Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function

Chabbi, Amel; Yallese, Mohamed Athmane; Meddour, Ikhlas; Nouioua, Mourad; Mabrouki, Tarek; Girardin, François

doi:10.1016/j.measurement.2016.09.043

Cited by 105 publications

(53 citation statements)

References 19 publications

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“…The result shows the significant improvement in surface finish with the hybrid approach as compared to the Taguchi analysis. Chabbi et al [19] investigated the influence of cutting parameters on the finish of surface roughness during the cutting of the polyoxymethylene (POM C) by utilizing the response surface methodology (RMS) method. The results revealed that the surface roughness was strongly influenced by the feed rate with a large contribution, followed by the cutting depth, whereas, the cutting speed has no influence.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

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Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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“…The most critical factor that affects the output responses can also be determined with this method. Additionally, the multi-response optimization of micro-turning process parameters was performed [25]. Therefore, in this work, the optimization of cutting parameters affecting the surface roughness and MRR was performed in the micro-turning process.…”

Section: Introductionmentioning

confidence: 99%

Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

et al. 2020

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Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

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“…This is essential when implementing the experimental designs necessary for the investigation of the influence of the cutting parameters on the machining performance indicators as well as to establishing the mathematical models of prediction needed for optimization. During the last years, many researchers performed statistical studies based on the design of experiments and the response surface methodology (RSM) with the objective of determining the impact of the cutting parameters on the cutting forces and the surface roughness of the finished products, as well as deriving the necessary mathematical models of prediction needed to optimize these parameters [3][4][5]. Ghani et al [6] examined the flank wear and surface roughness during gray cast iron machining by mixed ceramic tools.…”

Section: Introductionmentioning

confidence: 99%

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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Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function

Cited by 105 publications

References 19 publications

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

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

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