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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
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    References listed on IDEAS

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    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, April.
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