Effect of Deep Cryogenic treatment on AISI A8 Tool steel & Development of Wear Mechanism maps using Fuzzy Clustering

International Conference on Recent Advances in Materials & Manufacturing Technologies

Pillai, Nandakumar^1 ; Karthikeyan, R.^1

Department of Mechanical Engineering, BITS Pilani, Dubai Campus, United Arab Emirates^1

关键词: Coefficient of frictions;    Conventional treatments;    Deep cryogenic treatment;    Fuzzy C means clustering;    Mechanical and tribological properties;    Microstructural changes;    Probabilistic neural network models;    Wear mechanisms;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/346/1/012006/pdf
DOI  :  10.1088/1757-899X/346/1/012006

来源: IOP

【 摘 要 】

Tool steels are widely classified according to their constituents and type of thermal treatments carried out to obtain its properties. Viking a special purpose tool steel coming under AISI A8 cold working steel classification is widely used for heavy duty blanking and forming operations. The optimum combination of wear resistance and toughness as well as ease of machinability in pre-treated condition makes this material accepted in heavy cutting and non cutting tool manufacture. Air or vacuum hardening is recommended as the normal treatment procedure to obtain the desired mechanical and tribological properties for steels under this category. In this study, we are incorporating a deep cryogenic phase within the conventional treatment cycle both before and after tempering. The thermal treatments at sub zero temperatures up to -195°C using cryogenic chamber with liquid nitrogen as medium was conducted. Micro structural changes in its microstructure and the corresponding improvement in the tribological and physical properties are analyzed. The cryogenic treatment leads to more conversion of retained austenite to martensite and also formation of fine secondary carbides. The microstructure is studied using the micrographs taken using optical microscopy. The wear tests are conducted on DUCOM tribometer for different combinations of speed and load under normal temperature. The wear rates and coefficient of friction obtained from these experiments are used to developed wear mechanism maps with the help of fuzzy c means clustering and probabilistic neural network models. Fuzzy C means clustering is an effective algorithm to group data of similar patterns. The wear mechanisms obtained from the computationally developed maps are then compared with the SEM photographs taken and the improvement in properties due to this additional cryogenic treatment is validated.