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.

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.

The Study of Proper Conditions in Face Milling Palmyra Palm Wood and Coconut Wood Using Design of Experiment

2012 ◽  
Vol 488-489 ◽  
pp. 847-855
Author(s):  
S. Rawangwong ◽  
J. Chatthong ◽  
J. Rodjananugoon ◽  
W. Boonchouytan ◽  
R. Burapa
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Face Milling ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Absolute Percentage Error ◽  
Margin Of Error ◽  
Main Factors

The purpose of this research was to investigate the effects of main factors on the surface roughness in face milling process palmyra palm wood and coconut wood by computer numerical controlled milling machine and using shell end mill cutting tools 6 edges. The main factors including speed, feed rate, depth of cut and angle of cut were investigated for the optimum surface roughness. The result of preliminary trial showed that the depth of cut and the angle of the cut had no effect on surface roughness. It was found from the experiment that the factors affecting surface roughness were feed and speed, with tendency for reduction of roughness value at a lower feed rate and greater cutting speed. Therefore, in the facing process for palmyra palm wood it was possible to determine a face milling condition by means of the equation Ra = 0.954 + 20.4 Feed + 0.00126 Speed. This equation was employed at a limited speed of 800-1200 rpm, and the feed rate of 0.03-0.05 mm/tooth. The result from the experiment of the mean absolute percentage error of the equation of surface roughness is 6.10% which is less than the margin of error, and is acceptable. For coconut wood it was found from the experiment that the factor affecting surface roughness was feed rate and cutting speed, with tendency for reduction of roughness value at lower feed rate and greater cutting speed. Therefore, in the face milling coconut wood it was possible determine a facing condition by means of the equation Ra = 4.72 - 0.000864 Speed + 0.00443 Feed. Leading this equation goes to use is in limitation cutting speed 1000-2000 rpm at feed rate 100-300 mm/min. The result from the experiment of mean absolute percentage error of the equation of surface roughness is 4.64% which is less than the margin of error, and is acceptable. As a result, the selection of optimal machining parameters can be greatly benefited to the Coconut wood furniture manufacturing industry in terms of productivity improvement.

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.

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.

scholarly journals Surface Quality and Chip Formation in Turning of LM6 Aluminium Alloy and Particulate Reinforced Metal Matrix Composite

2013 ◽  
Vol 773-774 ◽  
pp. 894-901
Author(s):  
Muhammad Yusuf ◽  
M.K.A. Ariffin ◽  
N. Ismail ◽  
S. Sulaiman
Keyword(s):  
Surface Roughness ◽  
Aluminium Alloy ◽  
Surface Quality ◽  
Chip Formation ◽  
Feed Rate ◽  
Metal Matrix ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Cutting Condition

Majority of the components of aerospace and automotive vehicles need different machining operations, mainly for the assembly requirements. The components have to present both high dimensional precision and surface quality. This present work is concerned with the effect of cutting parameters (cutting speed, feed rate and depth of cut) on the surface roughness and the chip formation in turning process. The machining results are compared with LM6 aluminium alloy and TiC reinforced metal matrix composite under the same cutting conditions and tool geometry. The cutting condition models designed based on the Design of Experiments Response Surface Methodology. The objective of this research is to obtaining the optimum cutting parameters to get a better surface quality and also the chip formation and furthermore does not hazardous to the worker and the machined products quality. Results shows that Surface roughness values of LM6-TiC composite are higher as compared LM6 alloy at similar cutting condition. With increasing in cutting speed improves the surface quality. The surface quality increases with decrease of the feed rate and the depth of cut. There are difference chip forms for LM6 aluminium alloy and Al-TiC composite for a similar of cutting condition. Generally, chip formations of both materials are acceptable and favourable for the worker as well as the products and the tools.

An Investigation of Optimum Cutting Conditions in Face Milling Semi-Solid Cast 6061 Aluminum Alloy Using Carbide Tool

2013 ◽  
Vol 747 ◽  
pp. 777-780 ◽  
Author(s):  
S. Rawangwong ◽  
Worapong Boonchouytan ◽  
J. Chatthong ◽  
R. Burapa
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Face Milling ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Milling Machine ◽  
Optimum Cutting ◽  
The Mean ◽  
Main Factors ◽  
Semi Solid

The purpose of this research was to investigate the effect of the main factors of the surface roughness in semi-solid 6061 aluminum face milling. This study was conducted by using computer numerical controlled milling machine. The controlled factors were the speed, the feed rate and the depth of cut which the depth of cut was not over 1 mm. For this experiment, we used factorial designs and the result showed that the factors effected of surface roughness was the feed rate and the speed while the depth of cut did not effect with the surface roughness. Furthermore, the surface roughness was likely to reduce when the speed was 4,200 rpm and the feed rates was 1,300 mm/min. The result of the research led to the linear equation measurement value which was Ra = 0.186 - 0.000034 Speed + 0.000047 Feed rate. The equation formula should be used with the speed in the range of 3,200-4,200 rpm, feed rate in the range of 1,300-1,800 mm/min and the depth of cut not over 1 mm. The equation was used to confirm the research results, it was found that the mean absolute percentage error of the surface roughness obtained from the predictive comparing to the value of the experiment was 4.12%, which was less than the specified error and it was acceptable.

