A Hybrid Predictive Model for Cyclic Curled Chip Formation in Machining and Serrations Using Topological Properties

Project Title: A Hybrid Predictive Model for Cyclic Curled Chip Formation with Serrations in Machining Using Topological Properties Project Summary This is a proposal to develop a mathematical model for predicting, simulating and animating the cyclic chip formation process by applying the topological properties using the geometric formulation of the MAPLE program. The topological transformations are applied to multi-scale (micro, meso and macro) chip formation. This study is expected to establish the likely influencing parameters of product life in cutting tools and machined work, thus contributing to product sustainability and extended life time of involved products - cutting tool and machined product. A team of three senior researchers, specializing in machining processes, mechanics and mathematics, will partner in this project which poses a high risk challenge due the stochastic nature of the machining operation and the general complexity involved in the interactions of work and tool materials, tool geometry, and cutting conditions, along with the machine tool dynamics. The lack of predictive models for real world cyclic chip formation and break ability, despite significant advances in analytical and numerical tools, is a major impediment to improving machining productivity and attests to this complexity. It is proposed to develop a predictive model by hybridizing analytical and numerical methods for 2D chip formation in machining with coated grooved tools having finite cutting edge radii. The use of topological techniques will permit the likely void formation, growth and coalescence, thus providing the stochastic aspects to be introduced. Stresses, strains, strain-rates, temperatures, residual stresses generated on the machined surface (which contributes to the fatigue life of the machined product), and the cyclic chip loading inducing cyclic forces onto the cutting tool (which in hIm contributes to tool fai lure and progressive wear) will be predicted.