Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties

Abstract

Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values.

Investigating the Influence of Tool Selection on Surface Quality in Burnished AISI 1035 Steel

“…Figure 10 (b) shows the relationship between surface roughness average and feed rate value subjected to the spring tool. However, previous studies showed that the surface roughness was decreased as the feed rate increased until it reaches a certain value then it begins to be increased again [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Furthermore, in this study increasing the feed rate is correlated with an increase in surface roughness [21].…”

“…As manufacturing tolerances progressively tighten to meet precision requirements in applications involving fluid sealing, coefficient of friction control, and miniature clearances, understanding how surface shaping methods like burnishing sculpt very near-surface geometry becomes imperative [7,8]. Previous research has also identified opportunities to optimize process parameters to reduce energy consumption during surface treatment while enhancing surface quality [9]. For instance, it has been observed that surface roughness decreases with increasing burnishing speed, reaching a minimum value before rising again at higher speeds [10].…”

Enhancing Surface Integrity and Quality through Roller Burnishing: A Comprehensive Review of Parameters Optimization, and Applications.

“…The outcome demonstrates a 90% reduction in surface roughness and a 41% improvement in microhardness. Nguyen, T. T. et al [3] innovated the goals in terms of Internal Roller Burnishing parameters by utilizing an adaptive neurobased fuzzy inference system (ANFIS). the result of the optimization process.…”

Ecological design optimization of nozzle parameters for burnishing operation

“…The energy efficiency of the internal burnishing was enhanced by 7.0%, while the noise emission and Rs were decreased by 2.2%, and 25.0% [12]. Nguyen and Le revealed that the energy consumption and Rs of the external burnishing were reduced by 33.0% and 16.0%, respectively [13]. Nguyen et al stated that the total energy, mean roughness, and roundness of the MQL-assisted internal burnishing were reduced by 12.0%, 14.0%, and 43.0%, respectively [14].…”