KSEF R&D Excellence: Predictive Models for Dry Machining for Use in Automotive Manufacturing Industry

Machining processes constitute a significant proportion of the cost of manufacturing operations. It has been estimated that the total expenditure on machining in developed countries accounts for about 5 percent of the GDP, while in the US, nearly $100 billion is spent annually in such operations. Automotive, aerospace and other major manufacturing industrial sectors along with the defense industry are all very heavily involved in machining. Approximately 5000 manufacturers operate in the state of Kentucky. The metals-related manufacturing industrial sectors produce the largest GNP of the state of Kentucky with metal machining as a major industrial subgroup. A significant proportion of industrial growth is contributed by the aluminum industry. Also, at a national level, the U.S. automotive group (The Big Three) in their PNGV (Partnership for Next Generation Vehicles) initiative has recently embarked on a new project on "Dry Machining of Aluminum". With increasing concern for "environmentally conscious manufacturing", dry machining of aluminum alloys, which is aimed at eliminating the use of coolants, has become a major research focus. The proposed project is aimed at conducting an advanced study to understand the effects of cutting conditions, tool geometry and work-tool interactions in machining operations (turning and milling) and developing reliable and effective predictive models of chip formation in dry machining of aluminum alloys. Since these machining operations are associated with large strains, strain-rates, and high temperatures, it is proposed to develop a hybrid model by combining analytical and numerical methods, coupled with experimentally validated data, and then to develop predictive models for machining performance measures such as cutting forces, chip-forms/chip breakability, surface roughness, etc. The recently developed universal slip-line model for machining will be integrated into a thermo-viscoplastic finite element model to form a hybrid model of chip formation. Machining parameters (such as cutting forces and temperature distributions in the plastic deformation region) will be predicted from the hybrid model for dry machining conditions.