Effect of Cutting Parameters on Surface Roughness in Turning of Bone

2013 ◽  
Vol 845 ◽  
pp. 708-712 ◽  
Author(s):  
P.Y.M. Wibowo Ndaruhadi ◽  
S. Sharif ◽  
M.Y. Noordin ◽  
Denni Kurniawan
Keyword(s):  
Surface Roughness ◽  
Bone Tissue ◽  
Influencing Factor ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Machining Process ◽  
Turning Operation ◽  
Cutting Direction

Surface roughness indicates the damage of the bone tissue due to bone machining process. Aiming at inducing the least damage, this study evaluates the effect of some cutting conditions to the surface roughness of machined bone. In the turning operation performed, the variables are cutting speed (26 and 45 m/min), feed (0.05 and 0.09 mm/rev), tool type (coated and uncoated), and cutting direction (longitudinal and transversal). It was found that feed did not significantly influence surface roughness. Among the influencing factor, the rank is tool type, cutting speed, and cutting direction.

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.

Prediction of Surface Roughness in High Speed Machining of Inconel 718

2011 ◽  
Vol 264-265 ◽  
pp. 1193-1198
Author(s):  
Mokhtar Suhaily ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Inconel 718 ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Machining

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

scholarly journals VARIATION OF CUTTING PARAMETERS IN THE PROCESS OF TURNING AISI 4340 STEEL ON SURFACE ROUGHNESS

SINERGI ◽  
2019 ◽  
Vol 23 (2) ◽  
pp. 139
Author(s):  
M. Sobron Yamin Lubis ◽  
Erwin Siahaan ◽  
Steven Darmawan ◽  
Adianto Adianto ◽  
Ronald Ronald
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Aisi 4340 Steel ◽  
4340 Steel ◽  
Metal Machining ◽  
Aisi 4340

In the metal machining process, cutting speed and feed rate are cutting parameters that affect the surface quality of the workpiece produced. The use of improper cutting parameters can cause the workpiece surface to be rough, and the cutting toolage to be shorter. This study was conducted to determine the effect of cutting parameters and the use of carbide tools on the surface roughness of metal steel workpieces. The research was carried out using the experimental method of AISI 4340 steel metal workpiece turning using cutting tool coated. Five variations of cutting speed used are: 140 m/min, 150 m/min, 160 m/min, 170 m/min, 180 m/min and three variations in feed rate: 0.25 mm/rev, 0.3 mm/rev, 0.35 mm/rev. After the turning process, the surface roughness of the workpiece is measured using a surface tester. From the results of the study, it was found that the surface roughness value was directly proportional to the feed rate and inversely proportional to the cutting speed. The smallest surface roughness value is 9.56 μm on cutting speed 180 m / min, and feed rate is 0.25 mm/rev. 

Optimization of Cutting Parameters for End Milling AlSi/AlN Metal Matrix Composite Using the Taguchi Method

2013 ◽  
Vol 773-774 ◽  
pp. 429-436 ◽  
Author(s):  
Siti Haryani Tomadi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron ◽  
Abdul Razak Daud
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Matrix Composite ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Optimum Cutting ◽  
Optimization Of Cutting Parameters

In this paper, the optimization of cutting parameters is investigated to assess surface roughness and cutting force in the end milling of AlSi/AlN metal matrix composite. Eighteen experiments (L18) with five factors (cutting speed, feed rate, depth of cut, volume of particle reinforcement, and type of coated insert) were performed based on Taguchi designs of the experiment method. Two types of coating (TiB2 and TiN/TiCN/TiN) of the carbide cutting tool were employed to machine various volumes of AlN particle (5%, 7% and 10%) reinforced AlSi alloy matrix composite under dry cutting conditions. Signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were applied to investigate the optimum cutting parameters and their significance. The S/N analysis of the obtained results showed that the optimum cutting conditions for the cutting force were; A2 (triple coating of the insert), B2 (cutting speed: 200m/min), C1 (feed rate: 0.6mm/tooth), D1 (axial depth: 0.6mm) and E1 (5% reinforcement). At the mean time, the optimum cutting conditions for surface roughness were; A1 (single coating of insert), B3 (cutting speed: 250m/min), C2 (feed rate: 0.75mm/tooth), D1 (axial depth: 0.6mm) and E1 (5% reinforcement).The study confirmed that, with a minimum number of experiments, the Taguchi method is capable of determining the optimum cutting conditions for the cutting force and surface roughness for this new material under investigation.

scholarly journals Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel

2012 ◽  
Vol 9 (1) ◽  
pp. 37 ◽  
Author(s):  
LB Abhang ◽  
M Hameedullah
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Engineering Sciences

 Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions. 

