Optimization of Machining Parameters of EDM Of Al6061-Sic MMC Using Firefly Algorithm

2019 ◽  
Author(s):  
Rajesh Kanna S K ◽  
Sethuramalingam P ◽  
Abdul Munaf A ◽  
Lingaraj N ◽  
Sivashankar P
Keyword(s):  
Firefly Algorithm ◽  
Machining Parameters

scholarly journals Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Abderrahim Belloufi ◽  
Mekki Assas ◽  
Imane Rezgui
Keyword(s):  
Production Cost ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Machining Parameters ◽  
Metal Parts ◽  
Multipass Turning ◽  
Optimal Cutting Parameters ◽  
Selection Of

Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new optimization technique, firefly algorithm, is used for determining the machining parameters in a multipass turning operation model. The objective considered is minimization of production cost under a set of machining constraints. The optimization is carried out using firefly algorithm. An application example is presented and solved to illustrate the effectiveness of the presented algorithm.

Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm

2015 ◽  
Vol 815 ◽  
pp. 268-272 ◽  
Author(s):  
Nur Farahlina Johari ◽  
Azlan Mohd Zain ◽  
Noorfa Haszlinna Mustaffa ◽  
Amirmudin Udin
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Firefly Algorithm ◽  
Volume Fraction ◽  
Optimization Problems ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Time

Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Optimization of multi-pass turning and multi-pass face milling using subpopulation firefly algorithm

2018 ◽  
Vol 233 (5) ◽  
pp. 1520-1540 ◽  
Author(s):  
Goran R Miodragović ◽  
Violeta Đorđević ◽  
Radovan R Bulatović ◽  
Aleksandra Petrović
Keyword(s):  
Production Cost ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Face Milling ◽  
Iteration Step ◽  
Machining Parameters ◽  
Machining Time ◽  
Turning Operation ◽  
Good Convergence ◽  
Milling Operation

In this paper, Subpopulation Firefly Algorithm is proposed for optimization of machining parameters in multi-pass turning and multi-pass face milling operations. Basic Firefly Algorithm is modified with the aim to avoid space of local minimum and to meet the operation constraints in each iteration step. For that purpose, the following modifications are made: one firefly population is divided into two, a crossover operator is introduced and the searching for new design variables is continued until constraint functions are fulfilled. For turning operation, optimization is carried out for one objective: minimization of production cost. For face milling operation, multi-objective optimization is used for minimizing production cost and machining time, and maximizing profit rate at the same time. In both cases of multi-pass machining operations, optimization process implies meeting all operation constraints. For multi-pass turning operation, the best results from literature are confirmed with good convergence and low value of standard deviation. For multi-pass milling operation, better results are achieved compared with existing results from literature. The proposed algorithm showed capability of achieving global optimum for complex optimization problems.

Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm

2013 ◽  
Vol 31 (1) ◽  
pp. 1-9 ◽  
Author(s):  
S. Bharathi Raja ◽  
C. V. Srinivas Pramod ◽  
K. Vamshee Krishna ◽  
Arvind Ragunathan ◽  
Somalaraju Vinesh
Keyword(s):  
Electrical Discharge ◽  
Firefly Algorithm ◽  
Electrical Discharge Machining ◽  
Machining Parameters ◽  
Die Steel

Optimization of Constrained Machining Parameters in Turning Operation Using Firefly Algorithm

2012 ◽  
Vol 12 (10) ◽  
pp. 1038-1042 ◽  
Author(s):  
S. Bharathi Raja ◽  
N. Sathiya Narayanan ◽  
C.V. Srinivas Pramod ◽  
Arvind Ragunathan ◽  
Somala Raju Vinesh ◽  
...  
Keyword(s):  
Firefly Algorithm ◽  
Machining Parameters ◽  
Turning Operation

Re-Machining Parameter Optimization Using Firefly Algorithms

2013 ◽  
pp. 186-202
Keyword(s):  
Experimental Study ◽  
Parameter Optimization ◽  
Firefly Algorithm ◽  
Machining Parameters ◽  
Future Trends ◽  
Research Directions ◽  
Novel Approach ◽  
Machining Parameter ◽  
Selection Of

This chapter presents a novel approach for identification of the re-machining parameters. The chapter starts with an introduction about the significant role of re-machining at the reprocessing stage. Then, the related studies dealing with the selection of optimum machining parameters are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. A detailed description about the approach (i.e., firefly algorithm) can be found in the proposed methodology section. Right after this, an illustrative example is detailed in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.

Multi-Objective Optimization of Surface Roughness and Tool Wear in Turning Inconel 718: A Desirability Analysis, Genetic Algorithm and Firefly Algorithm 

2014 ◽  
Vol 592-594 ◽  
pp. 545-549 ◽  
Author(s):  
Shiv Kumar Sharma ◽  
M. Chandrasekaran ◽  
R. Thirumalai
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Tool Wear ◽  
Process Parameters ◽  
Inconel 718 ◽  
Firefly Algorithm ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Desirability Analysis

In this work, a parameter optimization in turning Inconel 718 for multiple performance characteristics has been attempted. The process parameters viz., cutting speed (v), feed (f) and depth of cut (d) is optimized that minimizes surface roughness (Ra) and tool wear (VB). Response surface methodology (RSM) employing CCD experimental design was used to develop predictive model for Ra and VB. The predictive capability of the model provides the average percentage error as 3.87 % and 5.10% for Ra and VB respectively with maximum percentage error limited to 14.67 %. The data are analysed to study the main effect and interaction effects of machining parameters through surface plot. Feed remains dominating factor. Process parameters are optimized for single and multiple objectives using three different techniques viz., statistical and mathematical approach based desirability analysis (DA) and soft computing based genetic algorithm (GA) and firefly algorithm (FA). The results are compared.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

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