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Erschienen in:
Mechanical Properties of Engineering Materials: Relevance in Design and Manufacturing Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

12. Comparison of Non-conventional Intelligent Algorithms for Optimizing Sculptured Surface CNC Tool Paths

verfasst von : Nikolaos A. Fountas, Nikolaos M. Vaxevanidis, Constantinos I. Stergiou, Redha Benhadj-Djilali

Erschienen in: Introduction to Mechanical Engineering

Verlag: Springer International Publishing

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Abstract

The optimization of process parameters referring to sculptured surface tool path planning increases efficiency and enhances product quality; thus, it is for the major research subject for many noticeable studies. Optimization for process parameters is usually conducted by working with a two-phase scheme; regression modeling based on the results obtained by a design of experiments, and optimization by employing an intelligent algorithm. Currently, new artificial algorithms have been developed and deployed to address different kinds of problems in engineering. In the present work, six new intelligent algorithms have been tested to sculptured surface tool path optimization problems, namely particle swarm optimization (PSO), invasive weed optimization (IWO), shuffled frog-leaping algorithm (SFLA), shuffled complex evolution (SCE), teaching–learning-based optimization (TLBO), and virus-evolutionary genetic algorithm (VGA). Except from the VGA which has been developed from scratch, the rest of the algorithms have been adopted from the literature whilst the case studies the algorithms are applied to have been established using design of machining simulation experiments on benchmark sculptured surfaces. The results obtained from case studies are compared with each other to investigate the capabilities of the aforementioned algorithms in terms of their application to the sculptured surface machining problem.

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Vorheriges Kapitel Smart Machining System Using Preprocessor, Postprocessor, and Interpolation Techniques
Glossar
Chordal deviation
The type of machining error owing to cutting tool interpolation when applying CNC machining as a key metal cutting operation
Computer-aided manufacturing (CAM)
Environment for modeling manufacturing processes with the aid of computers
Design of experiments
The process of establishing experimental runs so as to investigate the influence of independent process parameters to one or more responses (dependent variables)
Empirical models
Mathematical formulas (regression equations) relating independent variables and responses to be used for predicting crucial results prior to actual operations
Genetic algorithms
Artificial intelligence heuristics used for optimizing one or more objectives related to an engineering problem
Metal cutting
The field including the manufacturing processes where raw materials are turned to final products by removing the excess material by machining
Metal forming
The field including the manufacturing processes where raw materials are shaped directly to final products
Objective function
A mathematical relation expressing one or more criteria for optimization either with the use of artificial intelligence (genetic algorithms) or conventional engineering computing
Scallop height
The remaining material a tool path leaves among adjacent tool passes when machining free-form products with CNC technology
Sculptured surface machining
The metal cutting operation for producing free-form surfaces found in modern products, assisted by CNC machining technology
Tool path planning
The process of computing the path to be followed by one or more cutting tools using the CAM environment
Virus-evolutionary genetic algorithm
A special genetic algorithm following the principles of the virus theory of evolution
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Metadaten
Titel
Comparison of Non-conventional Intelligent Algorithms for Optimizing Sculptured Surface CNC Tool Paths
verfasst von
Nikolaos A. Fountas
Nikolaos M. Vaxevanidis
Constantinos I. Stergiou
Redha Benhadj-Djilali
Copyright-Jahr
2018
Buch
Introduction to Mechanical Engineering

Print ISBN: 978-3-319-78487-8

Electronic ISBN: 978-3-319-78488-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-78488-5_12

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