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

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Introduction to Mechanical Engineering

Part of the book series: Materials Forming, Machining and Tribology ((MFMT))

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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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Authors and Affiliations

  1. Faculty of Mechanical Engineering-Laboratory of Manufacturing Processes and Machine Tools (LMProMaT), School of Pedagogical and Technological Education (ASPETE), 141 21, N. Heraklion, Athens, Greece

    Nikolaos A. Fountas & Nikolaos M. Vaxevanidis

  2. Mechanical Engineering Department, Piraeus University of Applied Sciences (PUAS), 122 44, Aigaleo, Greece

    Constantinos I. Stergiou

  3. Faculty of Science, Engineering and Computing (SEC), School of Mechanical and Automotive Engineering—Roehampton Vale Campus, Kingston University, London, SW15 3DW, UK

    Nikolaos A. Fountas & Redha Benhadj-Djilali

Authors
  1. Nikolaos A. Fountas
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  2. Nikolaos M. Vaxevanidis
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  3. Constantinos I. Stergiou
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  4. Redha Benhadj-Djilali
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Corresponding author

Correspondence to Nikolaos M. Vaxevanidis .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

Glossary

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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Fountas, N.A., Vaxevanidis, N.M., Stergiou, C.I., Benhadj-Djilali, R. (2018). Comparison of Non-conventional Intelligent Algorithms for Optimizing Sculptured Surface CNC Tool Paths. In: Davim, J. (eds) Introduction to Mechanical Engineering. Materials Forming, Machining and Tribology. Springer, Cham. https://doi.org/10.1007/978-3-319-78488-5_12

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78487-8

  • Online ISBN: 978-3-319-78488-5

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