Machining parameters selection for stock removal turning in process plans using a float encoding genetic algorithm

Chen, Mu-Chen; Tseng, Hsien Yu

doi:10.1080/02533839.1998.9670412

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…Genetic algorithm (GA), the most basic evolutionary algorithm, is a popular method used for optimisation. Chen and Tseng (1998) introduced a float encoding GA into machining conditions selection. Morad and Zalzala (1999) applied a GA to minimise the makespan, the total rejects and the total cost of production.…”

Section: Related Workmentioning

confidence: 99%

A big data analytics based machining optimisation approach

Yin

Wang

2018

J Intell Manuf

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Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

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Section: Related Workmentioning

confidence: 99%