The Surface Roughness Modeling on Turn-Milling Process and Analysis of Influencing Factors

2011 ◽  
Vol 117-119 ◽  
pp. 1614-1620 ◽  
Author(s):  
Song Mei Yuan ◽  
Wei Wei Zheng
Keyword(s):  
Surface Roughness ◽  
Geometric Model ◽  
Cutting Speed ◽  
Design Feature ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Process ◽  
Rotating Speed ◽  
Movement Parameters ◽  
Turn Milling

Surface roughness plays an important role in product quality and has received serious attention for many years. It has formulated an important design feature in many situations such as parts subject to fatigue loads、precision fits、fastener holes and aesthetic requirements. In addition to tolerances, surface roughness imposes one of the most critical constraints for the selection of machines and cutting parameters in process planning. This paper focuses on developing a geometric model of turn-milling process in order to predict the surface roughness effectively so that we can offer reference or consulting in the practical machining process. The surface roughness model takes into account both cutting parameters and movement parameters such as cutter radius feed per tooth 、 cutting speed 、number of teeth 、cutters' eccentricity、 depth of cutting、spindle rotating speed and so on. Through this model, we discussed the influencing effects of various cutting parameters on the surface roughness and analyze which one has the most important impact.

Machinability Improvement by Workpiece Preheating during End Milling AISI H13 Hardened Steel

2011 ◽  
Vol 264-265 ◽  
pp. 888-893
Author(s):  
Mokhtar Suhaily ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Inconel 718 ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Machining

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

scholarly journals THE EFFECT OF FEED RATE AND CUTTING SPEED TO SURFACE ROUGHNESS DURING HOLE BORING OF ALUMINUM WITH ANTI-VIBRATION BORING BAR

2021 ◽  
Vol 3 ◽  
pp. 314-318
Author(s):  
Sabi Sabev ◽  
Plamen Kasabov
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Speed ◽  
Mathematic Model ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Boring Bar ◽  
Experiment Analysis

this study is focusing on the experimental investigation of the effects of cutting parameters on surface roughness during hole boring of 8062 aluminums with anti-vibration boring bar on lathe. Several experiments were conducted with different cutting conditions. Based on the results and using “Minitab 19” software, a mathematic model was made to predict the surface quality in connection with different cutting conditions. Finally, an experiment analysis was carried out to verify the analytical results. 

Investigation of Surface Roughness in Hardened GCr15 Steel Turning with Mixed Ceramic Insert

2012 ◽  
Vol 602-604 ◽  
pp. 1600-1603
Author(s):  
Bo Di Cui
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Dominant Factor ◽  
Nose Radius ◽  
Gcr15 Steel ◽  
Finish Hard Turning ◽  
Artificial Neural Network Ann ◽  
Mixed Ceramic

An experimental investigation was conducted to determine the effects of cutting conditions on surface roughness in finish hard turning of GCr15 steel with mixed ceramic inserts based on design of experiment. The influence of cutting speed, feed rate and nose radius on surface roughness was assessed using analysis of variance (ANOVA). The result indicated that the feed is the dominant factor on surface roughness followed by nose radius. Due to the complexity of machining process, artificial neural network (ANN) was employed to develop the predictive model of surface roughness. Simulations were done to describe the relationship between surface roughness and cutting parameters based on the proposed model.

Analysis of the Machinability of Aluminium Alloys UNS A97050-T7 and UNS A92024-T3 during Short Dry Turning Tests

2011 ◽  
Vol 264-265 ◽  
pp. 931-936 ◽  
Author(s):  
B. de Agustina ◽  
A. Saa ◽  
Mariano Marcos Bárcena ◽  
E.M. Rubio
Keyword(s):  
Surface Roughness ◽  
Aluminium Alloys ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Environmental Conservation ◽  
Machining Process ◽  
Cutting Fluids ◽  
Dry Machining ◽  
Production Technologies

The aluminium alloys are widely employed in the aeronautical, aerospace and automotive industries in the most important manufacturing processes. This is due to the fact they have a high resistance even at high temperatures as well as a low density. Nevertheless, these materials can commonly show problems associated with the heat generated during the machining process that reduces their machinability. For this reason, cutting fluids are still widely used. However, the growing social preoccupation towards environmental conservation has made it necessary to develop cleaner production technologies as dry machining, in which no cutting fluids are employed. This situation makes necessary to look for combinations of cutting parameters and types of tools that improve the machining in those extreme work. In this study, the UNS A97050-T7 and UNS A92024-T3 aluminium alloys were analyzed in terms of surface roughness and the morphology of chips obtained, using tools with TiN coating. It was found that the surface quality of the aluminium UNS A97050-T7 and UNS A92024-T3 bars improves with the descent of the feed and with the increase of the cutting speed, being the feed the cutting parameter more influential on the surface roughness. Thus the machining of the UNS A92024-T3 allows obtaining shorter chips than the UNS A97050-T7.

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