Effect of Silicon Content of Aluminum Alloy 6351 in Turning Process

2012 ◽  
Author(s):  
Daniel Fernandes da Cunha ◽  
Marcio Bacci da Silva
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Cutting Force ◽  
Feed Rate ◽  
Silicon Content ◽  
The Other ◽  
Depth Of Cut ◽  
Edge Region ◽  
Turning Process ◽  
Free Region

The machinability of three commercial samples of the 6351 aluminum alloy with different silicon content was investigated in this work. Several parameters were used to evaluate the machinability in turning process, including the quality of the machined surface and cutting force. A design of experiments with three levels was used focusing on low values of feed rate (0.10, 0.15 and 0.2 mm/rev). The other parameters involved were: depth of cut (1.0, 1.5 and 2.0 mm), the silicon content (1.1, 1.2 and 1.3%) and two sets of cutting speed, one in the build up edge region (80, 100 and 120 m/min) and the other in a built up edge free region (200, 600 and 1000 m/min). The surface roughness parameter evaluated was Rq. A second design of experiment with three levels using higher values of feed rate (0.2, 0.35 and 0.5 mm/rev) and depth of cut of 2.0 mm was used to evaluate the influence of the silicon content in the cutting force. The effect of cutting fluid (dry machining, minimum quantity of fluid and over head cooling) was also analyzed. The results show that the silicon content has influence on the surface roughness. The statistical model in the build up edge region explains 79.95% of the total variation of roughness and 99% for cutting forces, for the other region this value is 81.99% for surface roughness and 98.96% for cutting force. The diameter of the workpiece has an influence on the results because the variation of hardness.

Experimental Optimization of Process Parameters for Turning TC11 Titanium Alloy Using Taguchi Methodology Design

2016 ◽  
Vol 693 ◽  
pp. 1009-1014 ◽  
Author(s):  
Su Lin Chen ◽  
Bin Shen ◽  
Fang Hong Sun
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Process Parameters ◽  
Feed Rate ◽  
Cutting Temperature ◽  
Cvd Diamond ◽  
Depth Of Cut ◽  
Taguchi Methodology ◽  
Turning Process

This paper presents a study of the influence of cutting conditions (cutting velocity, feed, cutting depth and lubrication) on turning TC11 (Ti-6.5Al-3.5Mo-1.5Zr-0.3Si) titanium alloy. Taguchi methodology design was adopt for carrying out experiments. Turning process parameters such as cutting speed, feed rate and depth of cut were varied to study their effect on process responses such as cutting force (Ft), surface roughness (Ra) and temperature on cutting zones (T). Minimum quantity lubrication (MQL) technology was adopt to increase the lubricating and cooling effect. Meanwhile, CVD diamond coating was deposited on the cemented carbide insert to reduce its friction with workpiece and increase its wear resistance. From the analysis of orthogonal tests, depth of cut contributes the most for the main cutting force and cutting temperature, while feed rate had the most significant effect on surface roughness on the workpiece. MQL can reduce the cutting temperature at the cutting zones, especially for the uncoated cutting inserts whose temperature decreases by an average of 60~80°C. The cutting force, surface roughness and cutting temperature of CVD diamond coated inserts were all higher than those of uncoated tools, especially with MQL lubrication. Considering the cutting efficiency and cost, the optimal parameters in the turning process of TC11 for minimizing the cutting force, surface roughness and cutting temperature are obtained as Vc=115m/min, f=0.08mm, ap=0.5mm under MQL lubricating with uncoated cemented carbide as the cutting tool.

