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

Abstract

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%.

Hard turning of high-carbon high chromium tool steel using CBN tools under different lubricating/cooling conditions

“…The cutting speed (VC), cutting fluid conditions (Qc) and feed rate (f) also have significant contribution respectively, their contributions are (16.35; 5.09 and 4.43) % respectively. Similar results were reported by Bouzid et al, [17] when turning AISI 304 steel. Bouzid et al, [17] found that the cutting time and cutting speed have the major statistical significance as regards tool wear.…”

“…Similar results were reported by Bouzid et al, [17] when turning AISI 304 steel. Bouzid et al, [17] found that the cutting time and cutting speed have the major statistical significance as regards tool wear. Figure 2 shows the main influence (individual) of cutting parameters on VB.…”

“…It is concluded that feed rate influenced both surface roughness and cutting force components among all the parameters used. Similarly, modelling of machining parameters for tool wear and surface roughness has also been done by the several authors [16][17][18][19]. However, most of the research work in hard turning using any of the advanced cutting tool have be performed mainly under dry cutting conditions.…”

Tool Wear, Surface Topography, and Multi-Objective Optimization of Cutting Parameters during Machining AISI 304 Austenitic Stainless Steel Flange

“…For example, Nayak et al [17] measured material removal rate (MRR), cutting force, and surface roughness in experiments and used the grey correlation method to obtain suitable cutting parameters with these three quality characteristics as objectives. But this method could not reflect the interaction between independent variables.The response surface method (RSM) is an effective analytical method widely utilized in various fields [18]. It adopts a multivariate quadratic regression equation to fit the functional expression between values of experimental variables and response values [19][20][21].…”

“…The response surface method (RSM) is an effective analytical method widely utilized in various fields [18]. It adopts a multivariate quadratic regression equation to fit the functional expression between values of experimental variables and response values [19][20][21].…”