Optimisation and Evaluation of Machining Parameters for Turning Operation of Inconel-625

2015 ◽  
Vol 2 (4-5) ◽  
pp. 2306-2313 ◽  
Author(s):  
Hemant Jain ◽  
Jaya Tripathi ◽  
Ravindra Bharilya ◽  
Sanjay Jain ◽  
Avinash Kumar
2015 ◽  
Vol 1101 ◽  
pp. 393-396
Author(s):  
Mohammad Ahsan Habib ◽  
Md. Anayet U. Patwari ◽  
Koushik Alam Khan ◽  
A.N.M. Amanullah Tomal

For cost reduction and quality improvement of machining products, optimum output machining parameters such as material removal rate, tool wear ratio and surface roughness is very essential. Moreover, these output parameters are strongly depends on the precision of the machine tool as well as the input machining parameters. In this paper, a hybrid model of Artificial Bee Colony (ABC), which is motivated by the intelligent behavior of honey bees with Response Surface Methodology (RSM), has been developed for optimizing the surface roughness of stainless steel during turning operation. The predicted optimal value of surface roughness of stainless steel is further confirmed by conducting supplementary experiments. Finally, the performance of this algorithm is evaluated in comparison with desirability analysis. The performance of ABC is at par with that of desirability analysis for different parametric conditions.


2019 ◽  
Vol 11 (4) ◽  
pp. 107-121 ◽  
Author(s):  
Chinmaya PADHY ◽  
Pariniti SINGH

Minimum quantity lubrication (MQL) is currently a widely used lubricating technique during machining, in which minimum amount of lubricant in the form of mist is delivered to the machining interface, thus helps to reduce the negative effects caused to the environment and human health. Further, to enhance the productivity of machining process specifically for hard-to-cut materials, nano cutting fluid (suitably mixed nano materials with conventional cutting fluid) is used as an alternative method to conventional lubrication (wet) in MQL. In this study, h-BN nano cutting fluid was formulated with 0.1% vol. concentration of h-BN in conventional cutting fluid (Servo- ‘S’) for NCF-MQL technique and its tribological behaviors on machining(turning) performance of Inconel 625 were studied and compared with other lubricating conditions (dry, wet, MQL conventional). The tribological effects were analyzed in terms of tool wear analysis, chip morphology along with statistical analysis for machined surface and evolved cutting forces during machining. The optimal input machining parameters for experiments were defined by the use of Taguchi and Grey relational based multi response optimization technique. Finally, the tribological study shows that the use of h-BN NCF-MQL is a viable and sustainable option for improving machining performance of hard- to- cut material like Inconel 625.


2019 ◽  
Vol 969 ◽  
pp. 756-761
Author(s):  
Hari Vasudevan ◽  
Ramesh Rajguru ◽  
Moeiz Shaikh ◽  
Arsalan Shaikh

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.


2012 ◽  
Vol 217-219 ◽  
pp. 1501-1505
Author(s):  
Pongchanun Luangpaiboon

In this paper, an intelligent water drop algorithm or IWD has been developed to optimise machining parameters in turning operation including a spring force model. Firstly, machining conditions are to minimise the production cost in conventional manufacturing processes. Several passes of rough machining are started on the turning operation with a final pass of a finishing. Various constraints are considered in each non-linear and non-convex model. The machining parameters in the turning consist of the depth of cut, cutting speed and feed. Finally, in a specialised manufacturing application on the spring force problem, an achievement of a specific goal may be the primary objective subject to some process parameter ranges. The computational results clearly showed that the proposed sequential procedures of the IWD have considerably improved the objective functions.


2020 ◽  
Vol 4 (2) ◽  
pp. 32
Author(s):  
Jixiong Fei ◽  
Guoliang Liu ◽  
Kaushalendra Patel ◽  
Tuğrul Özel

Metal additive manufacturing processes such as selective laser melting (SLM), laser powder bed fusion (L-PBF), electron beam melting (EBM) and laser metal deposition (LMD) can produce additively manufactured nearly fully dense parts with high geometrical complexity. These are often used as components in automotive, aerospace and medical device industries. Finish machining of these components is required to achieve the desired surface finish and dimensional tolerances. The investigations on additively manufactured alloys, as reported in the literature, indicate that a layer-wise scan strategy (orthogonal or layer-to-layer rotation) and process parameters have significant influences on the resultant microstructure which affects the final mechanical properties and fatigue life. The solidification microstructure depicts that growth directions of columnar grains and sizes of cellular grains that are affected by the layer-wise scan strategy. This paper presents experimental investigations on finish milling parameters on a nickel-based alloy manufactured with L-PBF using two distinct layer-wise scan strategies. The results reveal some effects of milling direction against the layer-wise build direction. The effects of cutting speed and feed rate on resultant cutting forces, chip formation, as well as surface finish at various cutting orientations in nickel-based alloy workpieces are reported.


Fractals ◽  
2019 ◽  
Vol 27 (04) ◽  
pp. 1950043 ◽  
Author(s):  
GEEVIN JITHMAL PATHIRANAGAMA ◽  
HAMIDREZA NAMAZI

Analysis of workpiece surface quality is one of the major issues in manufacturing engineering. Turning operation is a famous machining operation that is widely used in machining of materials. In this research, we investigate the surface finish of machined workpiece from turning operation. For this purpose, we employ fractal theory to study the complex structure of machined workpiece’s surface in different conditions. The applied parameters include the variations of cutting depth, feed rate and spindle speed in wet and dry machining conditions. Based on the obtained results, we found the correlation between the increment of fractal dimension of machined surface and the increment of cutting depth, feed rate and spindle speed in wet machining condition. The obtained results will be discussed in relation with the complexity of machined surface. The employed method of analysis in this research can be widely applied to the analysis of the effect of different machining parameters and conditions on the surface quality of machined workpiece in case of different machining operations.


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