PREMISE: Predictive Modeling of Environmental Impact and Tool Performance in Near Dry Turning

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This project is supported by the Product Realization and Environmental Manufacturing Innovative Systems (PREMISE) Program to develop the analytical understanding and predictive modeling capability or the quantitative planning of near dry lubrication parameters in turning operations. The targets are set on the control of air quality and tool wear in achieving environmental and productivity missions. This project utilizes machining science, fluid mechanics, heat transfer theories, and liquid atomization principles to develop a set of predictive models that estimate the aerosol concentration and the tool wear rate as functions of near dry lubrication parameters and cutting conditions. To this end, the temperature and stress distributions under mixed or boundary lubrication will be developed; the generation of cutting fluid aerosol through evaporation, splash, and dissipation mechanisms will be examined; and the volumetric wear rate of cutting tool due to adhesion, abrasion, and diffusion will be quantitatively evaluated. Experimental calibration in non-machining tests and full-range validation in machining tests will follow the theoretical development.The results of the project will provide a scientific infrastructure needed for the evaluation, quantification, and optimization of near dry machining performances. The broader impacts of the project include the extendibility of the resulting scientific understanding to support process optimization, activity based costing, and life cycle analysis of a wide range of part integrity issues, various waste concerns, and in different machining configurations. The project will also aims to integrate research into educational programs, to encourage the involvement of underrepresented student groups, and to involve the collaboration of several industry sectors, including fluid dispensing system vendors, tool manufacturers, machine makers, and technology end users. The successful completion of this study will open up opportunities for future full-scale, team-based research programs to follow.