Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology

Abstract

In this study, an attempt has been made to statistically model the relationship between cutting parameters (speed, feed rate and depth of cut), cutting force components ( Fx, Fy and Fz) and workpiece absolute surface roughness ( Ra). The machining case of a martensitic stainless steel (AISI 420) is considered in a common turning process by means of a chemical vapor deposition–coated carbide tool. A full-factorial design (43) is adopted in order to analyze obtained experimental results via both analysis of variance and response surface methodology techniques. The optimum cutting conditions are achieved using mutually response surface methodology and desirability function approaches while the model adequacy is checked from residual values. The results indicated that the depth of cut is the dominant factor affecting ( Fx: 86%, Fy: 58% and Fz: 81%), whereas feed rate is found to be the utmost factor influencing surface roughness behavior ( Ra: 81%). In addition, a good agreement between the predicted and measured cutting force components and surface roughness was observed. The results are also validated experimentally by determining errors ( Fx: 6.51%, Fy: 4.36%, Fz: 3.59% and Ra: 5.12%). Finally, the ranges for optimal cutting conditions are projected for serial industrial production.

Simultaneous improvement of surface quality and productivity using grey relational analysis based Taguchi design for turning couple (AISI D3 steel/ mixed ceramic tool (Al2O3 + TiC))

“…This figure indicates that, for a given depth of cut, the surface quality is sensitive to the feed rate because the increase of this latter deteriorates quickly the surface quality. This is consistent with the conclusion of research work published by Bouzid et al (2015) where they remarked that the surface roughness (Ra) rapidly increases by increasing feed rate. However, this decrease in surface quality becomes increasingly small with lower values of the depth of cut.…”

Experimental investigation of surface roughness, flank wear, chip morphology and cost estimation during machining of hardened AISI 4340 steel with coated carbide insert

“…With feed upto 0.09 mm/rev, surface finish improves due to fact that the uncut chip thickness becomes very small which might give rise to the ploughing (led to material side flow), instead cutting of material at lower feeds relative to higher feeds, as reported by Rech & Moisan (2003) and Bartarya and Choudhury (2012). Nevertheless, more increase in feed, the contact length between the workpiece and the tool increases (Kaplan et al 2014;Hessainia et al 2015) and hence resulting in high thrust force (Nayak & Sehgal 2015) since the cutting tool has to extract more volume of material from the workpiece (Das et al 2016;Bouzid et al (2014b); Aouici et al 2014) thereby more vibration and heat generation ) resulting in high Ra values. The experimental results show that average surface roughness are low at higher depth of cut but at a slower rate for which it can be considered as less affecting parameter for surface roughness in the studied range, which could be used to improve productivity if it would not worsen the surface microstructure.…”

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

“…Xi reveals the coded variables that correspond to the studied machining parameters such as cutting speed (Vc), feed rate (f) and cutting time (t), and ε is a random experimental error. The analysis of variance (ANOVA) has been applied to check the adequacy of the developed machinability models (Bouzid et al, 2015;Berkani et al 2015;Zahia et al 2015;Keblouti et al 2017). The ANOVA table consists of sum of squares and degrees of freedom.…”