Finite Element Method and Experimental Investigation of Hot Turning of Inconel 718

Parida, Asit Kumar; Maity, Kalipada

doi:10.4028/www.scientific.net/aef.16.24

Cited by 13 publications

(3 citation statements)

References 10 publications

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“…Several investigations have been conducted by the researchers to study hot machining. Parida and Maity [9] investigated the machinability of several nickel-based alloys at elevated temperatures. The machinability in the hot condition was improved, comparing the machinability at room temperature.…”

Section: Fig 1 Turning Operation On the Lathementioning

confidence: 99%

Modeling and optimization of flank wear and surface roughness of Monel-400 during hot turning using artificial intelligence techniques

Hanief

Charoo

2020

Metall Mater Eng

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This work aims to model and investigate the effect of cutting speed, feed rate, depth of cut and the workpiece temperature on surface roughness and flank wear (responses) of Monel-400 during turning operation. It also aims to optimize the machining parameters of the above operation. A power-law model is developed for this purpose and is corroborated by comparing the results with the artificial neural network (ANN) model. Based on the coefficient of determination (R2), mean square error (MSE), and mean absolute percentage error (MAPE) the results of the power-law model are found to be in close agreement with that of ANN. Also, the proposed power law and ANN models for surface roughness and flank wear are in close agreement with the experiment results. For the power-law model R2, MSE, and MAPE were found to be 99.83%, 9.9×10-4, and 3.32×10-2, and that of ANN were found to be 99.91%, 5.4×10-4, and 5.96×10-2, respectively for surface roughness and flank wear. An error of 0.0642% (minimum) and 8.7346% (maximum) for surface roughness and 0.0261% (minimum) and 4.6073% (maximum) for flank wear were recorded between the observed and experimental results, respectively. In order to optimize the objective functions obtained from power-law models of the surface roughness and flank wear, GA (genetic algorithm) was used to determine the optimal values of the operating parameters and objective functions thereof. The optimal value of 2.1973 µm and 0.256 mm were found for surface roughness and flank wear, respectively.

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Section: Fig 1 Turning Operation On the Lathementioning

confidence: 99%

Modeling and optimization of flank wear and surface roughness of Monel-400 during hot turning using artificial intelligence techniques

Hanief

Charoo

2020

Metall Mater Eng

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“…Resistance heating with gas burners, oven heating with electric arcs, and other methods such as induction, radiation, and laser assistance can all be used [4]. Few researchers have proposed heating techniques for difficult to machine materials, namely, plasma assisting cutting [5], heating by induction [6], hardening by flame [7,8], laser heating [9], cryogenic machining [10], and Al3 Cr3 Sn alloy, hot ultrasonic method [11]. Sun et al [12] proposed a review of thermally enhanced machining processes focusing on laser and plasma theory for materials such as MMC and ceramics.…”

Section: Introductionmentioning

confidence: 99%

Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Chintakindi

Al-Samhan

Abidi

et al. 2022

Int J Comput Intell Syst

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The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (Ψ1 = 64.5), tool wear (Ψ2 = 22.3) and metal removal rate (Ψ3 = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 μm, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm3/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.

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“…Geleneksel delme ile karşılaştırıldığında, yüksek bir sıcaklıkta tork ve itme kuvvetinde azalma gözlemlenmiştir (Muhammad, Ahmed, Shariff, & Silberschmidt, 2012). Talaşlı imalatta sonlu elemanlar yöntemi ile yapılan birçok çalışma bulunmaktadır (Alatrushi, Bedir, & Yılmaz, 2020;Karagüzel, 2019;A. Parida & Maity, 2016;A.…”

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Ti–6al–4v Sicak İşlenmesi̇ Üzeri̇ne Etki̇leri̇ni̇n Sonlu Elemanlar Yöntemi̇ İle İncelenmesi̇

UĞUR

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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Heat assisted machining, known as hot machining, is an alternative machining method to increase the machinability of hard-to-cut metals and alloys. This study includes the investigation of cutting forces in hot working of Ti-6Al-4V alloy, which is widely used in the industry, by finite element method. Processing experiments were performed with ThirdWave AdvantEdge, the finite element analysis software. Analyzes were determined at constant depth of cut, cutting speed (V), feed rate (f), and temperature parameters were determined as three levels. Experiment list was created with Taguchi L9 orthogonal array. Cutting forces values were recorded according to the L9 experimental design. According to the results of the numerical analysis, it was observed that the cutting forces decreased when the room temperature conditions were compared with the hot machining conditions. The lowest cutting force value was measured in numerical analyzes carried out at 600°C hot machining conditions.

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Finite Element Method and Experimental Investigation of Hot Turning of Inconel 718

Cited by 13 publications

References 10 publications

Modeling and optimization of flank wear and surface roughness of Monel-400 during hot turning using artificial intelligence techniques

Modeling and optimization of flank wear and surface roughness of Monel-400 during hot turning using artificial intelligence techniques

Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Ti–6al–4v Sicak İşlenmesi̇ Üzeri̇ne Etki̇leri̇ni̇n Sonlu Elemanlar Yöntemi̇ İle İncelenmesi̇

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