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Published on: March 29, 2018
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Real-Time Adaptive Nanofluid-Based Lubrication in Stainless Steel Turning Using an Intelligent Auto-Tuned MQL System
Mahip Singh1,2, Amit Rai Dixit2, Anuj Kumar Sharma1,3
1Innovation Hub UP, Dr. APJ Abdul Kalam Technical University, Lucknow 226031, India.
Materials (Basel, Switzerland)
|October 29, 2025
Summary
This study introduces an adaptive lubrication system for stainless steel turning, significantly improving surface quality and reducing cutting forces by intelligently adjusting nanofluid concentration and flow rate in real-time.
Area of Science:
- Manufacturing Engineering
- Materials Science
- Tribology
Background:
- Machining stainless steel faces challenges with tool life, surface finish, and environmental impact due to dynamic cutting conditions.
- Conventional Minimum Quantity Lubrication (MQL) systems lack adaptability, failing to optimize lubrication under fluctuating process loads.
- Existing MQL systems offer fixed flow rates, limiting effectiveness in real-time process adjustments.
Purpose of the Study:
- To develop and validate an ambient-aware adaptive Auto-Tuned MQL (ATM) system for enhanced machining performance.
- To dynamically control nanofluid concentration and lubricant flow rate based on real-time sensor feedback.
- To improve surface quality, reduce cutting forces, and enhance sustainability in stainless steel turning.
Main Methods:
- Implementation of an ATM system with embedded sensors monitoring cutting temperature, surface roughness, and ambient conditions.
- Development of a feedback-driven control algorithm for real-time optimization of lubrication delivery.
- Experimental validation using Taguchi L9 design for AISI 304 stainless steel turning, varying feed rate, cutting speed, and nanofluid concentration.
Main Results:
- The ATM system reduced surface roughness by over 50% and cutting force by approximately 20% compared to conventional MQL.
- High predictive accuracy was achieved with regression models (R-squared > 99%).
- Surface analyses indicated reduced adhesion and wear under the adaptive lubrication strategy.
Conclusions:
- The proposed ambient-aware adaptive MQL system offers a robust solution for intelligent, real-time lubrication control in machining.
- Dynamic optimization of nanofluid concentration and flow rate significantly enhances machining outcomes and sustainability.
- The ATM system effectively addresses limitations of conventional MQL systems in fluctuating process environments.
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