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Open AccessArticle

Cutting Insert and Parameter Optimization for Turning Based on Artificial Neural Networks and a Genetic Algorithm

1
Department of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
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Department of Mechanical and Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
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Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(3), 479; https://doi.org/10.3390/app9030479
Received: 2 January 2019 / Revised: 25 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
The objective of this present study is to develop a system to optimize cutting insert selection and cutting parameters. The proposed approach addresses turning processes that use technical information from a tool supplier. The proposed system is based on artificial neural networks and a genetic algorithm, which define the modeling and optimization stages, respectively. For the modeling stage, two artificial neural networks are implemented to evaluate the feed rate and cutting velocity parameters. These models are defined as functions of insert features and working conditions. For the optimization problem, a genetic algorithm is implemented to search an optimal tool insert. This heuristic algorithm is evaluated using a custom objective function, which assesses the machining performance based on the given working specifications, such as the lowest power consumption, the shortest machining time or an acceptable surface roughness. View Full-Text
Keywords: cutting insert selection; cutting parameter optimization; artificial neural networks; genetic algorithm cutting insert selection; cutting parameter optimization; artificial neural networks; genetic algorithm
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MDPI and ACS Style

Solarte-Pardo, B.; Hidalgo, D.; Yeh, S.-S. Cutting Insert and Parameter Optimization for Turning Based on Artificial Neural Networks and a Genetic Algorithm. Appl. Sci. 2019, 9, 479.

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