scholarly journals Development of Models for Predicting Some Surface Responses in Oblique Metal Cutting Using Mild Steel and Coated Carbide

2021 ◽  
Vol 6 (3) ◽  
pp. 34-38
Author(s):  
I. T. Okafor ◽  
J. O. Osarenmwinda ◽  
M. K. Onifade
Keyword(s):  
Surface Roughness ◽  
Mild Steel ◽  
Metal Cutting ◽  
Milling Process ◽  
Work Piece ◽  
Absolute Value ◽  
Machine Centre ◽  
The Absolute ◽  
Α Level ◽  
Coated Carbide

Oblique metal cutting is a milling process which constitutes the work piece, tool piece, machine centre and the machinist or operator. This research has been able to obtain some responses such as tool life, and the surface roughness. Most machine elements fail due to some poor surface finish errors such as craters, waviness, flay and lay. Previous researchers have focused more on the absolute value of the surface roughness (Ra) and this cannot provide for all the errors encountered on surface texture. The primary aim of this research is to develop models that can predict the surface roughness of machine parts produced in oblique metal cutting above the absolute value of the average surface roughness and in turn provide a document or framework for machinist which can serve as a guide for machinist. The work has been able to determine a near perfect surface roughness for mild steel using coated carbide as tool piece as the models developed has minimized the effect of surface roughness at run 6, 6 and 8 for Ra, Rz and maximized TL in the values of 1.071408 micrometer, 2.668293 micrometer and 49837238 seconds respectively. Also. the analysis of variance performed has also shown that is proper to accept the analysis using a significance α level of 0.05.

Machinability of Aluminum Nitride Ceramic Using TiAlN and TiN Coated Carbide Tool Insert

2013 ◽  
Vol 773-774 ◽  
pp. 437-447
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Abou Ei Hossein A. Khaled ◽  
D. Sujan
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
End Milling ◽  
Cutting Speed ◽  
Milling Process ◽  
Work Piece ◽  
Carbide Tool ◽  
Coated Carbide ◽  
Cnc End Milling ◽  
End Milling Process

This research presents the performance of Aluminum nitride ceramic in end milling using using TiAlN and TiN coated carbide tool insert under dry machining. The surface roughness of the work piece and tool wear was analyzed in this. The design of experiments (DOE) approach using Response surface methodology was implemented to optimize the cutting parameters of a computer numerical control (CNC) end milling machine. The analysis of variance (ANOVA) was adapted to identify the most influential factors on the CNC end milling process. The mathematical predictive model developed for surface roughness and tool wear in terms of cutting speed, feed rate, and depth of cut. The cutting speed is found to be the most significant factor affecting the surface roughness of work piece and tool wear in end milling process.

scholarly journals Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Tools ◽  
Computing Methods ◽  
Milling Process ◽  
Dry Cutting ◽  
Inference System ◽  
Cutting Processes ◽  
Adaptive Neurofuzzy Inference System

Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end millingTi6Al4Valloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing ofANFISmodels, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability ofANFISin modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.

scholarly journals Optimization of process parameters on commercial mild steel using taguchi technique

2017 ◽  
Vol 7 (1.1) ◽  
pp. 138 ◽  
Author(s):  
V. Jaiganesh ◽  
B. Yokesh Kumar ◽  
P. Sevvel ◽  
A.J. Balaji
Keyword(s):  
Surface Roughness ◽  
Mild Steel ◽  
Metal Cutting ◽  
Metal Removal ◽  
Removal Rate ◽  
Chip Thickness ◽  
Critical Factor ◽  
Thickness Ratio ◽  
Depth Of Cut ◽  
Versus Input

In the present scenario of bulk manufacturing where Metal Removal Rate (MRR), Chip Thickness Ratio (CTR) and Surface Roughness (SR) is of significant importance in manufacturing the component using CNC (computer numerical controlled) machines. Nine experiments were conducted based on orthogonal array. General linear model has been generated for all the three output parameters such as (MRR, Chip Thickness Ratio surface roughness) versus input parameters (speed, time, depth of cut). The statistical method called the analysis of variance (ANOVA) is applied to find the critical factor. The Main effects of S/N ratio values are found and plotted in the form of graph. The optimized value is found for speed, time, and depth of cut by using “MINITAB” software. By using this optimized value the efficient metal cutting can be done in commercial mild steel.

Investigating the Effect of Cutting Speed Variation on Surface Roughness of 7136 Aluminum Alloy in End Milling

2015 ◽  
Vol 809-810 ◽  
pp. 129-134 ◽  
Author(s):  
Alina Bianca Bonţiu Pop ◽  
Mircea Lobonţiu
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Milling Process ◽  
Future Research ◽  
Cutting Depth ◽  
Speed Variation

Surface quality is affected by various processing parameters and inherent uncertainties of the metal cutting process. Therefore, the surface roughness anticipation becomes a real challenge for engineers and researchers. In previous researches [1] I have investigated the feed rate influence on surface roughness and manufacturing time reduction. The 7136 aluminum alloy was machined by end milling operation using standard tools for aluminum machining. The purpose of this paper is to identify by experiments the influence of cutting speed variation on surface roughness. The scientific contribution brought by this research is the improvement of the end milling process of 7136 aluminum alloy. This material is an aluminum alloy developed by Universal Alloy Corporation and is used in the aircraft industry to manufacture parts from extruded profiles. The research method used to solve the problem is experiment. A range of cutting speeds was used while the cutting depth and the feed per tooth were constrained per minimum and maximum requirements defined for the given cutting tool. The experiment was performed by using a 16 mm End milling cutter, holding two indexable cutting inserts. The machine used for the milling tests was a HAAS VF2 CNC. The surface roughness (response) was measured by using a portable surface roughness tester (TESA RUGOSURF 20 Portable Surface Finish Instrument). Following the experimental research, results were obtained which highlight the cutting speed influence on surface roughness. Based on these results we created roughness variation diagrams according to the cutting speed for each value of feed per tooth and cutting depth. The final results will be used as data for future research.

