Optimization of Surface Roughness in Plasma Arc Cutting of AISID2 Steel Using TLBO

Patel, Parthkumar; Nakum, Bhavdeep; Abhishek, Kumar; Kumar, Vivek; Kumar, Anshuman

doi:10.1016/j.matpr.2018.06.242

Cited by 17 publications

(13 citation statements)

References 4 publications

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“…It is also a population-based method and uses a population of solu-tions to find the global optimum solution. The wide application of TLBO in engineering fields is reflected in the improvement of the performance of distinguished systems, such as fatty acid methyl esters (Baghban et al, 2018), scheduling of projects (Kumar et al, 2018), plasma arc cutting (Patel et al, 2018) and power consumption optimization (Rao, 2019). The population is considered as a group of learners or a class of learners.…”

Section: Teaching-learning-based Optimizationmentioning

confidence: 99%

Design of an Optimal Fuzzy Controller of an Under-Actuated Manipulator Based on Teaching-Learning-Based Optimization

Mahmoodabadi

Baghini

2019

Acta Mechanica Et Automatica

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In this paper, an optimal fuzzy controller based on the Teaching-Learning-Based Optimization (TLBO) algorithm has been presented for the stabilization of a two-link planar horizontal under-actuated manipulator with two revolute (2R) joints. For the considered fuzzy control method, a singleton fuzzifier, a centre average defuzzifier and a product inference engine have been used. The TLBO algorithm has been implemented for searching the optimum parameters of the fuzzy controller with consideration of time integral of the absolute error of the state variables as the objective function. The proposed control method has been utilized for the 2R under-actuated manipulator with the second passive joint wherein the model moves in the horizontal plane and friction forces have been considered. Simulation results of the offered control method have been illustrated for the stabilization of the considered robot system. Moreover, for different initial conditions, the effectiveness and the robustness of the mentioned strategy have been challenged.

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Section: Teaching-learning-based Optimizationmentioning

confidence: 99%

Design of an Optimal Fuzzy Controller of an Under-Actuated Manipulator Based on Teaching-Learning-Based Optimization

Mahmoodabadi

Baghini

2019

Acta Mechanica Et Automatica

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“…Parthkumar Patel et al [3] studied the effect of cutting speed, gas pressure and torch height on surface roughness. Both theoretical methodology and experimental work were conducted.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Surface Roughness When Cutting Hard Material with Plasma Arc Cutting

Koura¹,

A.Afifi²,

Tawfik³

et al. 2021

AJMIE

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Surface quality represented in the primary surface texture is the main parameter when dealing with surfaces in relative rotary and sliding motions. In conventional machining processes, if further quality in surface roughness is required, finishing and superfinishing machining operations are available. In case of extremely high resistive, hard and high strength materials conventional machining are highly uneconomical. The solution was found in cutting such hard materials is by using non-traditional processes. Plasma arc cutting was one solution. However, the surface quality as a response of the parameters involved in this process, namely gas pressure, cutting speed and standoff distance is not in the high finished quality. The aim of this paper is studying the effect of the cutting conditions of plasma arc cutting on the primary surface texture as assessed by the International standard Arithmetical Mean Average "R a " for the produced parts. Experimental work was carried out on specimen made of Hardox400. The effect of the three considered parameters of plasma arc cutting were studied on the responded surface roughness. Results were analyzed using Minitab17 and binomial regression equations were deduced. Cutting speed showed greater effect on surface roughness and all has showed that surface roughness ranged from 5 to 5.5 µm.

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“…In the teaching phase, all students follow the teacher to learn while students discuss with each other in the learning phase. Because of its advantages of simple structure and few control parameters, TLBO algorithm is extensively used in scientific research and industry, such as economic dispatch [19], robot design [20], plasma arc cutting [21], iris recognition [22], trajectory planning [23], fuzzy clustering [24], generation control [25], fault detection [26], and shop scheduling [27].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Teaching-Learning-Based Optimization Algorithm for Optimal Design of Hybrid Active Power Filter

Cui

Dai

et al. 2020

IEEE Access

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Hybrid active power filter (HAPF) has been widely used to suppress harmonics in the electric power system. However, selecting HAPF parameters accurately remains a primary challenge faced by researchers. To optimize HAPF parameters and reduce the harmonic pollution, this paper proposes an improved teaching-learning-based optimization algorithm, namely HTLBO. In HTLBO, a self-study strategy based on Lévy-Flight is developed to avoid the population falling into local optima. Furthermore, in the teaching phase, all learners are divided into three hierarchies according to their learning ability, and learners at different hierarchies learn from different teachers respectively. While in the learning phase, each learner learns not only from a better individual but also from a worse individual. The above hierarchical teaching strategy and improved learning strategy effectively balance the exploration tendency and exploitation tendency of the algorithm. In addition, a competitive mechanism based on dynamic clustering is proposed to ensure the vitality of the entire population. The performance of HTLBO is verified by identifying the parameters of two classical HAPF topologies. Experimental results present that compared with the other nine well-established meta-heuristics algorithms, HTLBO achieves outstanding performance, especially in terms of accuracy and reliability. INDEX TERMS Hybrid active power filter (HAPF), harmonic pollution (HP), hierarchical learning, metaheuristics, teaching-learning-based optimization (TLBO).

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Optimization of Surface Roughness in Plasma Arc Cutting of AISID2 Steel Using TLBO

Cited by 17 publications

References 4 publications

Design of an Optimal Fuzzy Controller of an Under-Actuated Manipulator Based on Teaching-Learning-Based Optimization

Design of an Optimal Fuzzy Controller of an Under-Actuated Manipulator Based on Teaching-Learning-Based Optimization

Investigation on the Surface Roughness When Cutting Hard Material with Plasma Arc Cutting

A Hierarchical Teaching-Learning-Based Optimization Algorithm for Optimal Design of Hybrid Active Power Filter

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