scholarly journals MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3828 ◽  
Author(s):  
Nelson Wilson Paschoalinoto ◽  
Gilmar Ferreira Batalha ◽  
Ed Claudio Bordinassi ◽  
Jorge Antonio Giles Ferrer ◽  
Aderval Ferreira de Lima Filho ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Design ◽  
Feed Rate ◽  
Minimum Quantity Lubrication ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Minimum Quantity ◽  
Dependent Variables ◽  
Artificial Neural

This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.

scholarly journals Study of a Multicriterion Decision-Making Approach to the MQL Turning of AISI 304 Steel Using Hybrid Nanocutting Fluid

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7207
Author(s):  
Vineet Dubey ◽  
Anuj Kumar Sharma ◽  
Prameet Vats ◽  
Danil Yurievich Pimenov ◽  
Khaled Giasin ◽  
...  
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Feed Rate ◽  
Minimum Quantity Lubrication ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Aisi 304 ◽  
Minimum Quantity ◽  
Aisi 304 Steel ◽  
Multicriterion Decision Making

The enormous use of cutting fluid in machining leads to an increase in machining costs, along with different health hazards. Cutting fluid can be used efficiently using the MQL (minimum quantity lubrication) method, which aids in improving the machining performance. This paper contains multiple responses, namely, force, surface roughness, and temperature, so there arises a need for a multicriteria optimization technique. Therefore, in this paper, multiobjective optimization based on ratio analysis (MOORA), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and technique for order of preference by similarity to ideal solution (TOPSIS) are used to solve different multiobjective problems, and response surface methodology is also used for optimization and to validate the results obtained by multicriterion decision-making technique (MCDM) techniques. The design of the experiment is based on the Box–Behnken technique, which used four input parameters: feed rate, depth of cut, cutting speed, and nanofluid concentration, respectively. The experiments were performed on AISI 304 steel in turning with minimum quantity lubrication (MQL) and found that the use of hybrid nanofluid (Alumina–Graphene) reduces response parameters by approximately 13% in forces, 31% in surface roughness, and 14% in temperature, as compared to Alumina nanofluid. The response parameters are analyzed using analysis of variance (ANOVA), where the depth of cut and feed rate showed a major impact on response parameters. After using all three MCDM techniques, it was found that, at fixed weight factor with each MCDM technique, a similar process parameter was achieved (velocity of 90 m/min, feed of 0.08 mm/min, depth of cut of 0.6 mm, and nanoparticle concentration of 1.5%, respectively) for optimum response. The above stated multicriterion techniques employed in this work aid decision makers in selecting optimum parameters depending upon the desired targets. Thus, this work is a novel approach to studying the effectiveness of hybrid nanofluids in the machining of AISI 304 steel using MCDM techniques.

scholarly journals Nanofluid-based Minimum Quantity Lubrication (MQL) Face Milling of Inconel 625

2019 ◽  
Vol 16 (3) ◽  
pp. 6874-6888
Author(s):  
P. Singh ◽  
J. S. Dureja ◽  
H. Singh ◽  
M. S. Bhatti
Keyword(s):  
Negative Impact ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Face Milling ◽  
Cutting Fluid ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Base Oil ◽  
Minimum Quantity ◽  
Milling Parameters

Machining with minimum quantity lubrication (MQL) has gained widespread attention to boost machining performance of difficult to machine materials such as Ni-Cr alloys, especially to reduce the negative impact of conventional flooded machining on environment and machine operator health. The present study is aimed to evaluate MQL face milling performance of Inconel 625 using nano cutting fluid based on vegetable oil mixed with multi-walled carbon nanotubes (MWCNT). Experiments were designed with 2-level factorial design methodology. ANOVA test and desirability optimisation method were employed to arrive at optimised milling parameters to achieve minimum tool wear and machined surface quality. Experiments were performed under nanoparticles based minimum quantity lubrication (NMQL) conditions using different weight concentrations of MWCNT in base oil: 0.50, 0.75, 1, 1.25 and 1.5 wt. %; and pure MQL environment (without nanoparticles). The optimal MQL milling parameters found are cutting speed: 47 m/min, table feed rate: 0.05 mm/tooth and depth of cut: 0.20 mm. The results revealed improvement in the surface finish (Ra) by 17.33% and reduction in tool flank wear (VB) by 11.48 % under NMQL face milling of Inconel 625 with 1% weight concentration of MWCNT in base oil compared to pure MQL machining conditions.

Experimental Investigation on Turning of Monel K500 Alloy Using Nano Graphene Cutting Fluid Under Minimum Quantity Lubrication

2019 ◽  
Author(s):  
Arul Kulandaivel ◽  
Senthil Kumar Santhanam
Keyword(s):  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
High Strength ◽  
High Heat ◽  
Water Soluble ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Machining Processes ◽  
Turning Operation ◽  
Minimum Quantity

Abstract Turning operation is one of the most commonly used machining processes. However, turning of high strength materials involves high heat generation which, in turn, results in undesirable characteristics such as increased tool wear, irregular chip formation, minor variations in physical properties etc. In order to overcome these, synthetic coolants are used and supplied in excess quantities (flood type). The handling and disposal of excess coolants are tedious and relatively expensive. In this proposed work, Water Soluble Cutting Oil suspended with nanoparticles (Graphene) is used in comparatively less quantities using Minimum quantity lubrication (MQL) method to improve the quality of machining. The testing was done on Turning operation of Monel K500 considering the various parameters such as the cutting speed, feed and depth of cut for obtaining a surface roughness of 0.462μm and cutting tool temperature of 55°C for MQL-GO (Graphene oxide) process.

Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

2014 ◽  
Vol 592-594 ◽  
pp. 2733-2737 ◽  
Author(s):  
G. Harinath Gowd ◽  
K. Divya Theja ◽  
Peyyala Rayudu ◽  
M. Venugopal Goud ◽  
M .Subba Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Artificial Neural ◽  
End Milling Process

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

Sign in / Sign up

Export Citation Format

Share Document