Improvement in machinability of hardened steel by cryogenically treated carbide tools in turning

Abstract

Machining hardened steels at room temperature has historically been difficult due to the significant cutting forces generated during the process. These forces can result in increased tool wear and rough surfaces on the machined parts. One potential solution to these issues in hard turning operations is the proper implementation of cutting fluid. However, in recent years, deep cryogenic treatment (DCT) of carbide tools has emerged as a promising new solution for high-production machining. This technology has shown potential due to its ability to increase the hardness of the tools and reduce residual stresses, leading to improved performance. In addition, deep cryogenic treatment is environment friendly and cost-efficient compared to using conventional cutting fluids. In this research work, the performance of deep cryogenic treatment technique has been evaluated in turning of AISI 1040 hardened steels. Dry hard turning has been performed to compare the performance the of the treated SNMG inserts with the untreated SNMG inserts. The effects of the treatment on the response parameters i.e. surface roughness, cutting temperature, cutting force, and tool wear were evaluated based on the significant input parameters i.e. cutting speed, feed rate, depth of cut, as well as material hardness and soaking time. From the experiment result, it is evident that the application of deep cryogenic treatment in coated carbide insert has significantly improved responses. The SEM image of the microstructure of the treated SNMG inserts reveal uniform distribution of eta (η) phase carbide, along with alpha (α) and beta (β) phases, which leads to improved hardness and wear resistance. The study found that 8-hours of soaking time of the SNMG insert provided the best performance compared to 16-hours and 24-hours of soaking time. As the soaking time increases, it can lead to the formation of brittle carbides, which can reduce its toughness and make it prone to cracking or chipping during use. Finally, both ANN and RSM models have been developed for the prediction of surface roughness as a function of cutting parameters, which has been validated against the experimentally found results.