of AISI304 austenitic stainless steel. The polynomial network is composed of a number of functional nodes. These functional nodes are self-organized to form an optimal network architecture by using a predicted square error (PSE) criterion. It is shown that the polynomial network can correctly correlate the input variables (cutting speed and feed rate) with the output variable (surface roughness). Based on the surface roughness prediction model constructed, the surface roughness of the workpiece can be predicted with reasonable accuracy if the turning conditions are given and it is also consistent with the experimental results very well.

Sign in / Sign up

Export Citation Format

Share Document