Modelling of Kerf Width in Plasma Jet Metal Cutting Process using ANN Approach

Peko, Ivan; Nedić, Bogdan; Đorđević, Aleksandar; Veža, Ivica

doi:10.17559/tv-20161024093323

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Madic and Radovanovic [49] exposed that ANN outperforms regression models in assessing surface quality in mild steel cutting using a CO 2 laser. Peko et al [50] introduced a model for estimating kerf width with input parameters (cutting height, cutting speed, and arc current) in the plasma jet metal cutting process using a feedforward backpropagation ANN.…”

Section: Ann Studiesmentioning

confidence: 99%

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Ürgün,

Yiğit,

Fidan

et al. 2024

Arab J Sci Eng

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This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth.

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Section: Ann Studiesmentioning

confidence: 99%

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Ürgün,

Yiğit,

Fidan

et al. 2024

Arab J Sci Eng

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“…The ANN represents an artificial system based on mathematical models which is, in its structure, function and information processing, similar to the biological nervous systems and thus able to intelligently process the information simulating the biological intelligence [2]. ANNs are used for the purpose of diagnostics of the condition of the shafts on turbines and even within the monitoring [3] but in conditions where there is no continuous diagnostics or when it is not possible in an adequate manner, this limiting factor in the application of such models is impossible.…”

Section: Literature Reviewmentioning

confidence: 99%

MLP ANN Condition Assessment Model of the Turbogenerator Shaft A6 HPP Đerdap 2

Ilic¹,

Milošević

Jovanović³

et al. 2021

Teh. vjesn.

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This paper describes a model for estimating the condition of the shafts of turbines of the current generator in Hydropower plant Đerdap 2. For this purpose, an integral diagnostic approach was used. Based on the diagnostics of the condition of the shaft and the estimated lifetime, a multi-layer perceptron (MLP) based artificial neural network (ANN) is built, which is able to estimate the remaining lifespan of the turbine shaft. The MLP ANN model has not been made in this way on turbogenerators of hydroelectric power plant Đerdap 2 until now. The significance of this approach is that experiment brings about topology of ML ANN (number of neurons and layers) which is optimal for this model, training and testing. Results obtained from the neural network can be further used for decision-making about the moment of diagnosis or maintenance actions, as well as reducing stagnation and production losses.

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“…Namun peningkatan arus juga menyebabkan hasil potong menjadi lebih tidak beraturan karena turbulensi yang lebih besar pada daerah pelelehan atau daerah potong menyebabkan puncak dan lembah yang lebih besar. Penelitian ini diperkuat oleh penelitian yang dilakukan oleh Hamid, (2014) Peko, et al (2018) mengenai modelling of kerf width in plasma jet metal cutting process using ANN approach, dimana hasil penelitian tersebut menunjukkan perbedaan lebar pemotongan akibat parameter pemotongan (ketinggian potong, kecepatan pemotongan, dan kuat arus).…”

Section: Metodeunclassified

Pengaruh Variasi Kuat Arus Terhadap Lebar Pemotongan dan Kekerasan pada Baja Karbon Sedang dengan CNC Plasma Arc Cutting

Agnitias

Rusiyanto

2019

J. Din. Vok. Tek. Mesin

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The purpose of this research is to determine the effects of current variation to the cutting kerf and hardness in medium carbon steel cutting process using CNC Plasma Arc Cutting. This research uses experimental methods to study the effect of a given treatment. The electric current was varied for 20, 25, 30, 35, and 40 amps. The test parameters were cutting kerf and hardness. The data were analyzed using descriptive statistics. The test result shows that there are differences in cutting kerf and hardness. The lowest kerf and hardness value was obtained from the 20 amps current, with a kerf of 1.64 mm and a hardness of 707.4 HV. The highest kerf and hardness value were obtained from the 40 amps current test, with a kerf of 2.58 mm and a hardness of 857.7 HV. This research concludes that the variation of electric current in CNC Plasma Arc Cutting process of medium carbon steel affects the kerf (99.59%) and hardness (94.17%). Tujuan penelitian ini adalah untuk mengetahui seberapa besar variasi kuat arus memberikan pengaruh terhadap lebar pemotongan (Kerf Width) dan kekerasan pada proses pemotongan baja karbon sedang dengan menggunakan CNC Plasma Arc Cutting. Metode yang digunakan dalam penelitian ini adalah metode eksperimen dengan tujuan untuk mengetahui pengaruh sebab akibat terhadap perlakuan yang diberikan. Variasi kuat arus yang digunakan dalam proses pemotongan ini adalah 20 A, 25A, 30A, 35A, dan 40A. Pengujian yang di lakukan adalah kekerasan dan lebar pemotongan (Kerf Width). Data yang diperoleh kemudian dianalisis menggunakan teknik analisis statistik diskriptif. Hasil penelitian yang diperoleh menunjukkan bahwa terjadi perubahan nilai lebar pemotongan (Kerf Width) dan nilai kekerasan. Nilai lebar pemotongan (Kerf Width) dan kekerasan terendah diperoleh pada penggunaan arus sebesar 20 A yaitu dengan nilai lebar sebesar 1,64 mm dan kekerasan sebesar 707,4 HV. Nilai lebar pemotongan (Kerf Width) dan kekerasan tertinggi diperoleh pada penggunaan arus sebesar 40A dengan nilai lebar 2,58 mm dan kekerasan sebesar 857,7 HV. Penelitian ini dapat disimpulakan bahwa variasi kuat arus pada proses pemotongan baja karbon sedang menggunakan CNC Plasma Arc Cutting memberi pengaruh sebesar 99,59% terhadap nilai lebar pemotongan (Kerf Width) dan sebesar 94,17% terhadap nilai kekerasan.

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Modelling of Kerf Width in Plasma Jet Metal Cutting Process using ANN Approach

Cited by 11 publications

References 10 publications

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

MLP ANN Condition Assessment Model of the Turbogenerator Shaft A6 HPP Đerdap 2

Pengaruh Variasi Kuat Arus Terhadap Lebar Pemotongan dan Kekerasan pada Baja Karbon Sedang dengan CNC Plasma Arc Cutting

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