An Analysis on the Parametric Optimization of Electrochemical Honing Process

2020 ◽  
Vol 19 (02) ◽  
pp. 249-276
Author(s):  
Sunny Diyaley ◽  
Shankar Chakraborthy
Keyword(s):  
Removal Rate ◽  
Electrochemical Machining ◽  
Optimal Solution ◽  
Dimensional Accuracy ◽  
Cuckoo Search ◽  
Machining Process ◽  
De Algorithm ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Electrochemical honing (ECH) is a nontraditional machining process hybridizing the conjoint benefits of electrochemical machining (ECM) and mechanical honing actions. In this process, maximum amount of material is removed through anodic dissolution, followed by mechanical abrasion. In present day manufacturing industries, it has found wide ranging applications, mainly in finishing of varieties of gears, due to its various advantages, like increased material removal rate, long tool life, burr-free operation, achievement of higher surface finish and dimensional accuracy, generation of no residual stress, reduced noise, less material damage, etc. In order to achieve maximum machining capability from this process, it is always recommended to set its various input parameters at their optimal operating levels. In this paper, four powerful metaheuristic algorithms, i.e. firefly algorithm, differential evolution (DE) algorithm, cuckoo search (CS) algorithm and teaching–learning-based optimization (TLBO) algorithm are applied for single as well as multi-objective optimization of pulsed-ECH (PECH) and ECH processes. It is observed that TLBO algorithm supersedes other techniques in optimizing the two ECH processes with respect to the value of the derived optimal solution, consistency of the solutions and computational speed.

scholarly journals TEACHING-LEARNING-BASED PARAMETRIC OPTIMIZATION OF AN ELECTRICAL DISCHARGE MACHINING PROCESS

2020 ◽  
Vol 18 (2) ◽  
pp. 281 ◽  
Author(s):  
Vidyapati Kumar ◽  
Sunny Diyaley ◽  
Shankar Chakraborty
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Search Algorithm ◽  
Removal Rate ◽  
Differential Evolution Algorithm ◽  
Cuckoo Search ◽  
Machining Process ◽  
Machining Processes ◽  
Tlbo Algorithm ◽  
Teaching Learning

Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate, surface roughness, tool wear rate, overcut and circularity error are treated as responses. Based on single- and multi-objective optimization models, this process is optimized using the teaching-learning-based optimization (TLBO) algorithm, and its performance is contrasted against firefly algorithm, differential evolution algorithm and cuckoo search algorithm. It is revealed that the TLBO algorithm supersedes the others with respect to accuracy and consistency of the derived optimal solutions, and computational efforts.

Multivariable Modified Teaching Learning Based Optimization (MM-TLBO) Algorithm for Inverse Modeling of Residual Gravity Anomaly Generated by Simple Geometric Shapes

2020 ◽  
Vol 25 (4) ◽  
pp. 463-476
Author(s):  
Ata Eshaghzadeh ◽  
Alireza Hajian
Keyword(s):  
Gravity Anomaly ◽  
Gravity Data ◽  
Optimal Solution ◽  
Model Parameters ◽  
Major Purpose ◽  
Residual Gravity ◽  
Tlbo Algorithm ◽  
Residual Gravity Anomaly ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper presents an improved nature-based algorithm, namely multivariable modified teaching learning based optimization (MM-TLBO) algorithm, as in an iterative process can estimates the best values for the model parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MM-TLBO algorithm is to improve the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the radius ( R), depth ( h), shape factor ( q), density contrast ( ρ) and axis location ( x0) parameters. We apply MM-TLBO and TLBO methods for the residual gravity anomalies caused by the buried masses with a simple geometry such as spheres, horizontal and vertical cylinders. The efficiency of these methods are also tested by noise corruption synthetic data, as the acceptable results were obtained. The obtained results indicate the better performance the MM-TLBO algorithm than the TLBO algorithm. We have utilized the MM-TLBO for the interpretation of the six residual gravity anomaly profiles from Iran, USA, Sweden and Senegal. The advantage of the MM-TLBO inversion is that it can estimates the best solutions very fast without falling into local minimum and reaches to a premature convergence. The considered primary population for the synthetic and real gravity data are thirty and fifty models. The results show which this method is able to achieve the optimal responses even if a small population of learners had been considered.

