Study on Spindle Vibration and Surface Finish in Turning of Al 7075

2017 ◽  
Vol 261 ◽  
pp. 321-327 ◽  
Author(s):  
Abidin Şahinoğlu ◽  
Şener Karabulut ◽  
Abdulkadir Güllü
Keyword(s):  
Surface Roughness ◽  
Experimental Studies ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Dry Cutting ◽  
Al 7075 ◽  
Spindle Vibration ◽  
Coated Cemented Carbide

In this study, the relationship between the spindle vibration and surface roughness was investigated and the effect of the cutting parameters on surface roughness and spindle vibration during the machining of Aluminum alloy 7075 (Al 7075) were determined. Experimental studies have been carried out on a CNC turning machine using coated cemented carbide cutting tools under dry cutting environment. L64 full factorial design of experiments was used to investigate the optimal machining parameters for spindle vibration and surface roughness. The influences of machining parameters on vibration and surface roughness were evaluated by using analysis of variance (ANOVA) and main effect plots. The results revealed that the feed rate was the most effective cutting parameters on spindle vibration and surface roughness. The machine tool vibration amplitude and surface roughness values were significantly increased with increasing cutting feed. The depth of cut and cutting speed have the least effect on the spindle vibration and indicated an insignificant effect on surface roughness. Mathematical equations were developed to predict the vibration and surface roughness values using the regression analysis.

Effect of machining parameters on the surface finish of a metal matrix composite under dry cutting conditions

2015 ◽  
Vol 231 (6) ◽  
pp. 913-923 ◽  
Author(s):  
Brian Boswell ◽  
Mohammad Nazrul Islam ◽  
Ian J Davies ◽  
Alokesh Pramanik
Keyword(s):  
Surface Roughness ◽  
Metal Matrix Composite ◽  
Tool Life ◽  
Surface Finish ◽  
Feed Rate ◽  
Metal Matrix ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Dry Cutting

The machining of aerospace materials, such as metal matrix composites, introduces an additional challenge compared with traditional machining operations because of the presence of a reinforcement phase (e.g. ceramic particles or whiskers). This reinforcement phase decreases the thermal conductivity of the workpiece, thus, increasing the tool interface temperature and, consequently, reducing the tool life. Determining the optimum machining parameters is vital to maximising tool life and producing parts with the desired quality. By measuring the surface finish, the authors investigated the influence that the three major cutting parameters (cutting speed (50–150 m/min), feed rate (0.10–0.30 mm/rev) and depth of cut (1.0–2.0 mm)) have on tool life. End milling of a boron carbide particle-reinforced aluminium alloy was conducted under dry cutting conditions. The main result showed that contrary to the expectations for traditional machined alloys, the surface finish of the metal matrix composite examined in this work generally improved with increasing feed rate. The resulting surface roughness (arithmetic average) varied between 1.15 and 5.64 μm, with the minimum surface roughness achieved with the machining conditions of a cutting speed of 100 m/min, feed rate of 0.30 mm/rev and depth of cut of 1.0 mm. Another important result was the presence of surface microcracks in all specimens examined by electron microscopy irrespective of the machining condition or surface roughness.

scholarly journals Surface Quality Assessment after Milling AZ91D Magnesium Alloy Using PCD Tool

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 617 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Jarosław Korpysa
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
High Speed ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Roughness Parameters ◽  
Operational Parameters

Surface roughness is among the key indicators describing the quality of machined surfaces. Although it is an aggregate of several factors, the condition of the surface is largely determined by the type of tool and the operational parameters of machining. This study sought to examine the effect that particular machining parameters have on the quality of the surface. The investigated operation was the high-speed dry milling of a magnesium alloy with a polycrystalline diamond (PCD) cutting tool dedicated for light metal applications. Magnesium alloys have low density, and thus are commonly used in the aerospace or automotive industries. The state of the Mg surfaces was assessed using the 2D surface roughness parameters, measured on the lateral and the end face of the specimens, and the end-face 3D area roughness parameters. The description of the surfaces was complemented with the surface topography maps and the Abbott–Firestone curves of the specimens. Most 2D roughness parameters were to a limited extent affected by the changes in the cutting speed and the axial depth of cut, therefore, the results from the measurements were subjected to statistical analysis. From the data comparison, it emerged that PCD-tipped tools are resilient to changes in the cutting parameters and produce a high-quality surface finish.

