Study on the Critical Cutting Thickness and the Precision Cutting Experiments of Isotropic Pyrolyric Graphite

2011 ◽  
Vol 228-229 ◽  
pp. 458-463
Author(s):  
Ming Hai Wang ◽  
Hu Jun Wang ◽  
Zhong Hai Liu
Keyword(s):  
Surface Roughness ◽  
High Temperature ◽  
Prediction Model ◽  
Feed Rate ◽  
Cutting Speed ◽  
Carbon Composite ◽  
Cutting Depth ◽  
Brittle Ductile Transition ◽  
Roughness Prediction ◽  
Turbine Shaft

Isotropic pyrolyric graphite (IPG) is a new kind of brittle material, it not only has the general advantages of ordinal carbonaceous materials such as high temperature resistance, lubrication and abrasion resistance, but also has the advantages of impermeability and machinability that carbon/carbon composite doesn’t have. So it can be used for sealing the aeronautics and astronautics engines turbine shaft and the ethylene high-temperature equipment. The mechanism of this material removal during the precision cutting was analyzed by using the theory of strain gradient. The critical cutting thickness of IPG was calculated for the first time. Furthermore, the cutting process parameters such as cutting depth and feed rate which corresponding to the scale of brittle-ductile transition deformation of IPG was calculated. The prediction model of surface roughness in precision cutting of IPG was developed based on the Genetic algorithm. Using the surface roughness prediction model, the study investigates the influence of the cutting speed, the feed rate and the cutting depth on surface roughness in precision turning process was researched.

A Prediction Model of Surface Roughness in Micro End Milling

2015 ◽  
Vol 727-728 ◽  
pp. 354-357
Author(s):  
Mei Xia Yuan ◽  
Xi Bin Wang ◽  
Li Jiao ◽  
Yan Li
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Feed Rate ◽  
End Milling ◽  
Orthogonal Experiment ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Cutting Depth ◽  
Micro End Milling

Micro-milling orthogonal experiment of micro plane was done in mesoscale. Probability statistics and multiple regression principle were used to establish the surface roughness prediction model about cutting speed, feed rate and cutting depth, and the significant test of regression equation was done. On the basis of successfully building the prediction model of surface roughness, the diagram of surface roughness and cutting parameters was intuitively built, and then the effect of the cutting speed, feed rate and cutting depth on the small structure surface roughness was obtained.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

scholarly journals Experimental Investigation of the influence of the Cutting Conditions on the Surface Roughness in Hole turning the SCM400 Steel

2019 ◽  
Vol 4 (8) ◽  
pp. 11-14
Author(s):  
Nguyen Hong Son ◽  
Hoang Xuan Thinh ◽  
Nhu-Tung Nguyen ◽  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Experimental Design ◽  
Feed Rate ◽  
Cutting Speed ◽  
Orthogonal Matrix ◽  
Cutting Conditions ◽  
Cutting Depth ◽  
Part Surface

This paper presented the experimental results about investigation of the influence of the cutting conditions on the surface roughness when hole turning the SCM400 steel. Three cutting paramesters that have mentioned in this study included cutting speed, axial feed rate, and cutting depth. The experimental design was chosen following the orthogonal matrix and added the center experiment points. The analyzed results show that the axial feed rate has the greatest degree of impact on the surface roughness. And, the second and third factors have negligible effect on the surface roughness that are cutting speed and cutting depth, respectively. These results will guide the determination of the cutting conditions in order to machining the part surface with roughness that was ensured the setting requirement. Finally, the directions for further research were also mentioned in this paper.

Study on Surface Roughness of Milling In-Situ TiB2 Particle Reinforced Al Matrix Composites

2019 ◽  
Author(s):  
Xiao-fen Liu ◽  
Wen-hu Wang ◽  
Rui-song Jiang ◽  
Yi-feng Xiong ◽  
Kun-yang Lin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Feed Rate ◽  
Metal Matrix ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Matrix Composites ◽  
Cutting Depth

Abstract The current state of surface roughness focuses on the 2D roughness. However, there are shortcomings in evaluating surface quality of particle reinforced metal matrix composites using 2D roughness due to the fact that the measuring direction has a vital impact on the 2D roughness value. It is therefore of great importance and significance to develop a proper criterion for measuring and evaluating the surface roughness of cutting particle reinforced metal matrix composites. In this paper, an experimental investigation was performed on the effect of cutting parameters on the surface roughness in cutting in-situ TiB2/7050Al MMCs. The 2D roughness Ra, 3D roughness Sa and Sq were comparatively studied for evaluating the machined surface quality of in-situ TiB2/7050Al MMCs. The influence of cutting parameters on the surface roughness was also analyzed. The big difference between roughness Ra measured along cutting and feed directions showed the great impact of measuring direction. Besides, surface defects such as pits, grooves, protuberances and voids were observed, which would influence 2D roughness value greatly, indicating that 3D roughness was more suitable for evaluating surface quality of cutting in-situ TiB2/7050Al MMCs. The cutting depth and feed rate were found to have the highest influence on 3D roughness while the effect of cutting speed was minimal. With increasing feed rate, cutting depth or width, the 3D roughness increased accordingly. But it decreased as cutting speed increased.

