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Effects of Minimum Quantity Lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: A comprehensive review

期刊

JOURNAL OF CLEANER PRODUCTION
卷 127, 期 -, 页码 1-18

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.03.146

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MQL; Nanofluids; Vegetable oil; Roughness; Machining; Cutting-fluids

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In any metal cutting operation, the cutting fluid plays a vital role by cooling the surface of the work piece and the cutting tool, removing chips from the cutting zone and by lubricating the tool work piece interface. However, misuse of the cutting fluid and wrong methods of its disposal can affect human health and the environment badly. Also, it accounts for 16-20% of the total cost of manufacturing in the production industry. Among various techniques available on application of the coolant, researchers, of late, have been focussing on Near Dry Machining (NDM)/Minimum Quantity Lubrication (MQL) as it minimizes the use of coolant by spraying the mixture of compressed air and cutting fluid in an optimized manner instead of flood cooling. The MQL technique has proved to be suitable because it complies with the requirements of 'green' machining. This paper presents a review of the important research papers published regarding the MQL-based application of mineral oils, vegetable oils and nanofluid-based cutting fluids for different machining processes, such as, drilling, turning, milling and grinding, etc. The paper explains the mechanism of the MQL technique. In a systematic manner, the present work also discusses its effect on the performance parameters of different machining processes. Most of the experimental studies have shown that application of MQL produces surface better than dry machining and similar to that as produced under wet machining. Its application also reduces cutting forces, cutting zone temperature, tool wear, friction coefficient in comparison to dry and wet machining. Therefore, MQL technique has proved to be a viable alternative to the flood lubrication under similar performance parameters. (C) 2016 Elsevier Ltd. All rights reserved.

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