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

Laouissi, Aissa; Yallese, Mohamed Athmane; Belbah, Ahmed; Belhadi, Salim; Haddad, A.

doi:10.1007/s00170-018-2931-8

Cited by 74 publications

(31 citation statements)

References 39 publications

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“…The optimal machining parameters v, a, f, and r were selected to obtain the improvements in the power factor (PF), energy consumption (EC), and SR for the dry machining of a stainless steel 304 [6]; and the results indicated that EC and SR were decreased by 34.85% and 57.65%, respectively, while the PF was enhanced by 28.83%. Laouissi et al (2019) proposed the solutions to predict the tangential cutting force (Fz), cutting power (Pc), the MRR, and SR for the turning process of the cast iron, based on the ANN and RSM models [7]; and the authors emphasized that the ANN model could be applied to provide higher precision, as compared to the RSM one. Awale et al (2020) applied the GRA model to obtain the improvements in the machining force (Fc), machining temperature (MT), SR, and MRR [8]; and the outcomes indicated that the optimal values of the r, v, f, and a were 1.2 mm, 450 m/min, 0.05 mm/rev, and 0.2 mm, respectively.…”

Section: Introductionmentioning

confidence: 99%

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

Nguyen

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The burnishing process is used to enhance the machining quality via improving the surface finish, surface hardness, wear-resistance, fatigue, and corrosion resistance, and it is mostly used in aerospace, biomedical, and automotive industries to improve reliability and performance of the component. The combined turning and burnishing process is therefore considered as an effective solution to enhance both machining quality and productivity. However, the trade-off analysis between energy consumption, surface characteristics, and production costs has not been well-addressed and investigated. This study presents an optimization of the compressed air assisted-turning-burnishing (CATB) process for aluminum alloy 6061, aimed to decrease the energy consumption as well as surface roughness and to enhance the Vicker hardness of the machined surface. The machining parameters for consideration include the machining speed, feed rate, depth of cut, burnishing force, and the ball diameter. The improved Kriging models were used to construct the relations between machining parameters and the technological response characteristics of the machined surface. The optimal machining parameters were obtained utilizing the desirability approach. The energy based-cost model was developed to assess the effectiveness of the proposed CATB process. The findings showed that the selected optimal outcomes of the depth of cut, burnishing force, diameter, feed rate, and machining speed are 0.66 mm, 196.3 N, 8.0 mm, 0.112 mm/rev, and 110.0 m/min, respectively. The energy consumption and surface roughness are decreased by 20.15% and 65.38%, respectively, while the surface hardness is improved by 30.05%. The production cost is decreased by 17.19% at the optimal solution. Finally, the proposed CATB process shows a great potential to replace the traditional techniques which are used to machine non-ferrous metals.

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Section: Introductionmentioning

confidence: 99%

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

Nguyen

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…The effect of input variables (spindle speed, feeding speed, and cutting depth) on surface roughness during hard turning of AISI 1045 steel using YT5 tool investigated by Xiao et al 16 This study confirms that the feed rate has a great influence on surface roughness compared to the other two variables. Laouissi et al 17 also compared surface roughness, tangential cutting force, cutting power, and material removal rate (MRR) in turning of EN-GJL-250 cast iron using coated and uncoated silicon nitride ceramics. And the coated ceramic tool obtained a better surface quality and a minimum cutting force.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the effect of tool geometry on tool wear and surface roughness in hard turning

Duc

Giang

Dai

et al. 2020

Advances in Mechanical Engineering

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The main purpose of this study is to investigate the influence of tool geometry (cutting edge angle, rake angle, and inclination angle) and to optimize tool wear and surface roughness in hard turning of AISI 1055 (52HRC) hardened steel by using TiN coated mixed ceramic inserts. The results show that the inclination angle is the major factor affecting the tool wear and the surface roughness in hard turning. With the increase in negative rake and inclination angles, the tool wear decreases, and the surface roughness increases. However, the surface roughness will decrease when the inclination angle increases to overpass a certain limit. This is a new and significant point in the research of the hard turning process. From this result, the large negative inclination angle (λ = −10°) should be applied to reduce the surface roughness and the tool wear simultaneously. With the optimal cutting tool angles in the research, the hard machining process is improved remarkably with decreases of surface roughness and tool wear 8.3% and 41.3%, respectively in comparison with the standard tool angles. And the proposed tool-post design approach brings an effective method to change the tool insert angles using standard tool-holders to improve hard or other difficult-to-cut materials turning quality.

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“…In modelling and optimization of the machining parameters in the turning process, the authors of paper [4] proposed a model with the following input parameters: tangential cutting force, cutting power and the material removal rate, and with surface roughness as the output parameter. In experimental part of collecting data during the turning process, coated and uncoated silicon nitride ceramic tools were used while for the process of prediction the approach using neural networks and response surface methodology (RSM) was applied.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks

et al. 2020

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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

Cited by 74 publications

References 39 publications

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

An experimental study on the effect of tool geometry on tool wear and surface roughness in hard turning

Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks

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