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Article Dans Une Revue International Journal of Industrial Engineering Computations Année : 2018

Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approach

Résumé

The wear of cutting tools remains a major obstacle. The effects of wear are not only antagonistic at the lifespan and productivity, but also harmful with the surface quality. The present work deals with some machinability studies on flank wear, surface roughness, and lifespan in finish turning of AISI 304 stainless steel using multilayer Ti(C,N)/Al2O3/TiN coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), and combined effects of two cutting parameters, namely cutting speed and feed rate on lifespan (T), are explored employing the analysis of variance (ANOVA). The relationship between the variables and the technological parameters is determined using a quadratic regression model and optimal cutting conditions for each performance level are established through desirability function approach (DFA) optimization. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. In addition, it is indicated that the cutting time is the dominant factor affecting workpiece surface roughness followed by feed rate, while lifespan is influenced by cutting speed. The optimum level of input parameters for composite desirability was found Vc1-f1-t1 for VB, Ra and Vc1-f1 for T, with a maximum percentage of error 6.38%.

Dates et versions

hal-01999581 , version 1 (30-01-2019)

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Citer

Lakhdar Bouzid, Sofiane Berkani, Mohamed Athmane Yallese, François Girardin, Tarek Mabrouki. Estimation and optimization of flank wear and tool lifespan in finish turning of AISI 304 stainless steel using desirability function approach. International Journal of Industrial Engineering Computations, 2018, pp.349-368. ⟨10.5267/j.ijiec.2017.8.002⟩. ⟨hal-01999581⟩
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