Preparation and optimization of a new type of bionic microtextured tool

Current research on microtexturing mainly focuses on designing bionic microtextures. Compared with conventional microtextures, bionic microtextures exhibit superior friction reduction performance. However, owing to significant differences between the environments of existing biological models and the cutting conditions, these textures fail to achieve optimal performance. This study focuses on the challenges of short tool life, severe wear, and poor surface quality when machining difficult-to-machine materials under minimum quantity lubrication (MQL) conditions. A bionic microtextured tool is designed based on similarity analysis of earthworm locomotion and chip formation mechanisms. Turning experiments on AISI304 stainless steel are conducted using the fabricated bionic microtextured tool under MQL conditions, and the effects of different texture parameters on cutting force, friction coefficient, surface roughness, and tool wear are analyzed. The results show that the texture distance ( L ) most significantly affects the cutting force and coefficient of friction, texture amplitude ( A ) primarily influences surface roughness, and texture cycle ( T ) most affects tool wear. The NSGA-II algorithm, combined with grey relational analysis based on weighted combinations, is used to optimize the texture parameters, resulting in the optimal combination: A = 64.54 μm, texture width ( W ) = 89.99 μm, L = 175.23 μm, and T = 400 μm. Comparative experimental results reveal that, compared to nontextured and conventional microtextured tools, the optimized microtextured tool reduces surface roughness by 35.9% and 3.36%, tool wear by 28.81% and 6.79%, cutting force by 54.23% and 2.16%, and cutting temperature by 37.34% and 17.48%, respectively. These results validate the effectiveness of the optimized microtextured tool. This study provides a solution for machining difficult-to-cut materials in the future.