Experimental investigation and optimization of PAC parameters on Monel 400™ superalloy

Rajamani, D.; Ananthakumar, K.; Esakki, Balasubramanian; Davim, J. Paulo

doi:10.1080/10426914.2018.1532085

Cited by 33 publications

(14 citation statements)

References 26 publications

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“…Based on the fitness value of unemployed bees, the probability and cumulative probability are calculated using Equations (10) and (11). The onlooker bees are selected to look for new food sources using the roulette wheel selection method.…”

Section: Step 4 Determination Of New Position Of Employed Beesmentioning

confidence: 99%

“…The surface plots demonstrate that the mh with an increase in the parameters GP and SOD, from their lower to their highe The intensity of plasma expelled to the cutting zone will increase as the GP and crease. As a result of the increased plasma thrusts, the recast layer at the kerf surf progresses, as does the formation oxide layer at the cutting area; thus, the mh creases [11]. Therefore, a suitable value of GP and SOD should be found for mi the micro hardness of cut surfaces.…”

Section: Influence Of Pac Parameters On Selected Responsesmentioning

confidence: 99%

“…Among these non-traditional machining processes, plasma arc cutting (PAC) is a far-reaching thermal energy-based machining technique commonly used for processing diverse conductive materials to stringent design requirements and intricate geometrical profiles with manifest automation, higher cutting speed, and reasonable cutting cost [ 11 ]. Despite its potential benefits, the realization of attractive cutting features such as improving the removal of substrate material, refining the kerf characteristics, and enhancing the surface quality in PAC processed parts are tedious due to the existence of plentiful process related control factors [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

et al. 2021

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This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.

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Section: Step 4 Determination Of New Position Of Employed Beesmentioning

confidence: 99%

Section: Influence Of Pac Parameters On Selected Responsesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

et al. 2021

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“…Treatment for the combinations of points lies at the mid-point of the edges and centre of the cube. 47 The second-order polynomial relation evolved through RSM is used to express the behaviour of SIS process, which is given by…”

Section: Rsm-based Bbdmentioning

confidence: 99%

Prediction and analysis of surface roughness in selective inhibition sintered high-density polyethylene parts: A parametric approach using response surface methodology–grey relational analysis

Rajamani

Ziout

Esakki

et al. 2018

Advances in Mechanical Engineering

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Selective inhibition sintering (SIS) process intends to produce near-net-shape components through sintering of specific region of powder particles. The prediction of surface quality in SIS parts is a challenging task due to its complex part building mechanism and influence of abundant process parameters. Therefore, this study investigates the key contributing parameters such as layer thickness, heater energy, heater feedrate and printer feedrate on the surface quality characteristics (R a , R z and R q) of high-density polyethylene specimens fabricated through selective inhibition sintering process. The SIS system is custom built and experiments are conducted based on four-factor, three-level Box-Behnken design. The empirical models have been developed for predicting the influence of selected parameters on surface quality. The optimal process parameters such as the layer thickness of 0.1 mm, heater energy of 28.48 J/mm 2 , heater feedrate of 3.25 mm/s and printer feedrate of 110 mm/min are attained using grey relational multi-criteria decision-making approach. Furthermore, response surface analysis revealed that surface quality of sintered components is influenced significantly with heater energy and heater feedrate, followed by layer thickness. The confirmation experiments based on optimal process variables validate the developed grey relational analysis strategy.

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“…Earlier studies of the authors dealt with various optimization techniques to investigate the PAC process. Rajamani et al [16] investigated the influence of PAC parameters on kerf width, surface roughness, and microhardness of the machined Monel 400 alloy. They have proposed a statistical desirability approach for the optimization of PAC process parameters.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis

et al. 2020

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Nickel-based alloys, especially Monel 400™, is gaining its significance in diverse applications owing to its superior mechanical properties and high corrosion resistance. Machining of these materials is extremely difficult through the traditional manufacturing process because of their affinity to rapid work hardening and deprived thermal conductivity. Owing to these difficulties a well-established disruptive metal cutting process namely plasma arc cutting (PAC) can be widely used to cut the sheet metals with intricate profiles. The present work focuses on an intelligent modeling of the PAC process and investigation on the multi-quality characteristics of PAC parameters using the fuzzy logic approach. The Box-Behnken response surface methodology is incorporated to design and conduct the experiments, and to establish the relationship between PAC parameters such as cutting speed, gas pressure, arc current, and stand-off distance and responses which include the material removal rate (MRR), kerf taper (KT), and heat affected zone (HAZ). The quadratic regression models are developed and their performances are assessed using the analysis of variance (ANOVA). Fuzzy set theory-based models are formulated to predict various responses using the Mamdani approach. Fuzzy logic and regression results are compared with the experimental data. A comparative evaluation predicted an average error of 0.04% for MRR, 0.48% for KT, and 0.46% for HAZ, respectively. The effect of variations in PAC process parameters on selected responses are estimated through performing the sensitivity analysis.

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Experimental investigation and optimization of PAC parameters on Monel 400™ superalloy

Cited by 33 publications

References 26 publications

A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

Prediction and analysis of surface roughness in selective inhibition sintered high-density polyethylene parts: A parametric approach using response surface methodology–grey relational analysis

Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis

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