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Minimum Quantity Lubrication (MQL) in Automotive Powertrain Machining

Authors: Tai, Bruce L.; Stephenson, David A.; Furness, Richard J.; Shih, Albert J.;

Minimum Quantity Lubrication (MQL) in Automotive Powertrain Machining

Abstract

AbstractThis paper summarizes the advancements and challenges of minimum quantity lubrication (MQL) technology in automotive powertrain machining from both industrial and academic perspectives. MQL refers to applying a small amount of cutting fluid in the form of mist to the cutting zone rather than flooding the workpiece. Elimination of coolant systems creates significant saving from energy and equipment, the flexibility to relocate the machines, reduction of waste stream and floor space, and a cleaner and healthier work environment. Ford Motor Company has demonstrated these advantages, and currently has a total of over 400 MQL CNC machining centers in numerous global transmission and engine plants running MQL operations, with further implementation planned for new programs globally. Technical challenges to realize 100% implementation includes tool design, delivery system, chip management, and thermal related problems, particularly in high-energy density processes and difficult-to-machine metals, such as deep-hole drilling and compacted graphite iron (CGI). This paper provides a review of current status and limitations of MQL machining and highlights opportunities for research and development of the next-generation MQL technology in automotive powertrain machining.

Keywords

Powertrain manufacturing, Minimum Quantity Lubrication, Machining

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
122
Top 1%
Top 1%
Top 10%
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