Predictive modeling of near dry machining: Mechanical performance and environmental impact
The objective of this study is to develop a methodology to analyze the air quality and tool performance in turning process under near-dry condition. Near dry machining refers to the use of a very small amount of cutting fluid in the machining process. It was addressed in mid-1990’s in order to reduce the machining cost, to alleviate the environment impact, and to improve the product surface quality. Although previous research showed near dry machining could be an alternative technology to dry and flood-cooled machining, those studies were restricted to qualitative experimental results.
In order to implement the near dry machining technology, this dissertation develops the analytical models for both tool life and aerosol generation prediction. This research includes predictive models of cutting temperatures, cutting forces, tool wear progressions, and aerosol generation. The comparison of air quality and tool performance among dry machining process, near dry machining process, and flood cooling machining process is also presented. It is found that according to the selected cutting conditions in the model-based comparisons, the predicted cutting forces, cutting temperature and power consumption under near dry lubrication are reduced as high as about 30% compared with those in dry cutting but these predicted values are higher than those in wet cutting by about 10% under the same cutting conditions while the predicted tool wear land lengths are reduced by 60% compared with those in dry cutting but these values are higher than those in wet cutting about 1% under the same cutting conditions. However, the air quality for near dry machining with 12.5 ml/hr oil flow rate is worse than that for wet cutting due to different aerosol generation mechanisms.
After the physical behaviors in near dry turning are understood, it is possible to calculate the tool life and aerosol generation with given material properties and cutting conditions. The results of this research can support the future exploration of dimensional accuracy and cutting condition optimization.