Selection of optimal process parameters in WEDM while machining Al7075/SiCp metal matrix composites

Rao, Thella Babu; Krishna, A. Murali

doi:10.1007/s00170-014-5780-0

Cited by 58 publications

(31 citation statements)

References 35 publications

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“…Rao and Pawar [19] applied ABC optimization to find the optimum combination of process parameters for wire-EDM with an objective of achieving maximum machining speed for a desired value of surface finish. Rao and Krishna [20] also conducted wire-EDM machining experiments on Aluminum metal matrix composites reinforced with silicon carbide particulate and developed some empirical models with Response Surface Methodology (RSM) for Surface Roughness (SR), Metal Removal Rate (MRR), and Wire Wear Ratio (WWR) in terms of machining parameters. They did some works on multiobjective optimization for minimizing SR, WWR and maximizing MRR, using GA-II.…”

Section: Many Applications Of Metaheuristics In Electricalmentioning

confidence: 99%

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Hossain¹

2017

IJARA

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In die manufacturing industries surface roughness is considered as a vital quality characteristic in order to retain the consumers' satisfaction. On the other hand, manufacturers want to minimize the machining time which eventually reduces their cost. This research deals with an optimization problem to minimize the machining time (T) for end milling operation on hot die steel (H13), subject to specified surface roughness (R a ) limits. Six machining parameters and corresponding T and R a were recorded from 74 independent experiments. After exhaustive search, three machining parameters (tool inclination angle, tool diameter and radial depth of cut) for R a and two machining parameters (feed rate and radial depth of cut) for T are found to be highly influential. In terms of these corresponding parameters, two ANFIS models are developed for the prediction of R a and T, respectively. These models are utilized to find the optimum values of machining parameters. Five advanced metaheuristic algorithms, Artificial Bee Colony (ABC), quick artificial bee colony, modified differential evolution, ant colony optimization for real numbers and simulated annealing, with or without local search, are applied for solving this optimization problem. Each of the algorithms is run for 30 times, allowing 100,000 number of function evaluations in each run. Statistical analysis (F-test, t-test) are done to evaluate the performance of the algorithms. Hybrid ABC with local search is proposed as the best algorithm for solving this problem based on average of minimum machining time obtained. The proposed optimization approach can be used for parameter selection in real time machining with artificially intelligent Computer Numerical Control (CNC) machine tools.

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Section: Many Applications Of Metaheuristics In Electricalmentioning

confidence: 99%

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Hossain¹

2017

IJARA

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“…In addition, Singh and Sodhi [6] employed the RSM to investigate the cutting parameters of the CNC turning of Al7020 to obtain the maximal material removal (MMR) and a lower roughness. Rao and Krishna [7] used a non-dominated Sorting Genetic Algorithm-II (NSGA II) for evaluating the surface roughness, MMR, and wire-electrode wear when performing electrical discharge machining (EDM). Subramanian et al [8] used RSM to predict the surface roughness of Al7075-T651 work piece with high-speed steel end milling in terms of tool geometry.…”

Section: Introductionmentioning

confidence: 99%

Optimal Prediction and Design of Surface Roughness for CNC Turning of Al7075-T6 by Using the Taguchi Hybrid Qpso Algorithm

Chen

Huang

Yang

et al. 2016

Transactions of the Canadian Society for Mechanical Engineering

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This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

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“…Since there is no direct physical contact between the tool electrode and the workpiece, the process promises to be the effective and economical technique for machining conductive ceramics [7][8][9][10]. However, the electrical resistivity of the popular engineering ceramics is higher than that of metal, which makes machining engineering ceramics more difficult than machining metal using EDM.…”

Section: Introductionmentioning

confidence: 99%

Experimental research on electrical discharge machining characteristics of engineering ceramics with different electrical resistivities

Diao

et al. 2014

Int J Adv Manuf Technol

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Electrical discharge machining (EDM) is the extensively used nonconventional material removal process for machining engineering ceramics provided they are electrically conductive. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics that can be machined effectively by EDM. This paper investigates the effects of the electrical resistivity and the EDM parameters on the EDM performance of ZnO/Al 2 O 3 ceramic in terms of the machining efficiency and the quality. The experimental results showed that the electrical resistivity and the EDM parameters such as pulse on-time, pulse off-time, and peak current had the great influence on the machining efficiency and the quality during electrical discharge machining of ZnO/ Al 2 O 3 ceramic. Moreover, the electrical resistivity of the ZnO/ Al 2 O 3 ceramic, which could be effectively machined by EDM, increased with increasing the pulse on-time and peak current and with decreasing the pulse off-time, respectively. Furthermore, the ZnO/Al 2 O 3 ceramic with the electrical resistivity up to 3,410 Ω cm could be effectively machined by EDM with the appropriate machining condition.

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Selection of optimal process parameters in WEDM while machining Al7075/SiCp metal matrix composites

Cited by 58 publications

References 35 publications

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Optimal Prediction and Design of Surface Roughness for CNC Turning of Al7075-T6 by Using the Taguchi Hybrid Qpso Algorithm

Experimental research on electrical discharge machining characteristics of engineering ceramics with different electrical resistivities

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