Milling is a prevalent machining technique employed in various industries for the production of metallic and non-metallic components. This article focuses on the optimization of cutting parameters for polyamide (PA6) using carbide tools, utilizing a recently developed multi-objective, nature-inspired metaheuristic algorithm known as the Multi-Objective Grasshopper Optimization Algorithm (MOGOA). This optimization process’s primary objectives are minimizing surface roughness and maximizing the material removal rate. By employing the MOGOA algorithm, the study demonstrates its efficacy in successfully optimizing the cutting parameters. This research’s findings highlight the MOGOA algorithm’s capability to effectively fine-tune cutting parameters during PA6 machining, leading to improved outcomes in terms of surface roughness reduction and enhanced material removal rate.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Grasshopper Optimization Algorithm for Multi-objective Optimization of Multi-pass Face Milling of Polyamide (PA6)


    Additional title:

    Sae Int. J. Mater. Manf
    Sae International Journal of Materials and Manufacturing


    Contributors:


    Publication date :

    2023-10-30


    Size :

    8 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Visual Tracking with Levy Flight Grasshopper Optimization Algorithm

    Zhang, Huanlong / Gao, Zeng / Zhang, Jie et al. | British Library Conference Proceedings | 2019


    Hybrid Multi-Gradient Explorer Algorithm for Global Multi-Objective Optimization

    Sevastyanov, V. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2010



    Multi-Objective Aeroelastic Optimization

    Stettner, M. / Haase, W. / North Atlantic Treaty Organization | British Library Conference Proceedings | 2000


    Towards a multi-fidelity & multi-objective Bayesian optimization efficient algorithm

    Charayron, Rémy / Lefebvre, Thierry / Bartoli, Nathalie et al. | Elsevier | 2023