scholarly journals Surface Roughness and Residual Stresses of High Speed Turning 300 M Ultrahigh Strength Steel

2014 ◽  
Vol 6 ◽  
pp. 859207 ◽  
Author(s):  
Zhang Huiping ◽  
Zhang Hongxia ◽  
Lai Yinan
Keyword(s):  
Residual Stress ◽  
Surface Roughness ◽  
Prediction Model ◽  
Feed Rate ◽  
High Speed ◽  
Stress Test ◽  
Orthogonal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Factors Affecting

Firstly, a single factor test of the surface roughness about tuning 300 M steel is done. According to the test results, it is direct to find the sequence of various factors affecting the surface roughness. Secondly, the orthogonal cutting experiment is carried out from which the primary and secondary influence factors affecting surface roughness are obtained: feed rate and corner radius are the main factors affecting surface roughness. The more the feed rate, the greater the surface roughness. In a certain cutting speed rang, the surface roughness is smaller. The influence of depth of cut to the surface roughness is small. Thirdly, according to the results of the orthogonal experiment, the prediction model of surface roughness is established by using regressing analysis method. Using MatLab software, the prediction mode is optimized and the significance test of the optimized model is done. It showed that the prediction model matched the experiment results. Finally, the surface residual stress test of turning 300 M steel is done and the residual stress of the surface and along the depth direction is measured.

Investigation of Surface Characteristics and Chip Morphology in High Speed Cutting of Inconel718 Based on SHPB System

2019 ◽  
Author(s):  
Zengqiang Wang ◽  
Zhanfei Zhang ◽  
Wenhu Wang ◽  
Ruisong Jiang ◽  
Kunyang Lin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
Split Hopkinson Pressure Bar ◽  
Orthogonal Cutting ◽  
Cutting Speed ◽  
Surface Profile ◽  
Chip Morphology ◽  
Depth Of Cut ◽  
Machined Surface ◽  
High Speed Cutting

Abstract High speed cutting (HSC) technology has the characteristics of high material removal rates and high machining precision. In order to study the relationships between chip morphology and machining surface characteristic in high speed cutting of superalloy Inconel718. High-speed orthogonal cutting experiment are carried out by used a high speed cutting device based on split Hopkinson pressure bar (SHPB). The specimen surfaces and collected chips were then detected with optical microscope, scanning electron microscope and three-dimensional surface profile measuring instrument. The results show that within the experimental parameters (cutting speed from 8–16m/s, depth of cut 0.1–0.5mm), the obtained chips are sawtooth chips and periodic micro-ripple appear on the machined surface. With the cutting speed increases, machining surface roughness is decreases from 1.4 to 0.99μm, and the amplitude of periodic ripples also decreases. With the cutting depth increases, the machining surface roughness increases from 0.96 to 5.12μm and surface topography becomes worse. With the increase of cutting speed and depth of cut, the chips are transform from continues sawtooth to sawtooth fragment. By comparing the frequency of surface ripples and sawtooth chips, it is found that they are highly consistent.

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.

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

scholarly journals Precision Hard Turning of Ti6Al4V Using Polycrystalline Diamond Inserts: Surface Quality, Cutting Temperature and Productivity in Conventional and High-Speed Machining

Materials ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 5677
Author(s):  
Elshaimaa Abdelnasser ◽  
Azza Barakat ◽  
Samar Elsanabary ◽  
Ahmed Nassef ◽  
Ahmed Elkaseer
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
High Speed ◽  
Hard Turning ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Polycrystalline Diamond ◽  
Depth Of Cut ◽  
High Speed Machining ◽  
Conventional Machining

This article presents the results of an experimental investigation into the machinability of Ti6Al4V alloy during hard turning, including both conventional and high-speed machining, using polycrystalline diamond (PCD) inserts. A central composite design of experiment procedure was followed to examine the effects of variable process parameters; feed rate, cutting speed and depth of cut (each at five levels) and their interaction effects on surface roughness and cutting temperature as process responses. The results revealed that cutting temperature increased with increasing cutting speed and decreasing feed rate in both conventional and high-speed machining. It was found that high-speed machining showed an average increase in cutting temperature of 65% compared with conventional machining. Nevertheless, high-speed machining showed better performance in terms of lower surface roughness despite using higher feed rates compared to conventional machining. High-speed machining of Ti6Al4V showed an improvement in surface roughness of 11% compared with conventional machining, with a 207% increase in metal removal rate (MRR) which offered the opportunity to increase productivity. Finally, an inverse relationship was verified between generated cutting temperature and surface roughness. This was attributed mainly to the high cutting temperature generated, softening, and decreasing strength of the material in the vicinity of the cutting zone which in turn enabled smoother machining and reduced surface roughness.

scholarly journals Modeling and Parameter Optimization for Surface Roughness and Residual Stress in Dry Turning Process

2017 ◽  
Vol 7 (5) ◽  
pp. 2047-2055
Author(s):  
M. H. El-Axir ◽  
M. M. Elkhabeery ◽  
M. M. Okasha
Keyword(s):  
Residual Stress ◽  
Surface Roughness ◽  
Feed Rate ◽  
Rotational Speed ◽  
Cutting Speed ◽  
Electrochemical Analysis ◽  
Depth Of Cut ◽  
Roughness Parameters ◽  
Etching Technique ◽  
Surface Roughness Parameters

