scholarly journals Development of surface roughness model in turning process of 3X13 steel using TiAlN coated carbide insert

2021 ◽  
pp. 113-124
Author(s):  
Nhu-Tung Nguyen ◽  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Turning Process ◽  
Nose Radius ◽  
Machining Processes ◽  
Roughness Model ◽  
Input Parameters ◽  
Coated Carbide

Surface roughness that is one of the most important parameters is used to evaluate the quality of a machining process. Improving the accuracy of the surface roughness model will contribute to ensure an accurate assessment of the machining quality. This study aims to improve the accuracy of the surface roughness model in a machnining process. In this study, Johnson and Box-Cox transformations were successfully applied to improve the accuracy of surface roughness model when turning 3X13 steel using TiAlN insert. Four input parameters that were used in experimental process were cutting velocity, feed rate, depth of cut, and insert-nose radius. The experimental matrix was designed using Central Composite Design (CCD) with 29 experiments. By analyzing the experimental data, the influence of input parameters on surface roughness was investigated. A quadratic model was built to explain the relationship of surface roughness and the input parameters. Box-Cox and Johnson transformations were applied to develop two new models of surface roughness. The accuracy of three surface roughness models showed that the surface roughness model using Johnson transformation had the highest accuracy. The second one model of surface roughness is the model using Box-Cox transformation. And surface roughness model without transformation had the smallest accuracy. Using the Johnson transformation, the determination coefficient of surface roughness model increased from 80.43 % to 84.09 %, and mean absolute error reduced from 19.94 % to 16.64 %. Johnson and Box-Cox transformations could be applied to improve the acuaracy of the surface roughness prediction in turning process of 3X13 steel and can be extended with other materials and other machining processes

scholarly journals Surface Roughness Model when Hole Turning SAE 420 Steel

2020 ◽  
Vol 5 (6) ◽  
pp. 683-688
Author(s):  
Ky Hong Le
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Nose Radius ◽  
The Third ◽  
Roughness Model ◽  
Cutting Velocity ◽  
The Relationship

In this paper, a study on surface roughness model when hole turning SAE 420 steel has been done. This study has been presented with three main contents. The first content is to determine the influence of some parameters on the surface roughness. The second content is to build a quadratic model showing the relationship between surface roughness with cutting velocity, depth of cut, feed rate and nose radius. The third content is to evaluate the accuracy of surface roughness model by comparing the roughness value when estimating and roughness value when testing. The development directions for further studies are also mentioned in this study.

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.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

Low Cost Vibration Measurement and Optimization during Turning Process

2018 ◽  
Vol 1148 ◽  
pp. 103-108 ◽  
Author(s):  
N.V.S. Shankar ◽  
A. Gopi Chand ◽  
K. Hanumantha Rao ◽  
K. Prem Sai
Keyword(s):  
Surface Roughness ◽  
Cutting Tool ◽  
Low Cost ◽  
Machining Process ◽  
Vibration Measurement ◽  
Depth Of Cut ◽  
Turning Process ◽  
Taguchi Analysis ◽  
Series Of Experiments

During machining any material, vibrations play a major role in deciding the life of the cutting tool as well as machine tool. The magnitude acceleration of vibrations is directly proportional to the cutting forces. In other words, if we are able to measure the acceleration experienced by the tool during machining, we can get a sense of force. There are many commercially available, pre-calibrated accelerometer sensors available off the shelf. In the current work, an attempt has been made to measure vibrations using ADXL335 accelerometer. This accelerometer is interfaced to computer using Arduino. The measured values are then used to optimize the machining process. Experiments are performed on Brass. During machining, it is better to have lower acceleration values. Thus, the first objective of the work is to minimize the vibrations. Surface roughness is another major factor which criterion “lower is the better” applies. In order to optimize the values, a series of experiments are conducted with three factors, namely, tool type (2 levels), Depth of cut (3 levels) and Feed are considered (3 levels). Mixed level optimization is performed using Taguchi analysis with L18 orthogonal array. Detailed discussion of the parameters shall be given in the article.

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.

