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<p>The current situation in the automotive, aerospace, marine, and rail industry involves addressing component wear and tear. The composite materials are very appropriate for the problem’s replacement because they attain great strength, high hardness, strong wear resistance, exceptional corrosion resistance, and high impact toughness. The present investigation of C355 aluminium alloy hybrid composites (C355AHCs) shows they are utilized highly suitably for the various component preparations of the automotive, aerospace, marine, and rail industries. The C355 aluminium alloy addition with graphene (0, 2 and 4 wt.%) and boron carbide (0, 3 and 6 wt.%) hybrid nanocomposites are prepared by utilizing the selective laser melting (SLM) additive process. According to ASTM G99 rules, the pin-on-disc device was utilized to perform the wear experiment test. The optical microscope (OM) and Field Emission Scanning Electron Microscope (FESEM) to examine the characterization and worn surface analysis of the C355AHCs. The wear response was assessed via experiments carried out through pin-on-disc testing tribometer, considering applied load, sliding velocity, sliding distance, and sliding time, which were modelled by means of a Box-Behnken design approach. The novel aspect of this research work is the combination of SLM-processed C355/Gr/B<sub>4</sub>C hybrid nanocomposites with ANFIS-based tribological prediction, comparative modeling using RSM, and multi-objective optimization. From the experimental findings, it was found that the wear rate falls between 0.274&#xa0;g/min and 0.475&#xa0;g/min, whereby the applied load emerged as the most significant factor affecting the wear behaviour. The significance of the fitted regression equation via RSM and ANOVA analysis showed a level of significance (<i>P</i>-value &lt; 0.05), albeit with moderate prediction (R<sup>2</sup> = 0.576). As a means of overcoming this problem, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed, and the results were quite accurate and smooth. ANFIS showed better accuracy and smoothness than regression modelling in terms of prediction. Wear studies of worn surfaces showed that there was a shift from adhesive and abrasive wear in the base alloy to mild abrasive wear in the hybrid nanocomposites owing to the synergy of the graphene-lubricating effect and the B<sub>4</sub>Creinforcement effect. Multi-criteria optimization of wear, frictional force, and coefficient of friction through desirability analysis resulted in optimum conditions. This paper concludes that ANFIS is highly dependable in modelling tribological properties and that the produced SLM C355/Gr/B<sub>4</sub>C hybrid nanocomposites have promising application in automobile brakes and other engineering fields.</p>

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ANFIS-based prediction and optimization of tribological performance in graphene–boron carbide reinforced C355 aluminium alloy hybrid nanocomposites

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月13日

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  • Suresh Vellingiri,
  • Jarpula Naresh,
  • Ananda Guddenahalli Kullegowda,
  • Parthiban Settu,
  • Kunnathur Periyasamy Yuvaraj,
  • Sreenivasulu Bezawada,
  • Arundeep Murugan,
  • Sudha Dukkipati

摘要

The current situation in the automotive, aerospace, marine, and rail industry involves addressing component wear and tear. The composite materials are very appropriate for the problem’s replacement because they attain great strength, high hardness, strong wear resistance, exceptional corrosion resistance, and high impact toughness. The present investigation of C355 aluminium alloy hybrid composites (C355AHCs) shows they are utilized highly suitably for the various component preparations of the automotive, aerospace, marine, and rail industries. The C355 aluminium alloy addition with graphene (0, 2 and 4 wt.%) and boron carbide (0, 3 and 6 wt.%) hybrid nanocomposites are prepared by utilizing the selective laser melting (SLM) additive process. According to ASTM G99 rules, the pin-on-disc device was utilized to perform the wear experiment test. The optical microscope (OM) and Field Emission Scanning Electron Microscope (FESEM) to examine the characterization and worn surface analysis of the C355AHCs. The wear response was assessed via experiments carried out through pin-on-disc testing tribometer, considering applied load, sliding velocity, sliding distance, and sliding time, which were modelled by means of a Box-Behnken design approach. The novel aspect of this research work is the combination of SLM-processed C355/Gr/B4C hybrid nanocomposites with ANFIS-based tribological prediction, comparative modeling using RSM, and multi-objective optimization. From the experimental findings, it was found that the wear rate falls between 0.274 g/min and 0.475 g/min, whereby the applied load emerged as the most significant factor affecting the wear behaviour. The significance of the fitted regression equation via RSM and ANOVA analysis showed a level of significance (P-value < 0.05), albeit with moderate prediction (R2 = 0.576). As a means of overcoming this problem, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed, and the results were quite accurate and smooth. ANFIS showed better accuracy and smoothness than regression modelling in terms of prediction. Wear studies of worn surfaces showed that there was a shift from adhesive and abrasive wear in the base alloy to mild abrasive wear in the hybrid nanocomposites owing to the synergy of the graphene-lubricating effect and the B4Creinforcement effect. Multi-criteria optimization of wear, frictional force, and coefficient of friction through desirability analysis resulted in optimum conditions. This paper concludes that ANFIS is highly dependable in modelling tribological properties and that the produced SLM C355/Gr/B4C hybrid nanocomposites have promising application in automobile brakes and other engineering fields.