scholarly journals Pengaruh Parameter Proses Milling pada Austempered Ductile Iron (ADI) Terhadap Kekasaran Permukaan Benda Kerja dan Chip Thickness Ratio

2021 ◽  
Vol 16 (2) ◽  
pp. 200
Author(s):  
Rusnaldy Rusnaldy ◽  
Yusuf Umardani ◽  
Diva Tsamara Putra ◽  
Jovian Bernard
Keyword(s):  
Surface Roughness ◽  
Ductile Iron ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Weight Ratio ◽  
Salt Bath ◽  
Thickness Ratio ◽  
Austempered Ductile Iron ◽  
Depth Of Cut ◽  
Dynamic Wear

<p><em>Austempered ductile iron (ADI) is a difficult material for machining, </em><em>even though ADI is believed to have several advantages such as strength, ductility, high toughness, fatigue resistance, good dynamic wear resistance, has a good strength-to-weight ratio, easy to manufacture  and easy to cast that causes it to be widely used in various applications.  </em><em>This study investigates the effect of milling parameters on surface rougness and chip thickness ratio on milling of ADI. To produce ADI, ductile irons  were first austenitized in furnace at 900<sup>o</sup>C for 1 hour and then they were quenched in salt bath at 375<sup>o</sup>C for 1 hour. The work material was machined with uncoated carbide tool. The tool was 20 mm in diameter. The cutting experiments were carried out in the dry mode. The feed was varied from 0.05 to 0.1 mm/tooth for cutting speed ranging from 15 m/min to 25 mm/min and depth of cut ranging from 0.1 mm to 0.3 mm. The surface roughness was measured using the Mitutoyo SJ-201, surface roughness machine. The chip thickness was measured using software Image J from the photograph produced by digital microscope endoscope. The results show that connected and loose chips were produced. Long and continuous chips were not found in this study. The effects of cutting speeds, feeds and depth of cut on surface roughness and chip thickness ratio  are reported in this paper</em><em></em></p>

The Effects of Cutting Speed and Depth of Cut on Machinability Characteristics of Austempered Ductile Iron

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Ahmet Akdemir ◽  
Şakir Yazman ◽  
Hacı Saglam ◽  
Mesut Uyaner
Keyword(s):  
Surface Roughness ◽  
Ductile Iron ◽  
Flank Wear ◽  
Tangential Force ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Salt Bath ◽  
Austempered Ductile Iron ◽  
Dominant Factor ◽  
Depth Of Cut

Ductile iron can acquire enhanced thermal and mechanical properties from austempering heat treatment. The present study aims to identify the function of different cutting parameters affecting machinability and to quantify its effects. Turning was performed to test machinability according to the ISO3685-1993 (E) standard. After austenitizing at 900 °C for 90 min, austempered ductile iron (ADI) specimens were quenched in a salt bath at 380 °C for 90 min. The cutting force signals along three directions were measured in real time, whereas flank wear and surface roughness were measured offline. For the cutting parameters, the cutting speed and depth of cut were varied, but the feed rate was kept constant. In the flank wear tests, machining length was corresponded to tool life. In addition, in order to find out the effect of cutting parameters on surface roughness (Ra), tangential force (Ft), and flank wear (VB) during turning, response surface methodology (RSM) was utilized by using experimental data. The effect of the depth of cut on the surface roughness was negligible but considerable in the cutting forces. The increased cutting speed produced a positive effect on surface roughness. It is found that the cutting speed was the dominant factor on the surface roughness, tangential force, and flank wear.