Investigations on the Effect of CNC Dry Hard Turning Process Parameters on Surface Integrity: A Multi-performance Characteristics Optimization

2014 ◽  
Vol 14 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Suha K. Shihab ◽  
Zahid A. Khan ◽  
Aas Mohammad ◽  
Arshad Noor Siddiquee
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
Feed Rate ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Micro Hardness ◽  
Dry Cutting

AbstractThe cutting parameters such as the cutting speed, the feed rate, the depth of cut, etc. are expected to affect the two constituents of surface integrity (SI), i.e., surface roughness and micro-hardness. An attempt has been made in this paper to investigate the effect of the CNC hard turning parameters on the surface roughness average (Ra) and the micro-hardness (μh) of AISI 52100 hard steel under dry cutting conditions. Nine experimental runs based on an orthogonal array of the Taguchi method were performed and grey relational analysis method was subsequently applied to determine an optimal cutting parameter setting. The feed rate was found to be the most influential factor for both the Ra and the μh. Further, the results of the analysis of variance (ANOVA) revealed that the cutting speed is the most significant controlled factor for affecting the SI in the turning operation according to the weighted sum grade of the surface roughness average and micro-hardness.

scholarly journals Influence of Cutting Parameters on Surface Roughness for Wet and Dry Turning Process

2011 ◽  
Vol 471-472 ◽  
pp. 233-238 ◽  
Author(s):  
Muhammad Yusuf ◽  
Khairol Anuar ◽  
Napsiah Binti Ismail ◽  
Shamsuddin Sulaiman
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Fractional Factorial ◽  
Cutting Process ◽  
Depth Of Cut ◽  
Turning Process ◽  
Dry Cutting ◽  
Dry Turning ◽  
Coated Carbide

This paper presents a study of the quality of a surface roughness model for mild steel with coated carbide cutting tool on turning process. The experiments were carried out under wet and dry cutting conditions. The model is developed based on cutting speed, feed and depth of cut as the parameters of cutting process. This research applies the fractional factorial design of experiment approach to studied the influence of cutting parameters on surface roughness. The measured results were collected and analyzed using commercial software package called Minitab. Analysis of variances is used to examine the influence of turning factors and factor interactions on surface roughness. The result indicated that, there are inherent differences in surface roughness between wet and dry cutting process with the same parameters process model. Analysis of variance was found that feed parameter is the most significant cutting parameter, which influences the surface roughness. The most significant interactions were found between cutting speed and feed parameters for dry turning process. Therefore is a significant effect of using combination of the fluid for cooling the cutting operation.

Research on the Surface Roughness of Dry Cutting Different Levels of Austempering Ductile Iron (ADI)

2011 ◽  
Vol 464 ◽  
pp. 496-500
Author(s):  
Xiao Hong Xue ◽  
Xu Hong Guo ◽  
Ting Ting Chen ◽  
Dong Dong Wan ◽  
Qiao Wang
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Cubic Boron Nitride ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Isothermal Quenching ◽  
Dry Cutting ◽  
Quenching Temperature

Three cutting tools of different materials (ceramics CC6050, cubic boron nitride CB7025, carbide GC2025) are used for dry turning of 9 groups of ADI which heat-treated under different quenching time and quenching temperature. The surface roughness of ADI workpieces were tested after the finish turning at changed cutting parameters, and the influencing factors of surface quality were analysed. Results showed that the surface roughness values of all 9 groups of ADI workpieces obtained by CC6050 were the lowest and the surface quality was better at lower depth of cut ap and feed rate f with higher cutting speed vc . Meanwhile, the surface roughness was influenced by the isothermal quenching parameters of ADI workpieces significantly.

