A Divide-and-Conquer Bat Algorithm with Direction of Mean Best Position for Optimization of Cutting Parameters in CNC Turnings

Optimization of machining parameters is an important problem in the modern manufacturing world due to production efficiency and economics. This problem is well known to be complex and is regarded as a strongly nondeterministic polynomial (NP)-hard problem. To reduce the production cost of work-piece...

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Vydáno v:Computational intelligence and neuroscience Ročník 2022; s. 1 - 12
Hlavní autoři: Huang, Xingwang, He, Zongbao, Chen, Yong, Xie, Shutong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Hindawi 23.02.2022
John Wiley & Sons, Inc
Témata:
Algorithms
Combinations (mathematics)
Complexity
Computer Simulation
Costs
Cutting parameters
Genetic algorithms
Intelligence
Machining
Manufacturing
Manufacturing costs
Mathematical models
Mathematical optimization
Normal Distribution
Numerical controls
Optimization
Optimization algorithms
Polynomials
Process parameters
Production costs
Research methodology
Swarm intelligence
Taguchi methods
Turning (machining)
Workpieces
ISSN:1687-5265, 1687-5273, 1687-5273
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Popis
Shrnutí:Optimization of machining parameters is an important problem in the modern manufacturing world due to production efficiency and economics. This problem is well known to be complex and is regarded as a strongly nondeterministic polynomial (NP)-hard problem. To reduce the production cost of work-pieces in computer numerical control (CNC) machining, a novel optimization algorithm based on a combination of the bat algorithm and a divide-and-conquer strategy is proposed. First, the basic bat algorithm (BA) is modified with the aim to avoid finding the local optimal solution. In addition, a Gaussian quantum bat algorithm with direction of mean best position is developed. Second, in order to reduce the complexity of the optimization problem, the whole optimization problem is divided into several subproblems by using a divide-and-conquer strategy according to the characteristic of multipass turning operations. Finally, under a large number of machining constraints, the cutting parameters of the two stages of roughing and finishing are simultaneously optimized. Simulation results show that the proposed algorithm can find better combinations of the machining parameters than other algorithms proposed previously to further reduce the production cost. In addition, the outcome of our work presents a novel way to solve the complex optimization problem of machining parameters with a combination of traditional mathematical methods and swarm intelligence algorithms.
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Academic Editor: Diego Oliva
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/4719266