RMS-based optimisation of surface roughness when turning AISI 420 stainless steel

Yallese

Proceedings of the Institution of Mechanical Engineers, Part C:

et al. 2019

Self Cite

The purpose of this experimental work is to study the impact of the machining parameters ( Vc, ap, and f) on the surface roughness criteria ( Ra, Rz, and Rt) as well as on the cutting force components ( Fx, Fy, and Fz), during dry turning of martensitic stainless steel (AISI 420) treated at 59 hardness Rockwell cone. The machining tests were carried out using the coated mixed ceramic cutting-insert (CC6050) according to the Taguchi design (L25). Analysis of the variance (ANOVA) as well as Pareto graphs made it possible to quantify the contributions of ( Vc, ap, and f) on the output parameters. The response surface methodology and the artificial neural networks approach were used for output modeling. Finally, the optimization of the machining parameters was performed using desirability function (DF) minimizing the surface roughness and the cutting forces simultaneously. The results indicated that the roughness is strongly affected by the feed rate ( f) with contributions of (80.71%, 80.26%, and 81.80%) for ( Ra, Rz, and Rt) respectively, and that the depth of cut ( ap) is the factor having the major influence on the cutting forces ( Fx = 53.76%, Fy = 50.79%, and Fz = 65.31%). Furthermore, artificial neural network and response surface methodology models correlate very well with experimental data. However, artificial neural network models show better accuracy. The optimum machining setting for multi-objective optimization is Vc = 80 m/min, f = 0.08 mm/rev and ap = 0.141 mm.

Section: Resultsmentioning

confidence: 95%

Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420

Yallese

Proceedings of the Institution of Mechanical Engineers, Part C:

et al. 2019

Self Cite

“…Essentially, the approach is to translate the functions to a common scale [0, 1], combine them using the geometric mean and optimize the overall metric. DFA continue to be a commonly preferred method because it can solve multi response optimization problem by converting it into single response optimization problem which enable to sort the computational work (Bouzid et al, 2014). The concept of desirability function involves the translation of the responses from its individual desirability to scale of composite desirability (overall desirability function).…”

Section: Optimisation Of Machining Performance Using Desirability Funmentioning

confidence: 99%

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

Bouzid¹,

Berkani²,

Yallese³

et al. 2018

10.5267/j.ijiec

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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%.

Process Integr Optim Sustain

“…For example, Hessainia et al (2015) found that response surface methodology represents a powerful approach and can offer to scientific researchers as well industrial metal workers a helpful optimization procedure for various combinations of the workpiece and the cut material tool. The same technique was used by Bouzid et al (2014a) with the objective to optimization of surface roughness in turning of martensitic stainless steel AISI 420 with coated carbide tool concerning speed, feed, and depth of cut as cutting parameters. The appropriate combination and utilization, along with proper adjustment of machining parameters, are of prime importance for acquiring good quality of surface finish and grade of machined part, which generally consumes precious time and effort due to the dynamic behavior of the machining process.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation

Panda

Das

Dhupal

2017

The paper addresses the assessment, modeling, and optimization study of surface roughness in finish dry hard turning (FDHT) of AISI 4340 steel with coated ceramic tool by considering the cutting speed, axial feed, depth of cut, and nose radius as machining parameters. Thirty sets of longitudinal turning trials based on central composite (CCD) design of experiments (DOEs) are performed, and response surface methodology (RSM), particle swarm optimization (PSO), and finally, Gilbert's approach are subsequently applied for mathematical modeling, response optimization, tool life estimation, and economic analysis. Additionally, various diagnostic tests have been executed to check the statistical significance and validity, adequacy, effectiveness, and fitness of data of the proposed model using analysis of variance (ANOVA) and Anderson-Darling normal probability test. Results indicated that nose radius and feed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness is considered. The RSM model combined with PSO technique leads to minimum surface roughness value similar to cylindrical grinding of 0.2021 μm, corresponding to optimum process parameters: 220 m/min of cutting speed, 0.05 mm/rev of feed, 0.193 mm of depth of cut, and 1.6 mm of tool nose radius. Finally, under pre-cited optimum machining conditions, tool life was evaluated to perform cost analysis for the economical justification and feasibility of coated ceramic tools in hard turning. The total machining cost per piece is ensued to be lower ($0.34) as a consequence of higher tool life (44 min), reduction in downtime, and enhancement in savings, which finds economical benefits in hard turning. The novelty aspects of the present work are that (i) it demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing coated ceramic tool in hard turning processes considering technological, economical, and ecological aspects, which are helpful and efficient from industrial point of view and (ii) it contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-55 HRC.