4.7 Article

Experimentation on AISI T42 tool steel at very low

期刊

出版社

ELSEVIER
DOI: 10.1016/j.jmrt.2022.09.111

关键词

AISI T42; Low temperature; Cryogenic treatment; Hardness; Surface finish

资金

  1. European Union [847402]
  2. Enterprise Ireland [847402]
  3. National Research and Development Agency of Chile (ANID) [ANID/Fondap/15110019]

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This paper investigates the effect of cryogenic treatment on the machining performance of T42 tool on mild steel. By conducting experiments and performing analysis, significant independent variables are identified and empirical models are built to accurately predict the machining performance. The developed models show low deviation from experimental data, making them useful for determining the impact of independent variables on the responses.
Cryogenic Treatment is one of the heat treatment processes, which is used to develop the wear resistance property of tool material and remove residual stresses in Tool steels for better machining performance. This paper focuses on cryogenically treated T42 tool per-formance on mild steel as workpiece material. For investigating the machining performance, the responses considered in this work are machining time, surface roughness, thrust force, feed force, cutting force and MRR with the independent variables of spindle speed, feed rate and depth of cut. The design of Experiment concept is utilized for conducting the experi-ments with L27 orthogonal array. The significant independent variables on considered re-sponses are identified through ANOVA Table. The Effect of independent variables over responses has been found by the Response surface plot based on the identified significant parameters the empirical model is built with the aid of regression analysis. After prediction, the validation of the developed model is conducted with experimental data. The developed models are having less percentage of deviation with experimental data like Machining time-16.48%, surface roughness-0.14%, Thrust force-5.61%, Feed force-31.27%, Cutting force-15.50%, and MRR-0.11%. Hence, these models can be used to navigate independent vari-ables' values on responses within the range of independent variables considered for the experiments.

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