scholarly journals Hard Turning on JIS S45C Structural Steel: An Experimental, Modelling and Optimisation Approach

2019 ◽  
Vol 16 (4) ◽  
pp. 7315-7340 ◽  
Author(s):  
R. Kumar ◽  
A. Modi ◽  
A. Panda ◽  
A. K. Sahoo ◽  
A. Deep ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Steel ◽  
Network Architecture ◽  
Cutting Speed ◽  
Process Efficiency ◽  
Depth Of Cut ◽  
Experimental Modelling ◽  
Architecture Model ◽  
Coated Carbide ◽  
Level 2

The present research is performed while turning of JIS S45C hardened structural steel with the multilayered (TiN-TiCN-Al2O3-TiN) CVD coated carbide insert by experimental, modelling and optimisation approach. Herein, cutting speed, feed rate, and depth of cut are regarded as input process factors whereas flank wear, surface roughness, chip morphology are considered to be measured responses. Abrasion and built up-edge are the more dominant mode of tool-wear at low and moderate cutting speed while the catastrophic failure of tool-tip is identified at higher cutting speed condition. Moreover, three different Modelling approaches namely regression, BNN, and RNN are implemented to predict the response variables. A Back-propagation neural network with a 3-8-1 network architecture model is more appropriate to predict the measured output responses compared to Elman recurrent neural network and regression model. The minimum mean absolute error for VBc, Ra and CRC is observed to be as 1.36% (BNN with 3- 8-1 structure), 1.11% (BNN with 3-8-1 structure) and 0.251 % (RNN with 3-8-1 structure). A multi-performance Optimisation approach is performed by employing the weighted principal component analysis. The optimal parametric combination is found as the depth of cut at level 2 (0.3 mm)-feed at level 1 (0.05 mm/rev) – cutting speed at level 2 (120 m/min) considered as favourable outcomes. The predicted results were validated through a confirmatory trial providing the process efficiency. The significant improvement for S/N ratio of CQL is observed to be 9.3586 indicating that the process is well suited to predict the machining performances. In conclusion, this analysis opens an avenue in the machining of medium carbon low alloy steel to enhance the machining performance of multi-layered coated carbide tool more effectively and efficiently.

scholarly journals Application of neural network in determination of parameters for milling AZ91HP magnesium alloy with surface roughness constraint

2019 ◽  
Vol 252 ◽  
pp. 03017 ◽  
Author(s):  
Monika Kulisz ◽  
Ireneusz Zagórski
Keyword(s):  
Neural Network ◽  
Magnesium Alloy ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Maximum Efficiency ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Data Set ◽  
Coated Carbide

This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill. The model was developed on the basis of experimental data from the neural network training data set. The milling process was conducted at constant parameters of tool geometry, workpiece strength properties, technological machine properties, radial and axial depth of cut. The range of changeable machining parameters specified in this study included cutting speed, feed per tooth, and the output variable: the arithmetical mean roughness parameter (Ra). The process was modelled by means of MatLab software and its Neural Network Toolbox. The developed model was implemented in the algorithm designed to determine optimal milling conditions, exploring the space of acceptable parameters in search of those which would meet the specified roughness parameter at maximum efficiency.

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

The Effects of Cutting Parameters on Work-Hardening of Milling Invar 36

2015 ◽  
Vol 1089 ◽  
pp. 373-376
Author(s):  
Xing Wei Zheng ◽  
Guo Fu Ying ◽  
Yan Chen ◽  
Yu Can Fu
Keyword(s):  
Work Hardening ◽  
Lattice Distortion ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Metallographic Structure ◽  
Metallographic Observation ◽  
Coated Carbide ◽  
Carbide Insert ◽  
Axial Depth

An experiment of face milling of Invar36 was conducted by using coated carbide insert, the microhardness was tested and the metallographic structure was observed to figure out the principles of work-hardening. The results showed that the depth of work-hardening ranges from 80μm to 160μm among the parameters selected in the experiments. The degree and the depth of work-hardening were significantly affected by the axial depth of cut and feed per tooth. The degree and the depth of work-hardening showed a tendency to increase with the increase of the axial depth of cut and feed per tooth. Compared with the axial depth of cut and feed per tooth, cutting speed had less influence on the degree and depth of work-hardening. The degree and depth of work- hardening decreased slowly with the increase of cutting speed. Metallographic observation showed that work-hardening layer consisted of the thermal force influenced layer and the force influenced layer, while the amorphous metallographic structure was observed in the thermal force influenced layer, and lattice distortion was observed in the force influenced layer.