The influence of some turning variables and tool overhang on surface roughness parameters and residual stress induced due to machining 6061-T6 aluminum alloy is investigated in this paper. Four input parameters (cutting speed, feed rate, depth of cut and tool overhang) are considered. Tests are carried out by precision turning operation on a lathe. Design of experiment techniques, i.e. response surface methodology (RSM) and Taguchi's technique have been used to accomplish the objective of the experimental study. Surface roughness parameters are measured using a portable surface roughness device while residual stresses are measured employing deflection-etching technique using electrochemical analysis. The results obtained reveal that feed and rotational speed play significant role in determining the average surface roughness. Furthermore, the depth of cut and tool overhang are less significant parameters, whereas tool overhang interacts with feed rate. The best result of surface roughness was obtained using low or medium values of overhang with low speed and /or feed rate. Minimum maximum tensile residual stress can be obtained with a combination of tool overhang of 37 mm with very low depth of cut, low rotational speed and feed rate of 0.188 mm/rev.

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.

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.

scholarly journals Determination of Optimum Machining Condition for Surface Roughness Using Historical Data

2018 ◽  
Vol 7 (4.30) ◽  
pp. 73
Author(s):  
Mohd Shahfizal Mohd Ruslan ◽  
Haniff Abdul Rahman ◽  
Jaharah Abdul Ghani ◽  
Che Hassan Che Haron ◽  
Mohd Shahir Kassim ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
Optimum Condition ◽  
Feed Rate ◽  
High Speed ◽  
Historical Data ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Depth Of Cut ◽  
Radial Depth

Magnesium alloy is one of the lightest materials with a high strength to weight ratio and excellent machinability, which makes it attractive and suitable for various industrial applications such as automotive and aerospace components. For these particular industrial components, the end products require a mirror-like finish. This article details a statistical analysis about the effect of milling parameters on the surface roughness of Magnesium alloy AZ91D in the dry milling process. The historical data approach in the response surface methodology (RSM) was utilized to determine the cause and effect relationship between the input variables and output response. The effect of milling parameter studied was cutting speed (900 – 1400 m/min), feed rate (0.03 - 0.09 mm/tooth), and radial depth of cut (0.2 - 0.3 mm). The results confirmed that the interaction between feed rate and cutting speed is the primary factor controlling the surface evolution. The responses of various factors were plotted using a two-dimensional interaction graph and the cubic empirical model was developed at 95% confidence level. The optimum condition for achieving the minimum surface roughness was a cutting speed of 977 m/min, a feed rate of 0.02 mm/tooth, and an axial depth of cut of 0.29 mm. With this optimum condition, a surface arithmetic roughness of 0.054 μm is expected. This study confirmed that by milling AZ91D at high speed cutting, it is possible to eliminate the polishing process to achieve a super mirror-like finishing.

Optimization of Ti-6Al-4V Flank Milling Parameters Using Taguchi Method

2009 ◽  
Vol 69-70 ◽  
pp. 364-368
Author(s):  
Chang Yi Liu ◽  
Jun Jie Yi ◽  
Wen Hui Zhou ◽  
Cheng Long Chu
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Speed ◽  
Flank Milling ◽  
Depth Of Cut ◽  
Milling Parameters ◽  
Good Surface ◽  
Operation Parameters

This paper presents a study of the Taguchi design methodology, which is applied to optimize flank milling operation parameters when machining titanium alloy Ti-6Al-4V in conventional and high speed regimes. This study includes cutting speed, feed rate and depth of cut. Experimental runs are conducted using an orthogonal array of L9(33), with measurement of surface roughness. ANOVA analyses are carried out to analyze the effect of these operation parameters, and the optimal parameters combination is determined by seeking the best surface roughness. The analysis of result shows that the optimal combination for good surface roughness is high cutting speed, low feed rate. Finally, confirmation run verifies the results, which indicated that Taguchi method is both efficient and effective in determining the Ti-6Al-4V flank milling parameters for the optimization with minimum number of trials.

Cutting Temperature and Cutting Forces Investigation Based on the Taguchi Design Method when High-Speed Milling of Titanium Matrix Composites with PCD Tool

2016 ◽  
Vol 836-837 ◽  
pp. 168-174 ◽  
Author(s):  
Ying Fei Ge ◽  
Hai Xiang Huan ◽  
Jiu Hua Xu
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
High Speed ◽  
Volume Fraction ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
High Speed Milling ◽  
Radial Depth

High-speed milling tests were performed on vol. (5%-8%) TiCp/TC4 composite in the speed range of 50-250 m/min using PCD tools to nvestigate the cutting temperature and the cutting forces. The results showed that radial depth of cut and cutting speed were the two significant influences that affected the cutting forces based on the Taguchi prediction. Increasing radial depth of cut and feed rate will increase the cutting force while increasing cutting speed will decrease the cutting force. Cutting force increased less than 5% when the reinforcement volume fraction in the composites increased from 0% to 8%. Radial depth of cut was the only significant influence factor on the cutting temperature. Cutting temperature increased with the increasing radial depth of cut, feed rate or cutting speed. The cutting temperature for the titanium composites was 40-90 °C higher than that for the TC4 matrix. However, the cutting temperature decreased by 4% when the reinforcement's volume fraction increased from 5% to 8%.

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