Downloads
Abstract
The effects of cutting fluids to health, environment, productivity, and quality in machining operations have been discussed. MQL is a green technology that is gradually applied in mechanical processing. The article has introduced about MQL cooling lubrication method in mechanical processing. Some previous research results have been made to clarify the meaning of this method. In addition, the comparison of the outputs of MQL, dry, and wet methods has also been shown to show the effectiveness of the MQL method. Then, based on the MQL equipment being used at Tran Dai Nghia University, the authors designed the experiment to evaluate the impact of MQL parameters on the process outputs. Machining and optimization of that parameter. This paper presents the MQL parameters optimization approach in which the multi-response outputs based on Taguchi's L9 orthogonal array method is used. During the turning AISI-1045 steel, the cutting temperature, the maximum of tool wear, and the surface roughness were measured. The MQL parameters, which are the ratio of soluble lubricant and water, pressure of spray head, the flow volume of the emulsion was simultaneously optimized by taking the multi-response outputs using Taguchi based grey relational analysis (GRA) into consideration. In turning experiments, three different flow volume of emulsion Q (40, 60, 80 ml/h), three different levels pressure of spray head P (3, 5, 7 bar) and three different levels ratio of soluble lubricant and water R (4, 6, 8%) were used. Here, three mathematical models were created using response surface regression methodology. The experiments had been done to investigate the effect of the MQL parameters to the turning process. As a result, the set of optimal MQL parameters had been pointed out to simultaneously minimize the cutting temperature, the tool wear, and surface roughness. The Flow volume of emulsion 80 ml/h, Pressure of spray head 7 bar, Ratio of soluble lubricant, and water 6% was observed to be the most effective.
Issue: Vol 3 No SI1 (2019)
Page No.: SI92-SI102
Published: Apr 12, 2020
Section: Research article
DOI: https://doi.org/10.32508/stdjet.v3iSI1.726
Download PDF = 562 times
Total = 562 times