Erosion Wear Mathematical Model for WC-Co Reinforcement Hardness Distribution in Fe-Based Alloy Matrix With Evoluted Algorithms

Authors

  • Francisco Casesnoves MSc MD  Mechanical Engineering, Tallinn University of Technology, Computational Engineering Researcher, IEEE Individual Researcher, Estonia

Keywords:

Nonlinear Optimization, Mathematical Model, Alloy Reinforcement, Simulations, Convex Optimization.

Abstract

The current mathematical models for erosion, corrosion, tribocorrosion and biotribology have become more complicated in current industrial manufacturing because of the number of compounds in the physical-chemical structure of the material—with special difficult-significance in alloy reinforced matrix with changes in hardness distribution. In previous contributions, an Integral-Differential, [Casesnoves, 2016], model was presented in its main characteristics and primary numerical algorithms. In this step-up the study is looked up towards the specific mathematical construction of the model, algorithms with numerical determinations and comments about comparative study related to other discrete models. An initial comparison between functional integral-differential and discrete models based on statistical distributions is overviewed. The experimental materials in this theoretical-practical research are Fe–Cr–Si–B self-fluxing alloy powder with WC-Co spherical reinforcement. Nonlinear computational optimization results, graphical nonlinear optimization, [Casesnoves, 2017], based on experimental laboratory data, are shown with additional simulations and graphical optimization techniques.

References

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Published

2017-08-31

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Section

Research Articles

How to Cite

[1]
Francisco Casesnoves MSc MD, " Erosion Wear Mathematical Model for WC-Co Reinforcement Hardness Distribution in Fe-Based Alloy Matrix With Evoluted Algorithms, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.336-344, July-August-2017.