A comprehensive review of electric discharge machining of advanced ceramics

Solvability and optimal controls of impulsive Hilfer fractional delay evolution inclusions with Clarke subdifferential

Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: Modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN)

In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece mate...

https://journals.sagepub.com/doi/pdf/10.1177/0954406219873932

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

The use of cryogenic coolants has emerged as an environmentally conscious alternative to emulsion coolant options. Cryogenic media can be delivered with a variety of methods to the cutting edge and they can be used in combination with other traditional coolant options such as Minimum Quantity Lubrication (MQL) and compressed air cooling in order to aid dissipation of heat generated in the cutting zone and maximize the lubrication of the cutting edge and thus prolong tool life. This study focuses on the investigation of tool life when milling aerospace grade titanium (Ti-6Al-4 V) under different coolant delivery options. Tool wear progression was recorded for the following coolant options: cryogenic CO 2 , emulsion flood cooling, dry machining, cryogenic CO 2 combined with air or MQL as well as MQL alone.

Investigation of the influence of CO2 cryogenic coolant application on toolwear

A comparative study is undertaken in terms of the surface roughness criterion (Ra), the tangential cutting force (Fz), the cutting power (Pc), and the material removal rate (MRR) in turning of EN-GJL-250 cast iron using both coated and uncoated silicon nitride ceramics (Si3N4). The experimental procedure is carried out according to L27 Taguchi design process, and the analysis of variance ANOVA approach used to identify the cutting parameters that most influence the responses gathered. The artificial neural network approach (ANN) and the response surface methodology (RSM) were adopted to developing the mathematical prediction models applied in the optimization procedure that used genetic algorithm (GA). The predictive capabilities of the ANN and RSM models were further compared in terms of their mean absolute deviation (MAD), mean absolute error in percent (MAPE), mean square error (RMSE), and coefficient of determination (R2). It has been found that the ANN method provides more precise results compared to those of the RSM approach. Moreover, the coated ceramic tool has been found to lead to a better surface quality and a minimum cutting force compared to those obtained by uncoated ceramic. The wear tests undertaken show that, when the flank wear reaches the admissible value of [Vb] = 0.3 mm, the ratios (tool life CC1690/tool life CC6090), (RaCC1690/RaCC6090), and (FzCC1690/FzCC6090) are found to equal 0.88, 1.4, and 0.94, respectively.

Investigation, modeling, and optimization of cutting parameters in turning of gray cast iron using coated and uncoated silicon nitride ceramic tools. Based on ANN, RSM, and GA optimization

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 (Si3N4). Experimental tests were planned according to a L27 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.

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

The current work examines the performance of Minimum Quantity Lubrication when turning of EN-GJL-250 cast iron compared to dry and wet cooling methods. The Taguchi design L36 has been chosen for the planification of experimentation. Then, ANOVA has been established after data acquisition in order to define the effect of cutting conditions such as the used inserts, cutting depth, feed rate and cutting speed on the studied factors. Furthermore, the surface roughness has been deeply studied using 3D roughness topography to evaluate the MQL effect. Finally, the approach ANN-MOALO was found to be helpful for future industrial applications for predicting part quality and power consumption with accurate results and optimizing cutting parameters that helps to achieve the best production control.

Machinability study and ANN-MOALO-based multi-response optimization during Eco-Friendly machining of EN-GJL-250 cast iron

In the current work, an experimental study has been carried out in order to evaluate the influence of cutting settings on surface roughness and tangential force, when turning of X210Cr12 steel using coated carbide insert with various nose radius. The ANOVA analysis has been performed to determine the effect of cutting conditions on studied outputs. The experimental data have been analyzed using S/N ratios, mean effect graphs, and 3D response surface plots. The results indicate that the cutting insert nose radius and the feed rate are the mainly affecting factors on surface roughness, while tangential force is affected principally by depth of cut followed by feed rate. Confirmatory experiments have been established after Taguchi’s optimization. Mathematical prediction models have been developed using artificial neural network (ANN), and the multi-objective GWO algorithm was integrated for multi-objective optimization of Ra and Fz. It has been found that the cutting force is largely affected by the cutting depth with contribution and feed rate. Also, low feeds and cutting inserts with a large nose are useful for finishing process where low roughness is desired. Regarding the cooling mode, the minimum quantity lubrication is an interesting way to minimize lubricant quantity and protect operator health and environment with keeping better machining quality.

Aissa Laouissi

Papers

Investigation, modeling, and optimization of cutting parameters in turning of gray cast iron using coated and uncoated silicon nitride ceramic tools. Based on ANN, RSM, and GA optimization

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

Machinability study and ANN-MOALO-based multi-response optimization during Eco-Friendly machining of EN-GJL-250 cast iron

Multi-response optimization using artificial neural network-based GWO algorithm for high machining performance with minimum quantity lubrication

Tool wear, 3D surface topography, and comparative analysis of GRA, MOORA, DEAR, and WASPAS optimization techniques in turning of cold work tool steel