Study on the Cutting Properties about Magnesium Alloy when Dry Turning with Kentanium Cutting Tools

2010 ◽  
Vol 102-104 ◽  
pp. 653-657 ◽  
Author(s):  
Xu Hong Guo ◽  
Li Jun Teng ◽  
Wei Wang ◽  
Ting Ting Chen
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
Tool Wear ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Cutting Depth ◽  
Dry Turning ◽  
Main Influence

In recent years, the machinability of magnesium alloy is concerned more and more by the public. In this paper, a study on the cutting properties of magnesium alloy AZ91D when dry turning with kentanium cutting tools is presented. It shows the cutting force measured by a data acquisition system which is made up of Kistler9257B piezoelectric crystal sensor dynamometer, Kistler5070A10100 charge amplifier and computer. The effect of cutting parameters on cutting force was studied, and the experimental formula was built. The tool wear and chip characteristics were observed with KYKY-EM3200 electron scanning microscope and EDAX PV9900 alpha ray spectrometer, while the surface roughness of the workpiece was measured with 2205 profilometer. Results showed that the cutting depth was the main influence factor on cutting force, followed by feed rate and cutting speed . The main form of tool wear showed to be diffusive wear and adhesive wear. The feed rate had the main influence on chip form and the workpiece surface roughness, cutting speed was less effective, the cutting depth was the least.

Experimental Investigations of Cutting Conditions Influence on Main Cutting Force and Surface Roughness Based on Chip Form Types in Cast Nylon Turning

2011 ◽  
Vol 110-116 ◽  
pp. 3563-3569 ◽  
Author(s):  
Bandit Suksawat
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Causal Effect ◽  
Cutting Speed ◽  
High Speed Steel ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Path Coefficient ◽  
Cutting Depth

This paper aims to investigate cutting conditions influence on main cutting force and surface roughness based on considered chip form types in cast nylon turning operation with single-point high speed steel cutting tool. The 75 experiments were performed by average of three levels of cutting speed, five levels of cutting depth and five levels of feed rate. The results reveal that main cutting forces were increased by an increasing of cutting speed and cutting depth for all obtained chip form types for all chip form types. The surface roughness is affected by increasing of feed rate and reduction of cutting speed for 2.3 Snarled and 4.3 Snarled chip form types. The statistical path-coefficient analysis results are shown that the main cutting force affected by cutting speed, depth of cut and feed rate with total causal effect value of 0.5537, 0.4785 and 0.1718, respectively. The surface roughness is influenced by feed rate, cutting speed and depth of cut with 0.8400, -0.2419 and-0.0711 of total causal effect value, respectively. These results are useful to perform varying cutting conditions for high quality of workpiece in cast nylon turning by control the chip form type.

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 Multi-Response Optimization of Processing Parameters for Micro-Pockets on Alumina Bioceramic Using Rotary Ultrasonic Machining

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5343
Author(s):  
Basem M. A. Abdo ◽  
Hisham Alkhalefah ◽  
Khaja Moiduddin ◽  
Mustufa Haider Abidi
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Ceramic Materials ◽  
Depth Of Cut ◽  
Cutting Depth ◽  
Input Parameters ◽  
Response Optimization ◽  
Rotary Ultrasonic Milling ◽  
Desirability Index

The machining of ceramic materials is challenging and often impossible to realize with conventional machining tools. In various manufacturing applications, rotary ultrasonic milling (RUM) shows strengths, in particular for the development of high-quality micro-features in ceramic materials. The main variables that influence the performance and price of the product are surface roughness, edge chipping (EC), and material removal rate (MRR) during the processing of ceramics. RUM has been considered in this research for the milling of micro-pockets in bioceramic alumina (Al2O3). Response surface methodology in the context of a central composite design (CCD) is being used to plan the experiments. The impacts of important RUM input parameters concerning cutting speed, feed rate, depth of cut, frequency, and amplitude have been explored on the surface roughness in terms of arithmetic mean value (Ra), the EC, and the MRR of the machined pockets. The main effect and the interaction effect of the implemented RUM parameters show that by providing a lower feed rate and cutting depth levels and elevated frequency and cutting speed, the Ra and the EC can be minimized. At greater levels of feed rate and cutting depth, higher MRR can be obtained. The influence of RUM input parameters on the surface morphology was also recorded and analyzed using scanning electron microscopic (SEM) images. The study of the energy dispersive spectroscopy (EDS) shows that there is no modification in the alumina bioceramic material. Additionally, a multi-response optimization method has been applied by employing a desirability approach with the core objectives of minimizing the EC and Ra and maximizing the MRR of the milled pockets. The obtained experimental values for Ra, EC, and MRR at an optimized parametric setting were 0.301 µm, 12.45 µm, and 0.873 mm3/min respectively with a combined desirability index value of 0.73.

scholarly journals Prediction of Surface Roughness and Optimization of Cutting Parameters of Stainless Steel Turning Based on RSM

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Maohua Xiao ◽  
Xiaojie Shen ◽  
You Ma ◽  
Fei Yang ◽  
Nong Gao ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Surface Design ◽  
Composite Surface ◽  
Optimization Of Cutting Parameters

The turning test of stainless steel was carried out by using the central composite surface design of response surface method (RSM) and Taguchi design method of central combination design. The influence of cutting parameters (cutting speed, feed rate, and cutting depth) on the surface roughness was analyzed. The surface roughness prediction model was established based on the second-order RSM. According to the test results, the regression coefficient was estimated by the least square method, and the regression equation was curve fitted. Meanwhile, the significance analysis was conducted to test the fitting degree and response surface design and analysis, in addition to establishing a response surface map and three-dimensional surface map. The life of the machining tool was analyzed based on the optimized parameters. The results show that the influence of feed rate on the surface roughness is very significant. Cutting depth is the second, and the influence of cutting speed is the least. Therefore, the cutting parameters are optimized and tool life is analyzed to realize the efficient and economical cutting of difficult-to-process materials under the premise of ensuring the processing quality.

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.

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