Arindam Majumder

Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters

The process parameters of electric discharge machining such as current, pulse-on time and pulse-off time play a major role for deciding the machining performance such as material removal rate and wear ratio. In this article, the process parameters of electric discharge machining have been optimized for maximum material removal rate and minimum wear ratio. A properly trained neural network has been used to establish the relation between the process parameters and machining performance. Three different evolutionary algorithms such as simulated annealing, genetic algorithm and particle swarm optimization were then used with the neural network model to predict the optimum process parameters for maximum material removal rate and minimum wear ratio. The evolutionary algorithms thus used have been compared in terms of performance.

An approach to optimize the EDM process parameters using desirability-based multi-objective PSO

The present work deals with the prediction of optimal parametric data-set with maximum material removal rate (MRR) and a minimum electrode wear ratio (EWR) during Electrical discharge machining (EDM) of AISI 316LN Stainless Steel. For this purpose, empirical models showing relation between inputs and outputs were developed using response surface methodology. Desirability-based multi-objective particle swarm optimization-original, desirability-based multi-objective particle swarm optimization-inertia weight, and desirability-based multi-objective particle swarm optimization-constriction factor are then used to estimate the optimal process parameters for maximum MRR and minimum EWR. The results obtained by applying these three desirability-based multi-objective particle swarm optimization (DMPSO) algorithms are compared. From the comparison and confirmatory experiment, it can be observed that DMPSO-CF is the most efficient algorithm for the optimization of EDM parameters.

Optimization of process parameters of submerged arc welding by using grey–fuzzy-based Taguchi method for AISI 1518 grade steel

In this article, a grey–fuzzy-based algorithm with the Taguchi method is proposed to find the optimal process parameters’ setting for submerged arc welding process of AISI 1518 grade steel on the multiple performance characteristics such as tensile stress, toughness and hardness of weldment. Various process parameters, such as wire feed rate, stick out and traverse speed of welding process, were exposed by investigation. The proposed algorithm, coupling the grey relational analysis with the fuzzy logic, obtains a grey–fuzzy reasoning grade to evaluate the multiple performance characteristics according to the grey relational coefficient of each performance characteristic. Through the grey–fuzzy logic analysis, the optimization of multiple performance characteristics can be converted into the optimization of a single grey–fuzzy reasoning grade. Finally, the process parameters are optimized by the Taguchi method. The optimal process parameter combination becomes Wf-3 So-1 Ts-3, that is, the wire feed rate at level 3 (2 m/min), stick out at level 1 (20 mm) and traverse speed at level 3 (0.9 m/min) for maximum tensile stress and toughness and minimum hardness of weldments. It is also observed stick out on the overall mechanical property is more significant compared with other welding parameters (wire feed rate, traverse speed).

Cuckoo Search on Parallel Batch Processing Machines

This paper presents a new version of discrete cuckoo search algorithm to minimize makespan on parallel batch processing machines. In the proposed algorithm, we consider a modified Levy flight based on job interchange and job insertion techniques to generate new cuckoos by random walk. The initial population of the algorithm is generated using best-fit heuristic approach. Results of computational experimentation with a large set of random instances of non-sparse parallel batch processing with unequal job ready times show that the proposed algorithm significantly performs better than some state-of-the-art algorithms.

A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (Ton), pulse-off time (Toff) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.

Standard Deviation Method Based PSO: An Instigated Approach to Optimize Multi-Objective Manufacturing Process Parameters

Nowadays, optimization of process parameters in manufacturing process deals with a number of objectives. However, the optimization of such process becomes more complex if selected attributes are conflicting in nature. Therefore, to overcome this problem in this study a SDM based PSO algorithm is proposed for optimizing the manufacturing process having multi attribute. In this proposed approach the SDM is used to convert multi attributes into single attribute, named as multi performance index, while the optimal value of this multi performance index is predicted by PSO. Finally, three instances related to optimization of advanced manufacturing process parameters are solved by the proposed approach and are compared with the results of the other established optimization techniques such as Desirability based RSM, SDM-GA and SDM-CACO. From the comparison it has been revealed that the proposed approach performs better as compare to the existing approaches.