scholarly journals Investigation of AlCrN-Coated Inserts on Cryogenic Turning of Ti-6Al-4V Alloy

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1338
Author(s):  
Lakshmanan Selvam ◽  
Pradeep Kumar Murugesan ◽  
Dhananchezian Mani ◽  
Yuvaraj Natarajan
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Physical Vapor Deposition ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machined Surface ◽  
Coated Carbide ◽  
Cryogenic Conditions

Over the past decade, the focus of the metal cutting industry has been on the improvement of tool life for achieving higher productivity and better finish. Researchers are attempting to reduce tool failure in several ways such as modified coating characteristics of a cutting tool, conventional coolant, cryogenic coolant, and cryogenic treated insert. In this study, a single layer coating was made on cutting carbide inserts with newly determined thickness. Coating thickness, presence of coating materials, and coated insert hardness were observed. This investigation also dealt with the effect of machining parameters on the cutting force, surface finish, and tool wear when turning Ti-6Al-4V alloy without coating and Physical Vapor Deposition (PVD)-AlCrN coated carbide cutting inserts under cryogenic conditions. The experimental results showed that AlCrN-based coated tools with cryogenic conditions developed reduced tool wear and surface roughness on the machined surface, and cutting force reductions were observed when a comparison was made with the uncoated carbide insert. The best optimal parameters of a cutting speed (Vc) of 215 m/min, feed rate (f) of 0.102 mm/rev, and depth of cut (doc) of 0.5 mm are recommended for turning titanium alloy using the multi-response TOPSIS technique.

Cutting Forces and Surface Roughness in High-Speed End Milling of Ti6Al4V

2013 ◽  
Vol 589-590 ◽  
pp. 76-81
Author(s):  
Fu Zeng Wang ◽  
Jun Zhao ◽  
An Hai Li ◽  
Jia Bang Zhao
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Cutting Forces ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Wide Range ◽  
Radial Depth ◽  
Coated Carbide

In this paper, high speed milling experiments on Ti6Al4V were conducted with coated carbide inserts under a wide range of cutting conditions. The effects of cutting speed, feed rate and radial depth of cut on the cutting forces, chip morphologies as well as surface roughness were investigated. The results indicated that the cutting speed 200m/min could be considered as a critical value at which both relatively low cutting forces and good surface quality can be obtained at the same time. When the cutting speed exceeds 200m/min, the cutting forces increase rapidly and the surface quality degrades. There exist obvious correlations between cutting forces and surface roughness.

Optimization of Process Parameters in the Turning Operation of Inconel 625

2019 ◽  
Vol 969 ◽  
pp. 756-761
Author(s):  
Hari Vasudevan ◽  
Ramesh Rajguru ◽  
Moeiz Shaikh ◽  
Arsalan Shaikh
Keyword(s):  
Feed Rate ◽  
Cutting Speed ◽  
Influential Factor ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Turning Operation ◽  
Wide Range ◽  
Coated Carbide ◽  
Taguchi’S Design Of Experiments ◽  
Super Alloy

Many difficult to machine materials, such as Inconel 625Ni-based super alloy, are uncommon class of metallic materials with exceptional combination of greater thermal strength, toughness and resistance to deterioration. They have extensive applications in the manufacturing of new aero-engines, besides its enormous uses in marine, chemical and oil & petrochemical industries. In the context of its wide range of applications, there is a need for efficiently processing better methods in the manufacturing of such difficult to machine materials. This study consists of the turning operation of Ni-based super alloy Inconel 625 without coolant, carried out by physical vapour deposition (PVD) coated carbide inserts. The response parameters, such as surface roughness and material removal rate were evaluated in terms of cutting speed, feed rate and depth of cut. Sixteen experiments were carried out, based on Taguchi's Design of Experiments using orthogonal array. The resulting analysis was done based on response graph. The experimental results revealed that the feed rate was the most influential factor, followed by the depth of cut and cutting speed. The optimal parameters achieved were cutting speed of 90 m/min, the feed rate of 0.35 mm/rev and the depth of cut 0.2 mm.

