Tool segmentation design method of hybrid optimization framework with geometric modeling-finite element-genetic algorithm

Abstract

The contact area between the turning tool and the chip (workpiece) experiences friction, normal composite forces, and high temperatures under severe working conditions. Optimizing the tool geometry at the contact point with the chip is crucial for enhancing the comprehensive cutting performance of the turning tool. This study proposes a tool segmentation optimization design method within a hybrid optimization framework. Initially, a three-segment parametric geometric model of the tool, comprising the rake face shape, transition zone length, and tool edge radius, is constructed. Subsequently, a two-dimensional cutting simulation model based on thermal-mechanical coupling is developed. Utilizing Python, the model inputs and data response outputs from the ABAQUS cutting simulation process are redeveloped to facilitate direct, automatic iterative optimization of the tool structure using a genetic algorithm. The study explores the impact of varying cutting thicknesses on the optimal rake face shape, revealing that increased feed rates expand the optimization potential for minimizing cutting forces. The methodology was applied to the design of a cemented carbide turning tool for H13 steel, and comprehensive cutting performance tests were conducted. The findings indicate that the optimized tool significantly reduces temperatures and strain in the shear and friction zones, diminishes plastic deformation of the chip, and cuts the cutting force by approximately 8 %. Additionally, it lowers the adhesion of workpiece material on the rake face, reduces the contact area between the tool and the chip, and improves the workpiece's surface finish. The proposed method can provide a new automatic optimization design framework for the effective upgrading of the turning tool structure of traditional difficult-to-cut materials and the efficient development of the turning tool structure of new materials.