Advanced MQL methods for machining processes

Abstract

Cooling and lubrication are vital during tool-based machining processes. Conventional flood cooling has been historically dominant among several cooling methods. In modern times, besides improving machining performance, the advancement and adoption of minimum quantity lubrication (MQL) technology have gained high prominence for reducing parts production cost and achieving environmental and ecological sustainability. Attempts and efforts were driven towards several major directions that include: (i) retrofitting/modifying of the existing MQL system with design changes for internal MQL delivery, (ii) design of novel MQL systems/methods for better spray, (iii) development of biodegradable base fluids for green manufacturing, (iv) inclusion of nanoadditives within MQL fluids, (v) the use of cryogenic gas for better cooling and lubrication. This chapter discusses the working principles of these advanced MQL methodologies, followed by their test results. Under the new MQL system development, an ultrasonic atomization-based cutting fluid (ACF) spray system is presented in detail with fundamental modeling, simulation, and experimental validation to explain how fluid mist droplets develop a dynamic fluid film that accesses the cutting zone and improves machining performance. Fundamental insight of this novel MQL system is transformed into meaningful scenarios of machining, such as turning and milling of a titanium alloy. Findings from this ACF spray and other novel MQL systems are summarized. Lab tests and a few industry adoptions of the abovementioned advanced designs and developments have demonstrated high potential to achieve the anticipated manufacturing outcomes. Some combinations of two or more MQL approaches (e.g., cryo-based nanoMQL, hybrid nanoadditives, etc.) have been found to further enhance overall performance. However, all the proposed advanced MQL methodologies are still under development and iteration stages, while industry adoption is limited within a few companies. This chapter concludes with key findings, limitations, and future recommendations.