scholarly journals Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Aykut Eser ◽  
Elmas Aşkar Ayyıldız ◽  
Mustafa Ayyıldız ◽  
Fuat Kara
Keyword(s):  
Surface Roughness ◽  
Conjugate Gradient ◽  
Prediction Models ◽  
Experimental Studies ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Ann Model ◽  
Aa6061 Alloy

This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

scholarly journals Optimization of Cutting Parameters on Turning Process Based on Surface Roughness Using Response Surface Methodology

2011 ◽  
Vol 117-119 ◽  
pp. 1561-1565
Author(s):  
Muhammad Yusuf ◽  
Mohd Khairol Anuar Ariffin ◽  
N. Ismail ◽  
S. Sulaiman
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Process ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Optimization Of Cutting Parameters

This paper describes effect of cutting parameters on surface roughness for turning of aluminium alloy 7050 using carbide cutting tool with dry cutting condition. The model is developed based on cutting speed, feed rate and depth of cut as the parameters of cutting process. The selection of cutting process was based on the design of experiments Response Surface Methodology (RSM). The objective of this research is finding the optimum cutting parameters based on surface roughness. The relation between cutting parameters and surface roughness were discussed.

Optimization of Cutting Parameters for Turning Operation of Titanium Alloy Ti-6Al-4V Material Workpiece Using the Taguchi Method

2013 ◽  
Vol 685 ◽  
pp. 57-62
Author(s):  
Seyyed Pedram Shahebrahimi ◽  
Abdolrahman Dadvand
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Taguchi Method ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Material Tool

One of the most important issues in turning operations is to choose suitable parameters in order to achieve a desired surface finish. The surface finish in machining operation depends on many parameters such as workpiece material, tool material, tool coating, machining parameters, etc. The purpose of this research is to focus on the analysis of optimum cutting parameters to get the lowest surface roughness in turning Titanium alloy Ti-6Al-4V with the insert with the standard code DNMG 110404 under dry cutting condition, by the Taguchi method. The turning parameters are evaluated as cutting speed of 14, 20 and 28 m/min, feed rate of 0.12, 0.14 and 0.16 mm/rev, depth of cut of 0.3, 0.6 and 1 mm, each at three levels. The Experiment was designed using the Taguchi method and 9 experiments were conducted by this process. The results are analyzed using analysis of variance method (ANOVA). The results of analysis show that the depth of cut has a significant role to play in producing lower surface roughness that is about 63.33% followed by feed rate about 30.25%, and cutting speed has less contribution on the surface roughness. Also it was realized that with the use of the confirmation test, the surface roughness improved by 227% from its initial state.

Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm

2015 ◽  
Vol 815 ◽  
pp. 268-272 ◽  
Author(s):  
Nur Farahlina Johari ◽  
Azlan Mohd Zain ◽  
Noorfa Haszlinna Mustaffa ◽  
Amirmudin Udin
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Firefly Algorithm ◽  
Volume Fraction ◽  
Optimization Problems ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Time

Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Surface Roughness Optimization of some Machining Parameters in Turning Operations Using Taguchi Method

2009 ◽  
Vol 62-64 ◽  
pp. 613-620 ◽  
Author(s):  
Ishaya Musa Dagwa
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Signal To Noise Ratio ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Confirmatory Test ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Operations ◽  
Factors Affecting

In this study, an attempt has been made to optimize cutting parameters (cutting speed, depth of cut, and feed rate) in conventional turning operations. A Taguchi orthogonal array (L933) was used in surface roughness optimization of a solid round bar of mild steel material. The experimental runs were randomized; two skilled machinists were involved in the turning operation using the same machining parameters. ANOVA analysis was performed to identify the percentage contribution of the factors affecting surface roughness during machining. The optimal cutting combination was determined by using the signal-to-noise ratio and the following results were obtained; speed (level 2) = 55.m/min, depth of cut (level 3) = 0.08mm, and feed rate (levels 3) = at 0.08mm/rev. A prediction of surface roughness was carried out using the optimal setting followed by a confirmatory test on the lathe. The result shows that the confirmatory runs compared favourably (96.44%) with the predicted surface roughness.

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