IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i5p803-d763200.html
   My bibliography  Save this article

Machining Parameters Optimization Based on Objective Function Linearization

Author

Listed:
  • Cristina Gavrus

    (Department of Engineering and Industrial Management, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Nicolae-Valentin Ivan

    (Department of Manufacturing Engineering, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Gheorghe Oancea

    (Department of Manufacturing Engineering, Transilvania University of Brasov, 500036 Brasov, Romania)

Abstract

Manufacturing process optimization is an ever-actual goal. Within this goal, machining parameters optimization is a very important task. Machining parameters strongly influence the manufacturing costs, process productivity and piece quality. Literature presents a series of optimization methods. The results supplied by these methods are comparable and it is difficult to establish which method is the best. For machining parameters optimization, this paper proposes a novel, simple and efficient method, without additional costs related to new software packages. This approach is based on linear mathematical programming. The optimization mathematical models are, however, nonlinear. Therefore, mathematical model linearization is required. The major and difficult problem is the linearization of the objective function. This represents the key element of the proposed optimization method. In this respect, the paper proposes an original mathematical procedure for calculating the part of the objective function that refers to the analytical integration of the tool life into the model. This calculus procedure was transposed into an original software tool. For demonstrating the validity of the method, a comparison is presented among the results obtained by certain optimization techniques. It results that the proposed method is simple and as good as those presented by the literature.

Suggested Citation

  • Cristina Gavrus & Nicolae-Valentin Ivan & Gheorghe Oancea, 2022. "Machining Parameters Optimization Based on Objective Function Linearization," Mathematics, MDPI, vol. 10(5), pages 1-15, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:803-:d:763200
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/5/803/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/5/803/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard W. Cottle & Mukund N. Thapa, 2017. "Linear and Nonlinear Optimization," International Series in Operations Research and Management Science, Springer, number 978-1-4939-7055-1, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nitish Das & P. Aruna Priya, 2019. "A Gradient-Based Interior-Point Method to Solve the Many-to-Many Assignment Problems," Complexity, Hindawi, vol. 2019, pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:803-:d:763200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.