Modeling & Optimization of Surface Roughness & Vibration Amplitude in Heat Assisted End Milling of SKD 11 Tool Steel Using Ball Nose Tool

2012 ◽  
Vol 538-541 ◽  
pp. 799-803 ◽  
Author(s):  
A.K.M. Nurul Amin ◽  
Muhd Hafiz B. Md. Saad ◽  
Muammer Din Arif
Keyword(s):  
Surface Roughness ◽  
Response Surface ◽  
Tool Steel ◽  
Vibration Amplitude ◽  
End Milling ◽  
Heating Temperature ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Primary Input ◽  
Coated Carbide

Tool steel - SKD 11 is frequently used in industries for making dies and molds. This grade is chosen for its toughness, strength, and hardness maintained up to high temperature. However, the same properties make the steel extremely difficult and expensive to machine using conventional approaches. Heat assisted machining has been found wide spread application in recent years to improve machinability of difficult-to-cut materials. This research paper presents the outcome of an investigation on heat assisted end milling of SKD 11 conducted on a vertical machining center using ball nose coated carbide inserts. The Design of Experiments (DoE) was done using the Response Surface Methodology, in order to develop empirical mathematical models of surface roughness and vibration in terms of cutting speed, feed, axial depth of cut, and heating temperature. The models were checked for significance using Analysis of Variance (ANOVA). 3-D response surface graphs of the interactions of primary cutting parameters with the responses were plotted. Optimization was then performed by using the desirability function approach. From the graphs and optimized results it was concluded that the primary input parameters could be controlled in order to reduce vibration amplitude and produce semi-finished machined surfaces applying induction heat assisted technique.

scholarly journals Optimisation of the Machining of Stellite 6 PTA Hardfacing Using Surface Roughness

2010 ◽  
Vol 443 ◽  
pp. 227-231 ◽  
Author(s):  
Md Shahanur Hasan ◽  
Md Mazid Abdul ◽  
Richard E. Clegg
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Deposition Process ◽  
Depth Of Cut ◽  
High Corrosion Resistance ◽  
Turning Operations ◽  
Stellite 6 ◽  
Cutting Regimes ◽  
Coated Carbide ◽  
Carbide Inserts

Stellites are cobalt based super alloys. By virtue of their excellent physio-mechanical properties, stellites are highly regarded engineering materials. Stellites posses high corrosion resistance and wear resistance properties. This study investigates the Stellite deposition process and machinability of Stellite 6 deposited on steel subtrate. Stellite 6 was deposited onto a 4140 bar using a plasma transfer arc (PTA) system and machinability was assessed on the basis of surface roughness. A series of turning operations have been carried out on a conventional lathe using coated carbide inserts and surface roughness was evaluated by Stylus type Surtronic3+ instrument. The values of surface roughness were plotted against different cutting speed, feed rate and depth of cut to display the results in graphical forms. Optimal cutting regimes were established against the best values of surface roughness.

Investigation on Surface Roughness and Cutting Temperature in Turning AISI 316 Austenitic Stainless Steel Using TiAlSiN Coated Carbide Insert

2013 ◽  
Vol 446-447 ◽  
pp. 291-295
Author(s):  
Malhar Ozarkar ◽  
Rugwed Bhatkhande ◽  
Shray Jerath ◽  
A.P. Kulkarni
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Experimental Result ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Aisi 316 ◽  
Coated Carbide ◽  
Coated Cemented Carbide ◽  
Carbide Insert ◽  
Tialsin Coating

This study presents experimental result of surface roughness and chip-tool interface temperature developed during turning of AISI 316 austenitic stainless steels using TiAlSiN coated cemented carbide insert. TiAlSiN coating is deposited by Cathodic Arc Evaporation (PVD) technique. The work-tool thermocouple calibration set-up was developed. The air heater was used as a heating element at the work-tool junction. The experiments were conducted at cutting speeds in the range of 140 to 320 m/min, feed in the range of 0.08 to 0.26 mm/rev keeping depth of cut constant at 1 mm. The influence of cutting parameters and tool coating were investigated on the average chip-tool interface temperature and surface roughness. Experimentally interface temperature 979°C was observed at 260 m/min cutting speed and 0.14 mm/rev feed. The interface temperature in turning is strongly dependent on the cutting speed followed by feed and exactly reverse case was observed in case of surface roughness. TiAlSiN coating shows better performance and can be considered as a prominent candidate for the machining of AISI 316 work material.

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.

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