Traditional cutting fluids pose sustainability challenges, with high flow rates, limited high-speed effectiveness, and contribute to 7–17% of product cost. Minimum Quantity Lubrication (MQL) emerges as a viable alternative, though its phenomenological effects on peck drilling require further research. In this study, Oxley’s orthogonal cutting model is extended to oblique peck drilling, including Johnson–Cook’s material characterization. Moving infinitesimal heat sources are adopted to study heat partitioning. Applied to 48 experiments, varying MQL air/oil flow rates, cutting/feed speeds, the model predicts cutting torque, thrust force and workpiece temperature, providing insights into the cutting phenomenology. Correlations were found between cutting parameters and friction angle. Oil flow reduces friction, whereas air flow slightly decreases the heat transfer to the workpiece. This study supports MQL process parameter optimization and enables tool and workpiece temperature prediction.

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An Innovative Mechanistic-Thermal Model for Studying the Effect of MQL in Peck Drilling

  • Luca Bernini,
  • Paolo Albertelli,
  • Michele Monno

摘要

Traditional cutting fluids pose sustainability challenges, with high flow rates, limited high-speed effectiveness, and contribute to 7–17% of product cost. Minimum Quantity Lubrication (MQL) emerges as a viable alternative, though its phenomenological effects on peck drilling require further research. In this study, Oxley’s orthogonal cutting model is extended to oblique peck drilling, including Johnson–Cook’s material characterization. Moving infinitesimal heat sources are adopted to study heat partitioning. Applied to 48 experiments, varying MQL air/oil flow rates, cutting/feed speeds, the model predicts cutting torque, thrust force and workpiece temperature, providing insights into the cutting phenomenology. Correlations were found between cutting parameters and friction angle. Oil flow reduces friction, whereas air flow slightly decreases the heat transfer to the workpiece. This study supports MQL process parameter optimization and enables tool and workpiece temperature prediction.