Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN)

Nouioua, Mourad; Yallese, Mohamed Athmane; Khettabi, Riad; Belhadi, Salim; Bouhalais, Mohamed Lamine; Girardin, François

doi:10.1007/s00170-017-0589-2

Cited by 77 publications

(40 citation statements)

References 27 publications

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“…ANOVA results for arithmetic mean Ra are illustrated in Table 4, which indicates that feed and nose radius are the significant as well as dominant factors affecting Ra as their P value is less than 0.05 and larger F-value. This is in good agreement with previously published work (Bouzid et al 2014b;Meddour et al 2015;Nouioua et al 2017). Insert nose radius has the most important significant influence on surface finish and its contribution is 44.59%.…”

Section: Statistical Analysis On Surface Roughnesssupporting

confidence: 93%

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

Panda

Das

Dhupal

2017

Process Integr Optim Sustain

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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.

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Section: Statistical Analysis On Surface Roughnesssupporting

confidence: 93%

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

Panda

Das

Dhupal

2017

Process Integr Optim Sustain

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“…The other parameters' contributions do not exceed 1%. All these compare well with those of Nouioua et al [24]. Figure 2 illustrates the main effects representing the evolution of the two roughness criteria (Ra) and (Rz) and the tangential cutting force (Fz) in terms of the input parameters (ap, f and Vc).…”

Section: Statistical Analysis: Analysis Of Variance (Anova)supporting

confidence: 83%

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

Laouissi

Yallese

Belbah

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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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.

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“…As mentioned before, a number of researchers have focused on the development of empirical models to predict different factors during several cutting processes. Nouioua et al [13] developed ANN and RSM models related to cutting force and surface roughness during the turning process of X210Cr12 steel under dry, wet, and MQL machining. Multilayercoated tungsten carbide inserts with various nose radii were used for the experiment.…”

Section: Methodsmentioning

confidence: 99%

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

2018

SV-JME

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Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM-and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.

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Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN)

Cited by 77 publications

References 27 publications

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

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

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

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

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