Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation

Authors

  • Minh-Thai Le Le Quy Don Technical University, Faculty of Special Equipment, Vietnam
  • An Le Van Nguyen Tat Thanh University, Faculty of Engineering and Technology, Viet Nam
  • Trung-Thanh Nguyen Le Quy Don Technical University, Faculty of Special Equipment, Vietnam

DOI:

https://doi.org/10.5545/sv-jme.2022.303

Keywords:

Single diamond burnishing, Average roughness, Vickers hardness, Bayesian regularization, NSGA-G

Abstract

Diamond burnishing is an effective solution to finish a surface. The purpose of the current work is to optimize parameter inputs, including the spindle speed (S), depth of penetration (D), feed rate (f), and diameter of tool-tip (DT) for improving the Vickers hardness (VH) and decreasing the average roughness (Ra) of a new diamond burnishing process. A set of burnishing experiments is executed under a new cooling lubrication system comprising the minimum quantity lubrication and double vortex tubes. The Bayesian regularized feed-forward neural network (BRFFNN) models of the performances are proposed in terms of the inputs. The criteria importance through the inter-criteria correlation (CRITIC) method and non-dominated sorting genetic algorithm based on the grid partitioning (NSGA-G) are applied to compute the weights of responses and find optimality. The optimal outcomes of the S, D, f, and DT were 370 rpm, 0.10 mm, 0.04 mm/rev, and 8 mm, respectively. The improvements in the Ra and VH were 40.7 % and 7.6 %, respectively, as compared to the original parameters. An effective approach combining the BRFFNN, CRITIC, and NSGA-G can be widely utilized to deal with complicated optimization problems. The optimizing results can be employed to enhance the surface properties of the burnished surface.

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Published

2023-03-29

How to Cite

Le, M.-T., Le Van, A., & Nguyen, T.-T. (2023). Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation. Strojniški Vestnik - Journal of Mechanical Engineering, 69(3-4), 155–168. https://doi.org/10.5545/sv-jme.2022.303