Investigating and Modeling the Machinability of Al 6061 Using Response Surface Methodology

2015 ◽  
Vol 761 ◽  
pp. 267-272
Author(s):  
Basim A. Khidhir ◽  
Ayad F. Shahab ◽  
Sadiq E. Abdullah ◽  
Barzan A. Saeed
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Effect Of Temperature ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process

Decreasing the effect of temperature, surface roughness and vibration amplitude during turning process will improve machinability. Mathematical model has been developed to predict responses of the surface roughness, temperature and vibration in relation to machining parameters such as the cutting speed, feed rate, and depth of cut. The Box-Behnken First order and second-order response surface methodology was employed to create a mathematical model, and the adequacy of the model was verified using analysis of variance. The experiments were conducted on aluminium 6061 by cemented carbide. The direct and interaction effect of the machining parameters with responses were analyzed. It was found that the feed rate, cutting speed, and depth of cut played a major role on the responses, such as the surface roughness and temperature when machining mild steel AISI 1018. This analysis helped to select the process parameters to improve machinability, which reduces cost and time of the turning process.

Effect of Tool Edge Geometry on Cutting Force and Surface Roughness when Hard Turning Tool Steel

2012 ◽  
Vol 505 ◽  
pp. 15-19 ◽  
Author(s):  
Mohd Yusof Noordin ◽  
Ali Davoudinejad ◽  
Mohd Rosmaini Shaari
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Tangential Force ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Workpiece Material ◽  
Edge Preparation

High usage of hardened steel in the automotive, gear, bearing, tool and die making industries, makes it a highly suitable material for industrial production and research. This study was undertaken to investigate the performance of coated ceramic insert with different edge preparations in terms of cutting force and surface roughness. Plain turning experiments were carried out under dry cutting condition at two different cutting speeds and feed rates with a constant depth of cut. The workpiece material is ASSAB DF-3 hardened steel with a 55 ±1 HRC hardness. Results showed that insert edge preparation had a direct influence on the radial and feed forces but not on the tangential force. The use of T-land edge preparation results in the lowest radial and feed forces. In terms of surface finish, the use of honed with finishing wiper insert results in obtaining the lowest surface roughness values. Feed rate had a significant effect on surface roughness whereby by increasing feed rate, the surface roughness value also increased, whereas the effect of cutting speed was found to be insignificant. Increasing cutting speed resulted in lower feed and tangential forces however by increasing feed rate all cutting forces increased.

scholarly journals Effect of Cutting Parameters on Machinability of Hardened AISI 52100 Bearing Steel

2020 ◽  
Vol 3 (1) ◽  
pp. 474-481
Author(s):  
Mohammad Rafighi
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Bearing Steel ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Manufacturing Cost ◽  
Machining Parameters ◽  
Aisi 52100

In this study, the effects of machining parameters, namely feed rate, cutting speed and depth of cut on the surface roughness was investigated experimentally in hard turning of AISI 52100 bearing steel using cubic boron nitride insert under dry cutting condition. Taguchi method was utilized to reduce the number of trials to 9 for saving the time and manufacturing cost. The significant factor on the surface roughness was determined using analysis of variance. Furthermore, the optimum cutting parameter for reducing the surface roughness was obtained. The findings showed that the feed rate has the highest impact on surface roughness among any other cutting parameters, with 88.32% contribution.

The Optimization in Machining AISI 1030 Using Taguchi Method for Dry and Flood Cutting Condition

2013 ◽  
Vol 315 ◽  
pp. 841-845
Author(s):  
Noor Hakim Rafai ◽  
Mohd Hilmi Othman ◽  
Sulaiman Hasan ◽  
Tharmaah Rao A/L Sinnasalam
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Feed Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Quality Characteristic ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Cnc Milling ◽  
Dry Cutting

This research is an approach to investigate the effect of cutting condition on surface roughness in dry and flood cutting of AISI 1030. The objectives of this project are to compare the plastic injection mould quality between dry and flood cutting condition, as well as to determine the best cutting condition. The parameters used were depth of cut (0.25mm, 0.5mm, and 1.0mm), feed rate (50mm/rev, 100mm/rev and 150mm/rev) and cutting speed (700m/min, 1400m/min and 2100m/min). Surface roughness value was used to determine to quality characteristic of the machined mould. The experiments were done using Mazak CNC milling machine and the material selected was AISI 1030, which is a medium tensile and low hardenability carbon steel. Twenty-seven runs were done in both dry and flood cutting, adapting Taguchi Method - Orthogonal Array. After each machining, the surface roughness was measured using Mitutoyo Surface Roughness Tester. The data obtained was then analyzed through Signal to Noise Ratio calculation. This analysis produced the best combination of parameters which gives the lowest surface roughness. The best combinations for dry cutting are 2100m/min for cutting speed, 50mm/rev for feed rate and 0.25mmfor depth of cut. As for flood cutting, the best combinations are 2100m/min for cutting speed, 50mm/rev for feed rate and 1.0mm for depth of cut. The surface roughness obtained using this parameter in dry cutting is 0.27Ám and 0.40Ám in flood cutting. From the comparison, it is proved that dry cutting produced lower surface roughness compared to flood cutting.

Sign in / Sign up

Export Citation Format

Share Document