Evaluation of the Performance of CBN Tools When Turning Austempered Ductile Iron Material

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
K. Aslantas ◽  
İ. Ucun ◽  
K. Gök
Keyword(s):  
Surface Roughness ◽  
Ductile Iron ◽  
Cubic Boron Nitride ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Salt Bath ◽  
Austempered Ductile Iron ◽  
Depth Of Cut ◽  
Tool Performance ◽  
Austempering Temperature

The study deals with the machinability properties of austempered ductile iron using cubic boron nitride cutting tools. To emphasize the role of the austempering process, ductile iron specimens were first austenitized in salt bath at 900°C for 60min, after which they were quenched in a salt bath at 250°C and 325°C for 60min. Machining tests were carried out at various cutting speeds under the constant depth of cut and the feed rate. Tool performance was evaluated based on the workpiece surface roughness and flank wear. The influence of the austempering temperature and cutting speed on the chip form was also studied. The results point out that the lower austempering temperature results in the increase in the cutting forces, while better surface roughness is attained.

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.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness 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 developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

scholarly journals OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
Muhammad Yanis ◽  
Amrifan Saladin Mohruni ◽  
Safian Sharif ◽  
Irsyadi Yani
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Ann Model ◽  
Green Machining ◽  
Radial Depth ◽  
Thin Walled

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. 

The Effect of Build Up Edge Formation on the Machining Characteristics in Austempered Ferritic Ductile Iron

2013 ◽  
Author(s):  
Şakir Yazman ◽  
Ahmet Akdemir ◽  
Mesut Uyaner ◽  
Barış Bakırcıoğlu
Keyword(s):  
Surface Roughness ◽  
Ductile Iron ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Chip Morphology ◽  
Salt Bath ◽  
Chip Formation Mechanism ◽  
Machining Properties ◽  
Machining Characteristics ◽  
Cutting Speeds

In this study, chip formation mechanism during the machining of austempered ferritic DI and the effect of the emerging chip morphology on such machining properties as surface roughness and cutting forces has been scrutinized. After austenitizing at 900 °C for 90 min, DI specimens were austempered in a salt bath at 380 °C for 90 min. Chip roots were produced by using a quick stop device during the machining of austempered specimens in different cutting speeds. The metallographies of these specimens were performed and chip morphologies were examined. The fact that the cutting speed increased led to a decrease in built-up edge formation. Depending on this fact, it was detected that the change in built-up edge thickness substantially affected the surface roughness and cutting forces. It was also detected that during the machining, with the effect of cutting forces and stress, spheroidal graphites were broken off in the chip and lost their sphericity and so that the chip became fragile and unstable and grafites here displayed a lubricant feature.

scholarly journals Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6

2018 ◽  
Vol 14 (1) ◽  
pp. 67-76
Author(s):  
Mohanned Mohammed H. AL-Khafaji
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Thickness Ratio ◽  
Depth Of Cut ◽  
Feed Force ◽  
Machining Force ◽  
Force Components

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).

scholarly journals Optimization of process parameters on commercial mild steel using taguchi technique

2017 ◽  
Vol 7 (1.1) ◽  
pp. 138 ◽  
Author(s):  
V. Jaiganesh ◽  
B. Yokesh Kumar ◽  
P. Sevvel ◽  
A.J. Balaji
Keyword(s):  
Surface Roughness ◽  
Mild Steel ◽  
Metal Cutting ◽  
Metal Removal ◽  
Removal Rate ◽  
Chip Thickness ◽  
Critical Factor ◽  
Thickness Ratio ◽  
Depth Of Cut ◽  
Versus Input

In the present scenario of bulk manufacturing where Metal Removal Rate (MRR), Chip Thickness Ratio (CTR) and Surface Roughness (SR) is of significant importance in manufacturing the component using CNC (computer numerical controlled) machines. Nine experiments were conducted based on orthogonal array. General linear model has been generated for all the three output parameters such as (MRR, Chip Thickness Ratio surface roughness) versus input parameters (speed, time, depth of cut). The statistical method called the analysis of variance (ANOVA) is applied to find the critical factor. The Main effects of S/N ratio values are found and plotted in the form of graph. The optimized value is found for speed, time, and depth of cut by using “MINITAB” software. By using this optimized value the efficient metal cutting can be done in commercial mild steel.

Sign in / Sign up

Export Citation Format

Share Document