Effect of Cutting Parameters on the Surface Residual Stress of Face-Milled Pearlitic Ductile Iron

2017 ◽  
Vol 4 (1) ◽  
pp. 38-52
Author(s):  
Olutosin Olufisayo Ilori ◽  
Dare A. Adetan ◽  
Lasisi E. Umoru
Keyword(s):  
Residual Stress ◽  
Fluid Flow ◽  
Flow Rate ◽  
Ductile Iron ◽  
Cutting Parameters ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Fluid Flow Rate ◽  
Surface Residual Stress ◽  
Average Surface

The study determined the effect of cutting parameters on the surface residual stress of face-milled pearlitic ductile iron with a view to enhancing surface integrity of machined parts in the manufacturing industries. The pearlitic ductile iron used for this study was prepared and four cutting parameters were considered. The results obtained showed that the average surface residual stress of the machined surfaces was tensile and increased significantly with increase in depth of cut. Feed rate and cutting speed exhibited some effect, though not statistically significant, on average surface residual stress. The average residual stress was found to decrease significantly and drastically from 605.39 MPa to 101.72 MPa as cutting fluid flow rate increased from 0 ?/min to 4 ?/min. The study concluded that out of all four cutting parameters investigated, the cutting fluid flow rate has most considerable influence on the surface residual stress of the machined pearlitic ductile iron.

scholarly journals Influence of Cutting Fluid Flow Rate and Cutting Parameters on the Surface Roughness and Flank Wear of TiAlN Coated Tool In Turning AISI 1015 Steel Using Taguchi Method

2017 ◽  
Vol 62 (3) ◽  
pp. 1827-1832 ◽  
Author(s):  
C. Moganapriya ◽  
R. Rajasekar ◽  
K. Ponappa ◽  
R. Venkatesh ◽  
R. Karthick
Keyword(s):  
Surface Roughness ◽  
Fluid Flow ◽  
Tool Wear ◽  
Flow Rate ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Fluid Flow Rate

AbstractThis paper presents the influence of cutting parameters (Depth of cut, feed rate, spindle speed and cutting fluid flow rate) on the surface roughness and flank wear of physical vapor deposition (PVD) Cathodic arc evaporation coated TiAlN tungsten carbide cutting tool insert during CNC turning of AISI 1015 mild steel. Analysis of Variance has been applied to determine the critical influence of cutting parameters. Taguchi orthogonal test design has been employed to optimize the process parameters affecting surface roughness and tool wear. Depth of cut was found to be the most dominant factor contributing to high surface roughness (67.5%) of the inserts. However, cutting speed, feed rate and flow rate of cutting fluid showed minimal contribution to surface roughness. On the other hand, cutting speed (45.6%) and flow rate of cutting fluid (23%) were the dominant factors influencing tool wear. The optimum cutting conditions for desired surface roughness constitutes the following parameters such as medium cutting speed, low feed rate, low depth of cut and high cutting fluid flow rate. Minimal tool wear was achieved for the following process parameters such as low cutting speed, low feed rate, medium depth of cut and high cutting fluid flow rate.

Effect of coating on tool inserts and cutting fluid flow rate on the machining performance of AISI 1015 steel

2018 ◽  
Vol 60 (12) ◽  
pp. 1202-1208
Author(s):  
Chinnasamy Moganapriya ◽  
Rathanasamy Rajasekar ◽  
Kannayiram Ponappa ◽  
Palaniappan Sathish Kumar ◽  
Samir Kumar Pal ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Flow Rate ◽  
Cutting Fluid ◽  
Fluid Flow Rate ◽  
Machining Performance

MODELING AND OPTIMIZATION OF THE CUTTING FLUID FLOW AND PARAMETERS FOR INCREASING TOOL LIFE IN SLOT MILLING ON St52

2013 ◽  
Vol 04 (02) ◽  
pp. 1350001
Author(s):  
AMIR MAHYAR KHORASANI ◽  
ALEX KOOTSOOKOS
Keyword(s):  
Fluid Flow ◽  
Tool Life ◽  
Optimization Technique ◽  
Experimental Tests ◽  
Cutting Parameters ◽  
Cnc Machining ◽  
Cutting Fluid ◽  
Optimization Process ◽  
Depth Of Cut ◽  
Artificial Neural Network Ann