Nature instigated Grey wolf algorithm for parametric optimization during machining (Milling) of polymer nanocomposites

2021 ◽  
pp. 089270572199320
Author(s):  
Prakhar Kumar Kharwar ◽  
Rajesh Kumar Verma
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Polymer Nanocomposites ◽  
Feed Rate ◽  
Fitness Function ◽  
Depth Of Cut ◽  
Multiwall Carbon Nanotube ◽  
Grey Wolf ◽  
Milling Characteristics ◽  
The Impact

The new era of engineering society focuses on the utilization of the potential advantage of carbon nanomaterials. The machinability facets of nanocarbon materials are passing through an initial stage. This article emphasizes the machinability evaluation and optimization of Milling performances, namely Surface roughness (Ra), Cutting force (Fc), and Material removal rate (MRR) using a recently developed Grey wolf optimization algorithm (GWOA). The Taguchi theory-based L27 orthogonal array (OA) was employed for the Machining (Milling) of polymer nanocomposites reinforced by Multiwall carbon nanotube (MWCNT). The second-order polynomial equation was intended for the analysis of the model. These mathematical models were used as a fitness function in the GWOA to predict machining performances. The ANOVA outcomes efficiently explore the impact of machine parameters on Milling characteristics. The optimal combination for lower surface roughness value is 1.5 MWCNT wt.%, 1500 rpm of spindle speed, 50 mm/min of feed rate, and 3 mm depth of cut. For lower cutting force, 1.0 wt.%, 1500 rpm, 90 mm/min feed rate and 1 mm depth of cut and the maximize MRR was acquired at 0.5 wt.%, 500 rpm, 150 mm/min feed rate and 3 mm depth of cut. The deviation of the predicted value from the experimental value of Ra, Fc, and MRR are found as 2.5, 6.5 and 5.9%, respectively. The convergence plot of all Milling characteristics suggests the application potential of the GWO algorithm for quality improvement in a manufacturing environment.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

2020 ◽  
Vol 36 ◽  
pp. 28-46
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Salah Eddine Mechraoui ◽  
Mohamed Athmane Yallese ◽  
Mustapha Temmar
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 316L ◽  
Cutting Power ◽  
Dry Turning ◽  
Optimization Of Cutting Parameters

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

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.

Optimization of Surface Roughness by Using Different Cutting Tools for Aluminum Alloy 6063

2020 ◽  
Vol 17 (2) ◽  
pp. 961-966
Author(s):  
Allina Abdullah ◽  
Afiqah Azman ◽  
B. M. Khirulrizwan
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Optimum Condition ◽  
Feed Rate ◽  
Optimum Parameter ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Numerical Control ◽  
Depth Of Cut

This research outlines an experimental study to determine the optimum parameter of cutting tool for the best surface roughness (Ra) of Aluminum Alloy (AA) 6063. For the experiment in this research, cutting parameters such as cutting speed, depth of cut and feed rate are used to identify the effect of both cutting tools which are tungsten carbide and cermet towards the surface roughness (Ra) of material AA6063. The machining operation involved to cut the material is turning process by using Computer Numerical Control (CNC) Lathe machine. The experimental design was designed by Full Factorial. The experiment that had been conducted by the researcher is 33 with 2 replications. The total number of the experiments that had been run is 54 runs for each cutting tool. Thus, the total number of experiments for both cutting tools is 108 runs. ANOVA analysis had been analyzed to identify the significant factor that affect the Ra result. The significant factors that affect the Ra result of AA6063 are feed rate and cutting speed. The researcher used main effect plot to determine the factor that most influenced the surface roughness of AA6063, the optimum condition of surface roughness and the optimum parameter of cutting tool. The factor that most influenced the surface roughness of AA6063 is feed rate. The optimum condition of surface roughness is at the feed rate of 0.05 mm/rev, cutting speed of 600 rpm and depth of cut of 0.10 mm. While the optimum parameter of cutting tool is cermet insert with the lowest value of surface roughness (Ra) result which is 0.650 μm.

