Gear Hobbing Predictive Model

Hobbing is one of the most efficient and often used machining process to manufacture helical and spur gears. Hobbing process is a widely preferred to make gears as it can achieve near net shape with excellent tolerances and good surface finish at faster production rates. Gear hobbing is a complex machining process which requires a detailed understanding of the interaction of processing variables, the hobbing tool design and the material removal mechanisms. A clear understanding of the cutting forces and the temperature at the interface of the hobbing tool and gear blank workpiece interface is essential to predict the cutting induced residual stresses. The hobbing tool geometry along with the axial feed rate and the rotation speed of the tool dictates the chip shape. Because of the nature of the gear hobbing process which involves progressive cuts and varying material removal rates at the entry and exit of the cutting zone, the hobbing tool typically experiences non-uniform tool wear. Hobbing tools are expensive and excessive tool wear impacts increased tool interface temperature, chip shape and the gear surface finish quality. Industry needs a finite element method process modeling system to simulate the gear hobbing process to predict the chip shape, cutting load, temperature at the interface, tool wear and the cutting induced residual stresses. Commercially available, finite element method based process modeling system, DEFORM is widely used to model machining processes as well as material deformation and heat treatment processes. At the end of phase I base part of this project, we propose to demonstrate a proof of concept predictive models for analyzing forces, temperatures and residual stresses encountered during a typical gear hobbing process. In addition, it is also proposed that we will complete a typical heat treatment process model for a gear to demonstrate the capability with in DEFORM to model diffusion based carburization process to predict case depth, phase transformation during the quenching process to predict marternsitic volume fraction and any retained austenite in the gear as well as the residual stress distribution in the gear due to the heat treatment process. At the end of phase I option part of this project, we propose to investigate tool wear models, study the suitability of application of inverse methods to calculate material flow stress data at the high strain rates and evaluate options to improve computational efficiency of the machining processes.