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))

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: Introductionmentioning

confidence: 99%

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

“…An ANN is a data processing and modeling technique works similarly to the human brain, ANNs are very efficient on adaptation and learning and capable to model the highly nonlinear processes. for this reason, they are used as modeling tools in a number of applications [15,16,20,22] .…”

Section: Ann Modelingmentioning

confidence: 99%

“…Nouioua et al [20] , Kilickap et al [21] , Zerti et al [22] developed ANN and RSM models to predict outputs parameters, the authors found that artificial neural network models show better accuracy than response surface methodology with low error, which justifies the conclusion that concerns the high precision of the ANN compared to the RSM.…”

Section: Introductionmentioning

confidence: 97%

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

benkhelifa

Nouioua²,

Cherfia

2022

Preprint

Stainless steels have gained much attention to be one of the most widely used metallic due to their high mechanical properties, corrosion resistance in moderately corrosive environments and their ability to use in biomedical devices, food industry and implants in human body. However, owing to their low thermal conductivity and high ductility, these materials are classed as materials difficult to machine. Therefore, the object of this experimental study is to investigate the effect of cutting parameters such as cutting speed (Vc), feed rate (f) and depth of cut (ap) on the machining outputs including surface roughness (Ra), cutting temperature (TC) and tool flank wear (VB) during dry turning of AISI 316L using coated carbide insert TP2501. The experiments were conducted according to Taguchi L27 orthogonal array parameter design, response surface methodology (RSM) and artificial neural network (ANN) have been used. Statistical analysis revealed that the feed rate affected for surface roughness, depth of cut the dominant factor impacted for cutting temperature, and tool flank wear mainly influenced by Vc, f and ap, respectively. The prediction results obtained by ANN and RSM models showed a good agreement with experimental data. However, ANN models proved the capability to provide more accurate results compared to RSM models. According to optimization analysis based on desirability function (DF) and non-dominated sorting genetic algorithm (NSGA II), DF results were determined to acquire high machined part quality.

“…It can be used for optimizing material removal rate, electrode wear and surface roughness responses in PMEDM (Tripathy & Tripathy, 2017). Material removal rate (MRR), surface roughness and fractal dimension are also responses to optimize when machining AISI D2 tool steel (Prabhu & Vinayagam, 2016;Zerti et al, 2018). The results showed that voltage has the strongest effect (42.42 %) and pulse on time has the smallest effect (11.13%).…”

Section: Introductionmentioning

confidence: 99%

“…Besides, many other methods have also been used as Artificial neural network, Response surface methodology, and Taguchi's combination with other methods (GRA, MOORA-PCA, VIKOR, multiple response signal-to-noise, weighted signal-to-noise,...) for multi-target optimization in EDM (Tirumala et al, 2018;Munmun & Kalipad, 2017;Bhaumik & Maity, 2017;Nayaka et al, 2017;Munmun & Kalipada, 2017;Dey & Chakraborty, 2015). However, Taguchi-TOPSIS and Taguchi-GRA are the most commonly used (Kumar et al, 2018;Zerti et al, 2018;Mohapatraa et al, 2017). Dastagiri et al (2016) have shown that TOPSIS-Taguchi is more effective than Taguchi-GRA in multi-response optimization of PMEDM.…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization of process parameters for powder mixed electro-discharge machining according to the surface roughness and surface micro-hardness using Taguchi-TOPSIS

Huu¹,

Banh²,

Bui³

et al. 2018

10.5267/j.ijdns

In this study, the efficiency of integration between Taguchi and TOPSIS in multi-response optimization of powder mixed electrical discharge machining (PMEDM) process was evaluated. The input parameters, such as workpiece and tool electrode material, polarity, pulse on time (ton), pulse off time (toff), Current (I) and powder concentration have been selected to optimize two responses; namely surface roughness (Ra) and surface hardness (HV). The results show that titanium powder mixed dielectric fluid improves multi-response optimization efficiency in PMEDM. In addition, machining conditions, such as tool electrode material, powder concentration, pulse on time, polarity, current density, A×G and B×G interactions play a very important role on S/N ratio of C* whereby powder concentration has the strongest influence. TOPSIS-Taguchi is a potential method for multi-response optimization in PMEDM. However, the optimal results using ANOVA analysis show that there is a necessity to have more studies in TOPSIS-Taguchi to improve the integration efficiency between two methods for optimizing multiple responses in PMEDM.