Advanced Prediction of Surface Roughness by Monitoring of Dynamic Cutting Forces in CNC Turning Process

2012 ◽  
Vol 239-240 ◽  
pp. 661-669 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Depth Of Cut ◽  
Turning Process ◽  
Cnc Turning ◽  
Roughness Model ◽  
Developed Surface ◽  
Dynamic Cutting Force ◽  
Force Ratio ◽  
Cnc Turning Process

The aim of this research is to investigate the relation between the surface roughness and the dynamic cutting force ratio during the in-process cutting in CNC turning process. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, and the dynamic cutting force ratio. The dynamic cutting force ratio is proposed to predict the surface roughness during the cutting, which can be calculated and obtained by taking the ratio of the corresponding time records of the area of thedynamic feed force to that of the dynamic main force. The in-process relation between dynamic cutting force ratio and surface roughness can be proved by the frequency of the dynamic cutting force which corresponds to the surface roughnessfrequency. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method at 95% confident level. The proposed model has been verified by the new cutting tests. It is understood that the developed surface roughness model can be used to predict the in-process surface roughness with the high accuracy of 90.3% by utilizing the dynamic cutting force ratio.

Taguchi Method Based Experiments of Titanium TC11 Flank Milling Parameters

2009 ◽  
Vol 407-408 ◽  
pp. 608-611 ◽  
Author(s):  
Chang Yi Liu ◽  
Cheng Long Chu ◽  
Wen Hui Zhou ◽  
Jun Jie Yi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Flank Milling ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Mill Machining

Taguchi design methodology is applied to experiments of flank mill machining parameters of titanium alloy TC11 (Ti6.5A13.5Mo2Zr0.35Si) in conventional and high speed regimes. This study includes three factors, cutting speed, feed rate and depth of cut, about two types of tools. Experimental runs are conducted using an orthogonal array of L9(33), with measurement of cutting force, cutting temperature and surface roughness. The analysis of result shows that the factors combination for good surface roughness, low cutting temperature and low resultant cutting force are high cutting speed, low feed rate and low depth of cut.

In-Process Monitoring and Prediction of Surface Roughness in CNC Turning Process

2011 ◽  
Vol 199-200 ◽  
pp. 1958-1966 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Turning Process ◽  
Roughness Model ◽  
Force Ratio ◽  
Cnc Turning Process

The objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been verified to monitor the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It has been proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

scholarly journals Effect of Cutting Depth and Feed Speed to Surface Roughness in Lathe Process of Screw Conveyor Shaft (Case Study: PT. RAPP)

2020 ◽  
Vol 64 (2) ◽  
pp. 59-62
Author(s):  
Junaidi Abdul Khair ◽  
◽  
Deni Pranata ◽  
Ujang Nurhadek ◽  
◽  
...  
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Rotational Speed ◽  
High Speed ◽  
Feeding Rate ◽  
Production Quality ◽  
Screw Conveyor ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Turning Process

The metalworking process is one of the most important things in manufacturing of machine components, such as lathe process. Therefore, it is required continuously innovation to improve production quality. There are several ways to do this, for example by choosing the right type of tool, depth of cut, and spindle speed. In turning process for the production of goods is very important to produce a precision product in accordance to desiring of size and roughness. The turning speed of a lathe has a type of spindle rotation rate that is used according to production requirements, which uses a rotational speed that can be changed the rate of rotation of the machine, in order to determine the level of surface roughness in the turning process. One is affected the optimal conditions of the turning speed and feeding rate. In this paper, the variations of different rotational speed levels of low speed, medium speed and high speed according to variations of feeding rate in order to know the difference in roughness results for the screw conveyor shaft operation. The roughness was measured on the surface turning process using a reference of surface roughness stand comparator (ISO2632 / I-1975). The result of test revealed the greater speed of feed rate, the greater value of roughness. Reversely, the smaller speed of feed rate affected the lower roughness value.

scholarly journals Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

2021 ◽  
pp. 71-84
Author(s):  
Nhu-Tung Nguyen ◽  
Dung Hoang Tien ◽  
Nguyen Tien Tung ◽  
Nguyen Duc Luan
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Tool Wear ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Speed ◽  
Milling Process ◽  
Face Milling ◽  
Depth Of Cut ◽  
High Speed Milling

In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

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