Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation

Yıldız, Ali Rıza; Öztürk, Ferruh

doi:10.1243/09544054jem570

Cited by 78 publications

(19 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They have identified a Pareto-optimal solution set based on relative effects of the various factors on the objective function. Recently Yildiz and Ozturk (2006) applied hybrid enhanced genetic algorithm. They have introduced a refined design space for population in genetic algorithm by integrating robust design parameter concept.…”

Section: Optimizing the Chosen Factor Levels To Attain Minimum Tool Fmentioning

confidence: 99%

Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools

Palanikumar

Davim

2009

Journal of Materials Processing Technology

View full text Add to dashboard Cite

Section: Optimizing the Chosen Factor Levels To Attain Minimum Tool Fmentioning

confidence: 99%

Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools

Palanikumar

Davim

2009

Journal of Materials Processing Technology

View full text Add to dashboard Cite

“…Yildiz and Ozturk [21] present a hybrid-enhanced genetic algorithm that is developed for solving the optimization problems in design and manufacturing. The present approach is applied to optimize turning operation for the determination of cutting parameters considering minimum production cost under a set of machining constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulated annealing algorithm for balanced allocation problem

Rajesh

Pugazhendhi

Ganesh

2011

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper deals with the balanced allocation of customers to multiple third party logistics warehouses. The allocation problem generally deals with clustering of customers so as to achieve minimum total resource viz. cost or time. But the real challenge arises when it is required to strike a balance between the allocation while also minimizing the total cost or time. Since the problem develops to be non-deterministic polynomial-time hard, the paper uses simulated annealing approach to solve the problem. The balanced solution is achieved by using the min-max function. The effectiveness of the new algorithm is presented through simulation of large sets of problems.

show abstract

“…Yildiz (2009) demonstrated the superiority of the proposed hybrid method by combining immune algorithm with a hill climbing local search algorithm for solving multi-pass turning operation. Also, hybridization of simulated annealing and Hooke-Jeeves algorithm (Chen & Tsai, 1996), genetic algorithm and simulated annealing (Wang et al, 2004), Taguchi's method and genetic algorithm (Yildiz & Ozturk, 2006), etc., proved the effectiveness and efficiency of combined approach for solving machining optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of machining processes using pattern search algorithm

Madić¹,

Radovanović²

2014

10.5267/j.ijiec

View full text Add to dashboard Cite

Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS) algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.

show abstract

Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation

Cited by 78 publications

References 42 publications

Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools

Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools

Simulated annealing algorithm for balanced allocation problem

Optimization of machining processes using pattern search algorithm

Contact Info

Product

Resources

About