Optimization of Process Parameters in Plasma arc Cutting of EN 31 Steel Based on MRR and Multiple Roughness Characteristics Using Grey Relational Analysis

Das, Milan Kumar; Kumar, K. Ashok; Barman, Tapan Kumar; Sahoo, Prasanta

doi:10.1016/j.mspro.2014.07.342

Cited by 42 publications

(7 citation statements)

References 6 publications

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“…In order to satisfy these requirements, the corresponding NTM process, work material, shape feature and sub-shape feature are accordingly selected in Figure 12. For the PAM process, based on an extensive survey of the existing literature (Xu et al, 2002;Das et al, 2014;Adalarasan et al, 2015;Ramakrishnan et al, 2018), arc voltage, cutting current, cutting speed, feed rate, torch stand-off distance, plasma gas pressure and pierce height are identified as the predominant control parameters influencing its machining performance. On the other hand, the important responses are shortlisted as conicity, chamfer, dross, heat affected zone (HAZ), kerf width, MRR and SR. Now, in Figure 13, the end user preselects arc voltage, cutting current, feed rate and torch stand-off distance as the available PAM process parameters, and chamfer, dross, kerf width and SR as the desired responses.…”

Section: Example 3: Plasma Arc Machiningmentioning

confidence: 99%

Development of an intelligent decision model for non-traditional machining processes

Chakraborty

Kumar

2021

Decis. Mak. Appl. Manag. Eng.

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In order to fulfil the ever increasing requirements of various hard and difficult-to-machine materials in automobile, turbine, nuclear, aviation, tool and die making industries, the conventional material removal processes are now being continuously substituted by an array of non-traditional machining (NTM) processes. The efficient and improved capabilities of these NTM processes have made them indispensible for the present day manufacturing industries. While deploying a particular NTM process for a specific machining application, the concerned process engineer must be aware of its capability which is influenced by a large number of controllable parameters. In this paper, an intelligent decision model is designed and developed in VBASIC to guide the concerned process engineer to have an idea about the values of various NTM process responses for a given parametric combination. It would also advise about the tentative settings of different NTM process parameters for achieving a set of target response values. The operational procedure of this developed system is demonstrated with the help of three real time examples.

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Section: Example 3: Plasma Arc Machiningmentioning

confidence: 99%

Development of an intelligent decision model for non-traditional machining processes

Chakraborty

Kumar

2021

Decis. Mak. Appl. Manag. Eng.

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“…Moreover, the product designed with this goal will deliver more consistent performance regardless of the environment in which it is used. Yang et al (2001), Bini et al (2008), Radovanovic et al (2011), Mis et al (2011), Bhuvenesh et al (2012, Salonitis et al (2012), Das et al (2014), Prajapati (2015 and Kechagias et al (2017) are conducted this technique to their study.…”

Section: Mrr= (Weight Diff/density)/cutting Timementioning

confidence: 99%

Developments in Plasma Arc Cutting (PAC) of Steel Alloys: A Review

Asmael¹,

Cinar²,

Zeeshan³

2018

JKUKM

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“…Yamaguchi et al [9] performed a physical investigation on the magnetic arc blow during plasma machining operations and proposed a magnetic shielding cap by experimenting on various nozzles materials. Milan et al [10] investigated PAC of EN 31 steel and showed that gas pressure has the major influence on the MRR characteristics. Siva Ramakrishna et al [11] used formulae for the calculation of MRR and validated with adequacy test.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigations on SS 304 alloy using plasma arc machining

Hema

Ganesan

2020

SN Appl. Sci.

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Plasma arc machining is a well-recognized unconventional machining process widely used to machine intricate part profiles for alloys which are difficult to machine. The surface roughness, material removal rate (MRR), and kerf ratio are predominant factors which influence the performance and quality of plasma cut surfaces. The present research focusses on the effect of plasma arc cutting parameters such as arc voltage, cutting speed, standoff distance, and plasma offset on the cut quality characteristics of SS 304 alloy machined using two different types of nozzles (130 A and 200 A). The experiments were conducted according to a mixed Taguchi design of L18 orthogonal array, and grey relational analysis technique is used for optimization of the above-said cutting conditions. The experimentation on SS 304 alloy is carried out using two different nozzles and identified the best suited nozzle to cut SS 304 alloy of thickness 6 mm which produces better surface roughness and MRR characteristics. Scanning electron microscopy analysis is carried out to inspect the surface morphologies at various cutting conditions.

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Optimization of Process Parameters in Plasma arc Cutting of EN 31 Steel Based on MRR and Multiple Roughness Characteristics Using Grey Relational Analysis

Cited by 42 publications

References 6 publications

Development of an intelligent decision model for non-traditional machining processes

Development of an intelligent decision model for non-traditional machining processes

Developments in Plasma Arc Cutting (PAC) of Steel Alloys: A Review

Experimental investigations on SS 304 alloy using plasma arc machining

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