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.

scholarly journals The Effect of Machining Parameters and Optimization of Temperature Rise in Turning Operation of Aluminium-6061 Using RSM and Artificial Neural Network

2021 ◽  
Author(s):  
Mahesh Gopal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
Cutting Speed ◽  
Tool Geometry ◽  
Machining Parameters ◽  
Ann Model ◽  
Turning Operation ◽  
Artificial Neural ◽  
Coated Carbide

The aim of this study is to determine the effect of the machining parameters and tool geometry. The turning operation is carried out as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise of aluminium-6061 as a cutting material and Al2O3 coated carbide tool is used as a cutting tool for turning operation. The ANOVA analysis is used to measure the performance quality and mathematical model is developed. The values of probability >(F) is less than 0.05 indicates, the model conditions are significant. The cutting speed is the most influencing parameters compared to other parameters. For the optimum machining parameters leading to temperature rise, the Artificial Neural Network (ANN) model is trained and tested using MAT Lab software. The ANN recommends best minimum predicted value of temperature rise. The confirmatory analysis results, the predicted values were found to be in commendable agreement with the experimental values.

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.

scholarly journals Surface Quality Assessment after Milling AZ91D Magnesium Alloy Using PCD Tool

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 617 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Jarosław Korpysa
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
High Speed ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Roughness Parameters ◽  
Operational Parameters

Surface roughness is among the key indicators describing the quality of machined surfaces. Although it is an aggregate of several factors, the condition of the surface is largely determined by the type of tool and the operational parameters of machining. This study sought to examine the effect that particular machining parameters have on the quality of the surface. The investigated operation was the high-speed dry milling of a magnesium alloy with a polycrystalline diamond (PCD) cutting tool dedicated for light metal applications. Magnesium alloys have low density, and thus are commonly used in the aerospace or automotive industries. The state of the Mg surfaces was assessed using the 2D surface roughness parameters, measured on the lateral and the end face of the specimens, and the end-face 3D area roughness parameters. The description of the surfaces was complemented with the surface topography maps and the Abbott–Firestone curves of the specimens. Most 2D roughness parameters were to a limited extent affected by the changes in the cutting speed and the axial depth of cut, therefore, the results from the measurements were subjected to statistical analysis. From the data comparison, it emerged that PCD-tipped tools are resilient to changes in the cutting parameters and produce a high-quality surface finish.

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 Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approach

2020 ◽  
Vol 65 (1) ◽  
pp. 10-26
Author(s):  
Septi Boucherit ◽  
Sofiane Berkani ◽  
Mohamed Athmane Yallese ◽  
Riad Khettabi ◽  
Tarek Mabrouki
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Cutting Force ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Desirability Approach ◽  
Optimization Of Cutting Parameters ◽  
Coated Carbide

In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).

scholarly journals Prediction of Cutting Force in Turning Process by Using Artificial Neural Network

2020 ◽  
Vol 16 (2) ◽  
pp. 34-46
Author(s):  
Marwa Qasim Ibraheem
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Operation ◽  
Artificial Neural

        Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.

scholarly journals An Investigation on Optimum Process Parameters in Terms of Surface Roughness for Turning Titanium Alloy Ti-6Al-4V Using Coated Carbide

2019 ◽  
Vol 4 (4) ◽  
pp. 137-145
Author(s):  
Abdul Md Mazid ◽  
Md. Shahanur Hasan ◽  
Kazi Badrul Ahsan
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Real Life ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Ti Alloys ◽  
Machined Parts ◽  
Coated Carbide ◽  
Coated Carbide Tools

The quality of machined parts and the productivity of machining that leads to economic sustainability.  These factors are also vital for machinability improvement for materials, as well as, for economically sustainable manufacturing. Due to their poor machinability titanium alloys (Ti-alloys) are categorised as difficult-to-machine materials. For the same reason products made of Ti-alloys are highly expensive and are used only in strategic and sophisticated industries.  A series of real-life experimental investigations was carried out to reveal the economic optimal zones of machining parameters that can produce the best possible surface roughness in machining Ti-6Al-4V alloy, using the coated carbide cutting tools, in shortest period of operation time. As the output of the research, for using the coated carbide tools for machining the investigated Ti-alloy, optimal zones of cutting speed, feed rate and depth of cut have been proposed and presented in graphical format. The current research revealed that all three groups (with nose radius Nr = 0.4, 0.8, and 1.2 mm) of coated carbide tools are capable to produce best surface finish, ranging between Ra = 0.5 - 1.0 µm, with cutting speed starting at V = 60 m/min and beyond at least up to V = 250 m/min while keeping the feed rate and depth of cut as constants as f = 0.1 mm/rev and d = 0.5 mm. The data on the graphs may help researchers, engineers and manufacturers to select optimal economic cutting speed, feed rate and depth of cut to achieve a certain level of surface roughness of machined components as assigned by the product designer on the part drawing. This reduces the production cost substantially, reduces number of defect products and improves product quality for machined parts.

Preheating in End Milling of AISI D2 Hardened Steel with Coated Carbide Inserts

2009 ◽  
Vol 83-86 ◽  
pp. 56-66 ◽  
Author(s):  
Mohd Amri Lajis ◽  
A.K.M. Nurul Amin ◽  
A.N. Mustafizul Karim ◽  
A.M.K. Hafiz
Keyword(s):  
Tool Wear ◽  
Surface Finish ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Inductive Heating ◽  
Machining Parameters ◽  
Aisi D2 ◽  
Coated Carbide

This study was conducted to investigate the effect of preheating through inductive heating mechanism in end milling of AISI D2 hardened steel (60-62 HRC) by using coated carbide tool inserts. Apart from preheating, two other machining parameters such as cutting speed and feed were varied while the depth of cut constant was kept constant. Tool wear phenomenon and machined surface finish were found to be significantly affected by preheating temperature and other two variables. End milling operation was performed on a Vertical Machining Centre (VMC). Preheating of the work material to a higher temperature range resulted in a noticeable reduction in tool wear rate leading to a longer tool life. In addition, improved surface finish was obtained with surface roughness values lower than 0.4 μm, leaving a possibility of skipping the grinding and polishing operations for certain applications.

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. 

scholarly journals Content Comparative Investigation on Tool Wear During End Milling of AISI H13 Steel with Different Tool Path Strategies

2017 ◽  
Vol 6 (4) ◽  
pp. 327-333
Author(s):  
Erry Yulian T. Adesta ◽  
Muhammad Riza ◽  
Avicenna Avicenna
Keyword(s):  
Tool Wear ◽  
Experimental Work ◽  
Tool Path ◽  
End Milling ◽  
Cutting Speed ◽  
Comparative Investigation ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
H13 Steel ◽  
Coated Carbide

Tool wear prediction plays a significant role in machining industry for proper planning and control machining parameters and optimization of cutting conditions. This paper aims to investigate the effect of tool path strategies that are contour-in and zigzag tool path strategies applied on tool wear during pocket milling process. The experiments were carried out on CNC vertical machining centre by involving PVD coated carbide inserts. Cutting speed, feed rate and depth of cut were set to vary. In an experiment with three factors at three levels, Response Surface Method (RSM) design of experiment with a standard called Central Composite Design (CCD) was employed. Results obtained indicate that tool wear increases significantly at higher range of feed per tooth compared to cutting speed and depth of cut. This result of this experimental work is then proven statistically by developing empirical model. The prediction model for the response variable of tool wear for contour-in strategy developed in this research shows a good agreement with experimental work.

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