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 MINIMUM QUANTITY LUBRICATION ON SURFACE ROUGHNESS IN TOOL-BASED MICROMILLING

2017 ◽  
Vol 18 (1) ◽  
pp. 147-154
Author(s):  
Mohammad Yeakub Ali ◽  
Wan Norsyazila Jailani ◽  
Mohamed Rahman ◽  
Muhammad Hasibul Hasan ◽  
Asfana Banu
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Spindle Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Microfluidic Channels ◽  
Machining Parameters ◽  
Machining Processes ◽  
Dry Cutting ◽  
Minimum Quantity

Cutting fluid plays an important role in machining processes to achieve dimensional accuracy in reducing tool wear and improving the tool life. Conventional flood cooling method in machining processes is not cost effective and consumption of huge amount of cutting fluids is not healthy and environmental friendly. In micromachining, flood cooling is not recommended to avoid possible damage of the microstructures. Therefore, one of the alternatives to overcome the environmental issues to use minimum quantity of lubrication (MQL) in machining process. MQL is eco-friendly and has economical advantage on manufacturing cost. However, there observed lack of study on MQL in improving machined surface roughness in micromilling. Study of the effects of MQL on surface roughness should be carried out because surface roughness is one of the important issues in micromachined parts such as microfluidic channels. This paper investigates and compares surface roughness with the presence of MQL and dry cutting in micromilling of aluminium alloy 1100 using DT-110 milling machine. The relationship among depth of cut, feed rate, and spindle speed on surface roughness is also analyzed. All three machining parameters identified as significant for surface roughness with dry cutting which are depth of cut, feed rate, and spindle speed. For surface roughness with MQL, it is found that spindle speed did not give much influence on surface roughness. The presence of MQL provides a better surface roughness by decreasing the friction between tool and workpiece.

scholarly journals Optimization Study on Turning Process by Using Taguchi-copras Method

2021 ◽  
Vol 309 ◽  
pp. 01010
Author(s):  
Do Duc Trung ◽  
Nguyen Huu Quang ◽  
Tran Quoc Hoang ◽  
Cao The Anh ◽  
Nguyen Hong Linh ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Nose Radius ◽  
Multi Objective ◽  
Optimization Study ◽  
Input Parameters ◽  
Cutting Velocity ◽  
Copras Method

In this article, a multi-objective optimization of turning process study is presented. Two output parameters of the turning process taken into consideration are surface roughness and Material Removal Rate (MRR). Taguchi method has been applied to design the experimental matrix with four input parameters including nose radius, cutting velocity, feed rate and cutting depth. Copras method has been employed to solve the multi-objective optimization problem. Finally, the optimal values of the input parameters have been determined to simultaneously ensure the two criteria of the minimum surface roughness and the maximum MRR.

scholarly journals Research and Modelling of Surface Roughness, Cutting Forces and I-kaz Coefficients for S42C in Turning using Response Surface Methodology

2019 ◽  
Vol 8 (12S2) ◽  
pp. 608-620
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Response Surface ◽  
Cutting Parameters ◽  
Machining Process ◽  
Regression Equations ◽  
Turning Process ◽  
Machining Processes ◽  
Surface Method

This paper presents the optimization in machining processes on the cutting parameters for the S45C in turning process using the response surface method (RSM). The experimental work conducted investigates the influence of cutting parameters on statistical analysis of signals and surface quality. The paper also presents a statistical analysis of signal processing. The cutting force was measured during machining using the Kistler 9129AA dynamometer to monitor the force signals and the data was analyzed using the I-kazTM method of statistical analysis. This statistical analysis was used to assess the effect of force signals during the machining process. The RSM models for Ra and Rz, and Ideveloped with ANOVA and multiple regression equations. The models also were compared and validated with the predicted and measured of Ra and Rz values, and I-kaz coefficients. The optimal configuration of cutting parameters was observed at 200 m/min, 0.1 mm/rev and 0.521 mm with desirability of 95.9%. It is observed that the models developed are suggested to be utilized for predicting surface roughness values and I-kaz coefficients for the machining of S45C steel.

scholarly journals The Outcome of Turning Factors on the Machining Characteristics While Turning 655M13 Steel Alloy using Tialn Coated Carbide Insert

2020 ◽  
Vol 9 (3) ◽  
pp. 1251-1260 ◽  
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Models ◽  
Tangential Force ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Steel Alloy ◽  
Nose Radius ◽  
Feed Force ◽  
Coated Carbide

This exploration is carried out to reveal the outcome of turning factors such as cutting velocity, depth of cut and feed rate on the surface roughness, mean cutting force and tool-work interface temperature on turning cylindrical 655M13 steel alloy components. The experiments are designed based on (33) full factorial design and conducted on a turning centre with Titanium Aluminium Nitride (TiAlN) layered carbide tool of 0.8mm nose radius, simultaneously cutting forces such as feed force, thrust force and tangential force and the tool-work interface temperature are observed using calibrated devices. The surface roughness of the turned steel alloy parts is deliberated by means of a precise surface roughness apparatus. Prediction models are created for average surface roughness, mean cutting force and tool-work interface temperature by nonlinear regression examination with the aid of MINITAB numerical software. The optimum machining conditions are confirmed with the aid of a Genetic Algorithm. The outcome of each turning factor on the surface roughness, mean cutting force and tool-work interface temperature is studied and presented accordingly.

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