Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

Aouici, Hamdi; Yallese, Mohamed Athmane; Chaoui, Kamel; Mabrouki, Tarek; Rigal, Jean-François

doi:10.1016/j.measurement.2011.11.011

Cited by 280 publications

(163 citation statements)

References 19 publications

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“…This procedure includes following six steps (Aouici et al, 2012). These are, (1) definition of independent input variables and the desired output responses, (2) adoption of an experimental design plan, (3) performing regression analysis with the quadratic model of RSM, (4) calculate analysis of variance (ANOVA) for the independent input variables in order to find significant parameters that affect the responses, (5) determination of the situation of the quadratic model of Response Surface Methodology and decide whether the model of RSM needs screening variables or not and finally, (6) optimization, conduction of confirmation experiments and verifying the predicted performance characteristics.…”

Section: Experimental Design 221 Response Surface Methodsologymentioning

confidence: 99%

“…Also, it was reported that workpiece hardness, cutting edge geometry and cutting speed significantly influenced the force component. Aouici et al, (2012) studied the effects of cutting speed; feed rate, depth of cut and work-piece hardness on surface roughness and force components in hard turning of AISI H11 steel using CBN tools. Mathematical models and analysis of results were performed using RSM.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and analysis of tool wear and surface roughness in hard turning of AISI D2 steel using response surface methodology

Mir¹,

Wàňi²

2018

10.5267/j.ijiec

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The present work deals with some machinability studies on tool wear and surface roughness, in finish hard turning of AISI D2 steel using PCBN, Mixed ceramic and coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters viz., cutting speed, cutting time and tool hardness on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA).The relationship(s) between input variables and the response parameters are determined using a quadratic regression model. The results show that the tool wear was influenced principally by the cutting time and in the second level by the cutting tool hardness. On the other hand, cutting time was the dominant factor affecting workpiece surface roughness followed by cutting speed. Finally, the multiple response optimizations of tool wear and surface roughness were carried out using the desirability function approach (DFA).

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Section: Experimental Design 221 Response Surface Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling and analysis of tool wear and surface roughness in hard turning of AISI D2 steel using response surface methodology

Mir¹,

Wàňi²

2018

10.5267/j.ijiec

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show abstract

“…1.6 times better than uncoated CC650 ceramic tool whereas uncoated ceramic tool was helpful for decreasing the machining force. Aouici et al [13] examined the influence of cutting speed, feed and work material hardness along with depth of cut on surface quality along with cutting force in turning of hardened AISI H11 grade steel using CBN cutting tools through RSM approach and developed mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Panda¹,

Sahoo²,

Panigrahi³

et al. 2018

INT. J. AUTOMOT. MECH. ENG.

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The existing endeavor investigates on machinability characteristics through performance measurement of flank wear, surface quality and chip morphology during finish turning of AISI 52100 bearing steel (55 ± 1 HRC) under dry environment employing carbide insert coated along with various layers (TiN/TiCN/Al2O3). Secondly the influence of machining variables viz. cutting speed, feed rate and depth of cut on responses are assessed by ANOVA and modeled through quadratic regression and artificial neural network. Multiparametric optimization of cutting conditions has been obtained through Taguchi based grey relational analysis. Finally, tool life at ideal conditions has been evaluated through experiment. Based on the study, it is disclosed that coated carbide with multilayer insert outperformed during hard machining as wear at the flank surface and surface quality are within the benchmark cap of 0.3 mm and 1.6 microns respectively. From the chip morphology analysis, multilayer coated carbide insert generates lower temperature and maintains cutting edge sharpness and delays the growth of tool wear. ANN model using multilayered feed forward network yields accurate prediction of responses with minimum error percentage compared to QR model. The optimal parametric combination through GRA approach is found to be d1 (0.1 mm)-f1 (0.04 mm/rev)-v2 (110 m/min) and is greatly improved. Feed is the compelling aspect for multi-responses pursued by cutting speed. The tool life at optimized cutting condition is found to be approximately 19 minutes.

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“…Verma et al [17] also derived through Taguchi method optimal cutting condition to obtain appreciably low surface roughness in turning ASTM A242 Type-1 alloy steel. The effect of cutting parameters and workpiece hardness on surface roughness and cutting force components in hard turning of AISI H11 steel was investigated by Aouici et al [18] using cubic boron nitride. Yadav et al [19] evaluated the relation between the change in hardness of EN 8 workpiece during turning with varying machining parameters.…”

Section: Introductionmentioning

confidence: 99%

Exploring Machinability of AISI 4340 Steel with Coated Carbide Inserts

Das¹,

Das

Mandal

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Abstract:Turning is widely employed as a material removal process for roughing or semi-finishing operation. However, turning of a workpiece material needs use of an appropriate cutting tool insert. Insert material and its geometry play quite significant role in obtaining desired machinability. For obtaining better finish of machined surface, nose radius also plays an important role. In the present work, two coated carbide cutting tool inserts with two different nose radii of 1.2 mm and 1.6 mm are used under two cutting velocity and four feed of low values to explore machinability of AISI 4340 steel during turning in dry condition. Type of chip formation, cutting forces required, etc. are noted during the experiment. The condition corresponding to good machinability is finally recommended.

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Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

Cited by 280 publications

References 19 publications

Modelling and analysis of tool wear and surface roughness in hard turning of AISI D2 steel using response surface methodology

Modelling and analysis of tool wear and surface roughness in hard turning of AISI D2 steel using response surface methodology

Investigating Machinability in Hard Turning of AISI 52100 Bearing Steel Through Performance Measurement: QR, ANN and GRA Study

Exploring Machinability of AISI 4340 Steel with Coated Carbide Inserts

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