Surface Roughness Study of Milled Carbon Fiber Reinforced Polymer (CFRP) Composite Using 4 mm 2-Flute Titanium Aluminum Nitride (TiAlN) Coated Carbide End Mills

2014 ◽  
Vol 887-888 ◽  
pp. 1101-1106 ◽  
Author(s):  
Mohamed Konneh ◽  
Sudin Izman ◽  
Mirza Emmil Dzahi Padil ◽  
Rosniza Roszat
Keyword(s):  
Surface Roughness ◽  
Weight Ratio ◽  
High Strength ◽  
Milling Process ◽  
Machining Parameters ◽  
Inherent Anisotropy ◽  
Pull Out ◽  
Cfrp Composite ◽  
End Mills ◽  
Coated Carbide

As the goal for aircraft weight reduction and low fuel consumption becomes a dire concern in aerospace industries, there is driving desire for the increasing use of advanced exotic materials such as composites, titanium and Inconels in the aerospace industry because of their high strength to weight ratio. Nevertheless the inherent anisotropy, inhomogeneous properties of CFRP and low bonding strength within the laminates make machining of these composite materials results in several undesirable effects such as delamination, micro-cracking, burr, fiber pull out and breakage. This paper discusses an experimental investigation into the influence of machining parameters on surface roughness when milling CFRP using 4 mm-diameter 2-fluted carbide end-mill coated with Titanium Aluminium Nitride (TiAlN). Relationship between the machining variables and the output variables is established and a mathematical model is predicted for the surface roughness produced during the milling process for the machining conditions investigated.

scholarly journals Tool and work piece vibrations measurement - a review

2018 ◽  
Vol 9 (4) ◽  
pp. 1254 ◽  
Author(s):  
Perunalla PBGSN Murthy ◽  
Ch Srinivasa Rao ◽  
K Venkata Rao
Keyword(s):  
Surface Roughness ◽  
Data Acquisition ◽  
Tool Life ◽  
Metal Cutting ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Work Piece ◽  
Feed Speed ◽  
Process Data

Tool condition monitoring is one of the important aspects in machining process to improve tool life. It comprises three important steps namely machining data acquisition, data analysis and decision making. Vibration in metal cutting has direct impact on the tool life as well as surface roughness. The present study focused on measurement of vibration during the machining process. Data acquisition is made by using various types of sensors. A wide variety of technologies like contact and non contact sensors have been used for real time data acquisition of tool or work piece vibrations. Research works carried out by many authors is highlighted in measurement of cutting tool and machine tool vibrations using different sensors. Influence of various input parameters like tool geometry, feed, speed and depth of cut on the magnitude of vibrations is discussed. Influence of vibration on surface roughness, tool life and power consumption is reviewed. Three dimensional vibration measurement with single Laser Doppler Vibrometer is also covered for precise analysis of vibration.

scholarly journals Optimization of process parameters in turning of nuclear graded steel alloy (AISI-410) for sustainable manufacture

2021 ◽  
Vol 9 ◽  
Author(s):  
Anis Fatima ◽  
◽  
Muhammad Wasif ◽  
Muhammad Omer Mumtaz ◽  
◽  
...  
Keyword(s):  
Surface Roughness ◽  
Statistical Model ◽  
Surface Finish ◽  
Metal Cutting ◽  
Functional Performance ◽  
Cutting Speed ◽  
Dimensional Accuracy ◽  
Process Parameter ◽  
Depth Of Cut ◽  
Work Piece

Metal cutting operations involve intense heat generation owing to plastic deformation of the work piece and due to friction at the tool-work piece and tool-chip interface. The heat generated in metal cutting unfavourably affects the quality and thus the functional performance of the product. It is known that quality and functional performance is the function of roughness and dimensional accuracy. To maintain a longer component life, along with the robust material choice, a component should have good surface finish and dimensional accuracy. While, for the organization to monitor and control their environmental issues in a holistic manner, emphasis in adopting eco-friendly practices and protecting environment has been growing continuously across all the business sectors. In this study, an attempt is made to optimize the process parameter of stainless steel AISI-410 alloy, a nuclear graded material, for better surface finish. For this, Taguchi L9 orthogonal array was utilise to identify the process parameter and cutting environment. Analysis of variance (ANOVA) was also conducted to highlight the significant parameter that affects the surface finish most. A statistical model to forecast the surface roughness was also developed and was validated by an experiment with a maximum error of 12%. Results indicates that feed rate is the most critical factor that effects the surface roughness with the contribution of 91.5%, followed by environment with 5.22% contribution, cutting speed and depth of cut with 2.7 % and 0.4 % respectively. The correlation coefficient of 0.9213 and conformation tests reveals that developed statistical model predicts surface roughness with the statistical error limit.

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