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<p>The failures due to wear and friction lead to a huge loss of energy across the globe, and there is an increasing need for sustainable machining. This investigation focuses on the machinability of 15 − 5 precipitation-hardening (PH) stainless steel (SS), a high-strength, corrosion-resistant alloy used in aerospace and petrochemical industries. First, graphene quantum dots (GQDs) were synthesized and characterized using FTIR, UV-Vis, and Raman spectroscopy. In the second stage, turning trials were performed on 15 − 5 PH SS under three different cutting environments (dry, cryogenic, and GQD-based minimum quantity lubrication (GQD-MQL)) to investigate the effect of machining conditions on surface roughness (Ra), cutting temperature (Tc), flank wear (Vb), and chip morphology. GQD-MQL significantly reduces friction and thermal load at the cutting area by offering superior tribological performance. The lowest Vb values are obtained when the GQD-MQL condition is employed, which enhances the surface quality of the machined face. In the third stage, machine learning (ML) models-multi-layer perceptron (MLP), K-nearest neighbor (K-NN), and random forest (RF) were applied for predicting and classifying key machining responses. Finally, in the fourth stage, a decision matrix was employed to rank each criterion across the three environments to select the most sustainable machining process.</p>

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Graphene quantum dots lubricant for precipitate-hardened steel machining: a data-driven approach for cleaner manufacturing

  • 期刊论文

  • 发表日期：

    2026年1月15日

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  • Naresh Babu Munuswamy,
  • Dinesh Babu Munuswamy,
  • Meenakshi Naresh Babu,
  • Anandan Viswanathan,
  • Neelakandapillai Lakshmanan Parthasarathi,
  • Ruby Thomas,
  • Priyanka Mishra,
  • N S Ross

摘要

The failures due to wear and friction lead to a huge loss of energy across the globe, and there is an increasing need for sustainable machining. This investigation focuses on the machinability of 15 − 5 precipitation-hardening (PH) stainless steel (SS), a high-strength, corrosion-resistant alloy used in aerospace and petrochemical industries. First, graphene quantum dots (GQDs) were synthesized and characterized using FTIR, UV-Vis, and Raman spectroscopy. In the second stage, turning trials were performed on 15 − 5 PH SS under three different cutting environments (dry, cryogenic, and GQD-based minimum quantity lubrication (GQD-MQL)) to investigate the effect of machining conditions on surface roughness (Ra), cutting temperature (Tc), flank wear (Vb), and chip morphology. GQD-MQL significantly reduces friction and thermal load at the cutting area by offering superior tribological performance. The lowest Vb values are obtained when the GQD-MQL condition is employed, which enhances the surface quality of the machined face. In the third stage, machine learning (ML) models-multi-layer perceptron (MLP), K-nearest neighbor (K-NN), and random forest (RF) were applied for predicting and classifying key machining responses. Finally, in the fourth stage, a decision matrix was employed to rank each criterion across the three environments to select the most sustainable machining process.