Comparative study of the performance of coated and uncoated silicon nitride (Si3N4) ceramics when machining EN-GJL-250 cast iron using the RSM method and 2D and 3D roughness functional parameters

Abstract

This paper presents a comparative study of both the surface roughness (Ra and Rz) and the tangential cutting force (Fz) achieved during the machining of EN-GJL-250 cast iron by uncoated and coated silicon nitride ceramics (Si 3 N 4). Experimental tests were planned according to a L 27 Taguchi design. Both the surfaces of response (RSM) and analysis of variance (ANOVA) methods were applied to determine and classify the cutting parameters affecting the surface roughness and cutting forces and for deriving the mathematical models to be used in the optimization stage when implementing the desirability function. Moreover, in order to localize the surface defects in the machined profiles, 2D and 3D topographic analysis based on the Abbott-Firestone curve was employed. The results obtained show that the surface roughness is largely influenced by both the feed rate and the cutting speed, while the depth of cut is the factor that most influenced the cutting force followed by the feed rate. Furthermore, the coated ceramic tool demonstrates a better surface quality and lower cutting force than those obtained by uncoated ceramic. The 3D topographic analysis indicates the generated surfaces as constituted by numerous interrupted ridges. The wear tests show that the CC1690 ceramics are more efficient than the CC6090 ceramics in terms of cutting force, surface roughness and wear resistance.

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

“…This can be explained by the presence of a high proportion of carbon (+3%) in the form of flake graphite that weakens the cast iron structure and creates damaged grooves. 11 Also, the 3D surface topography obtained by the CC6050 mixed ceramic (Figure 16(a)) shows the presence of higher profile peaks compared to the surfaces generated by both the CC1690 silicon nitride (Figure 16(b)) and the CC620 ceramic (Figure 16(c)). On the other hand, the surface topography produced by CBN7050 (Figure 16(d)) shows the shortest peaks and the lowest roughness values.…”

“…Figures 9-12 illustrate the 3D response surfaces corresponding to (Ra, Fz, Pc, and Ecs) draw from the equations ( 8)- (11). The plots of these 3D curves made it possible to visualize the impact of each input factor on the responses studied.…”

Monitoring and optimization of machining process when turning of AISI316L based on response surface methodology, artificial neural network and desirability function.

“…In DF, the operating conditions who have highest desirability value is considered as the optimal solution [27]. The equations to calculate the desirability are substantiated in Refs [24,28]. The asked objects and the variation interval of cutting parameters for optimization and its optimal result chosen are summarized in tables 8 and 9, respectively.…”

Monitoring and Optimization of the Machining Process When Turning of AISI 316L Based on RSM-DF and ANN-NGSAII Approaches.

“…In DF, the operating conditions who have highest desirability value is considered as the optimal solution [32] . The equations to calculate the desirability are substantiated in Refs [29,33] .…”