Performance evaluation of newly designed nozzle on abrasive jet machining characteristics of laminated composites

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
M. Balasubramanian ◽  
S. Madhu
Keyword(s):  
Removal Rate ◽  
Dimensional Accuracy ◽  
Machining Process ◽  
Machining Time ◽  
Content Type ◽  
Abrasive Jet Machining ◽  
Glass Fibre Reinforced Polymer ◽  
Challenging Tasks ◽  
Fibre Reinforced Polymer ◽  
Glass Fibre Reinforced

Purpose The purpose of the study is to machine the composites at lower machining time with higher accuracy without causing delamination. Design/methodology/approach Abrasive jet machining is the technology appropriate for machining composite materials to obtain good dimensional accuracy without causing de-lamination. The central composite design was followed in deciding the number of experiments to be carried out. Findings The influence of abrasive jet machining process parameters on machining time, material removal rate (MRR) and kerf characteristics were investigated. The experimental results proved the newly designed internal threaded nozzle increased MRR, thereby reducing the machining time. Originality/value Machining of glass fibre reinforced polymer (GFRP) is one of the challenging tasks given its non-linear and in-homogeneous properties. In this investigation, newly developed threaded and unthreaded nozzles in machining were used for making holes on the GFRP composites.

PMU Placement Optimization for Fault Observation Using Different Techniques

2021 ◽  
pp. 196-220
Author(s):  
Hamid Bentarzi
Keyword(s):  
Cuckoo Search ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Placement Optimization ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

scholarly journals Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zong-Sheng Wu ◽  
Wei-Ping Fu ◽  
Ru Xue
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Random Number ◽  
Nonlinear Problems ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Linear And Nonlinear ◽  
Weighted Factor ◽  
Teaching Learning ◽  
Dynamic Inertia

Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.

Study of Pulse Electrochemical Machining Performance of Deep-Small Hole on Nickel-Based Alloy

2011 ◽  
Vol 204-210 ◽  
pp. 1830-1834
Author(s):  
Zhao Long Li ◽  
Shi Chun Di
Keyword(s):  
Base Alloy ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Machining Process ◽  
Machining Accuracy ◽  
Machining Performance ◽  
Math Model ◽  
Technical Parameters ◽  
Pulse Electrochemical Machining ◽  
Small Holes

The method of machining deep hole on Ni-base alloy which can tolerant high temperature by pulse electrochemical machining has been proposed in this paper. Five technical parameters are discussed on the effect of mass removal rate of machining process. Establish a dynamic math model, and analyze the effect of process parameters on the mass material removal rate of deep small holes. Machining accuracy of deep small holes was analyzed.

scholarly journals Synthesis of Thinned Concentric Circular Antenna Arrays Using Modified TLBO Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zailei Luo ◽  
Xueming He ◽  
Xuedong Chen ◽  
Xin Luo ◽  
Xiaoqing Li
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Extended Space ◽  
Half Wavelength ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Antenna Array Synthesis ◽  
Circular Antenna Arrays ◽  
Circular Antenna Array

Teaching-learning-based optimization (TLBO) algorithm is a new kind of stochastic metaheuristic algorithm which has been proven effective and powerful in many engineering optimization problems. This paper describes the application of a modified version of TLBO algorithm, MTLBO, for synthesis of thinned concentric circular antenna arrays (CCAAs). The MTLBO is adjusted for CCAA design according to the geometry arrangement of antenna elements. CCAAs with uniform interelement spacing fixed at half wavelength have been considered for thinning using MTLBO algorithm. For practical purpose, this paper demonstrated SLL reduction of thinned CCAAs in the whole regular and extended space other than the phi = 0° plane alone. The uniformly and nonuniformly excited CCAAs have been discussed, respectively, during the simulation process. The proposed MTLBO is very easy to be implemented and requires fewer algorithm specified parameters, which is suitable for concentric circular antenna array synthesis. Numerical results clearly show the superiority of MTLBO algorithm in finding optimum solutions compared to particle swarm optimization algorithm and firefly algorithm.

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