In this paper the CNC machining of St52 was modeled using an artificial neural network (ANN) in the form of a four-layer multi-layer perceptron (MLP). The cutting parameters used in the model were cutting fluid flow, feed rate, spindle speed and the depth of cut and the model output was the tool life. For obtaining more accuracy and spending less time Taguchi design of experiment (DOE) has been used and correlation between the output of the ANN and the experimental results was 96%. Further optimization process has been done by use of a genetic algorithm (GA). After optimization process tool life was increased about 8% equal to 33 min and was corroborated by experimental tests. This demonstrates that the coupling of an ANN with the GA optimization technique is a valid and useful approach to use.

Analysis of MQL Effect on Forces, Friction, and Surface Roughness in Turning of AISI 4140 Steel

2018 ◽  
Author(s):  
Rodrigo Maldonado Mendieta ◽  
Juan de Dios Calderón Nájera
Keyword(s):  
Surface Roughness ◽  
Fluid Flow ◽  
Tool Wear ◽  
Cutting Force ◽  
Flow Rate ◽  
Numerical Control ◽  
Cutting Fluid ◽  
Fluid Flow Rate ◽  
Aisi 4140 ◽  
Fea Simulation

Minimum Quantity Lubrication or MQL is an increasingly used technique for metal cutting operations and it has become an attractive alternative for machining parts at big scale production. However, fully lubricated conditions are still in use for machining special materials so that surface finish, tool wear, and temperature distribution levels remain at optimum levels. On the other hand, dry condition machining is in use as well although with some restraint due to issues with material burr, surface roughness, and tool wear. The main purpose of this work is to analyze the effects of cutting fluid flow rate, its application mechanisms, and cutting speed on surface roughness and establish the lowest possible cutting fluid flow rate that yields to minimum surface roughness (Ra). To achieve the objective, a set of experiments was performed using a Computer Numerical Control (CNC) lathe instrumented with a Kistler 9121 dynamometer and a customized cutting fluid application system to obtain coefficients of friction and cutting forces. Finally, a previously 2D finite element analysis (FEA) simulation from Akbar et al. [1] is applied and compared to experimental results to find out if the cutting force can be predicted. A first regression model that correlates cutting force and surface roughness is posed, so that FEA simulation can be implemented to predict the final surface roughness. AISI 4140 machinery steel in annealed condition is used to carry out the simulated and experimental work.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

Application of Cross-correlation Analysis Method for Measurement of the Fluid Flow Rate Based on X-ray Radiation

2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Correlation Analysis ◽  
Flow Rate ◽  
Cross Correlation ◽  
Fluid Flow Rate ◽  
Analysis Method ◽  
X Ray ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

A Suction Device Using Air Under Pressure

1956 ◽  
Vol 23 (2) ◽  
pp. 269-272
Author(s):  
L. F. Welanetz
Keyword(s):  
Fluid Flow ◽  
Flow Rate ◽  
Fluid Flows ◽  
Central Hole ◽  
Fluid Flow Rate ◽  
Circular Plates ◽  
Suction Device ◽  
Holding Power ◽  
Plate Separation

Abstract An analysis is made of the suction holding power of a device in which a fluid flows radially outward from a central hole between two parallel circular plates. The holding power and the fluid flow rate are determined as functions of the plate separation. The effect of changing the proportions of the device is investigated. Experiments were made to check the analysis.

scholarly journals Estimation of Flow Rate Through Analysis of Pipe Vibration

2018 ◽  
Vol 12 (4) ◽  
pp. 294-300 ◽  
Author(s):  
Santhosh K. Venkata ◽  
Bhagya R. Navada
Keyword(s):  
Neural Network ◽  
Fluid Flow ◽  
Flow Rate ◽  
Back Propagation ◽  
Vibration Signal ◽  
Vibration Sensor ◽  
Percentage Error ◽  
Fluid Flow Rate ◽  
Bee Colony ◽  
Neural Network Algorithm

Abstract In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.

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