An investigation of surface damage in the high speed turning of Inconel 718 with use of whisker reinforced ceramic tools

Zhou, Jinming; Bushlya, Volodymyr; Ståhl, Jan-Eric

doi:10.1016/j.jmatprotec.2011.09.022

Cited by 110 publications

(64 citation statements)

References 17 publications

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“…Regarding surface quality and productivity, Yadav et al (2015) and Tebassi et al (2016b) obtained that the most influencing factor on MRR is depth of cut, whereas spindle speed and depth of cut are the most influencing factors on flank wear. In the same way, at low feed rate; the tendency for built-up edge formation, is also higher than at a higher feed, due to an increase in the size of the plastic deformation area at the interface of the tool and workpiece (Zhou et al, 2012).…”

Section: Introductionmentioning

confidence: 80%

Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach

Tebassi¹,

Yallese²,

Belhadi³

et al. 2017

10.5267/j.ijiec

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Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this research work has been carried out in order to elucidate surface roughness and productivity mathematical models during turning of Inconel 718 superalloy (35 HRC) with SiC Whisker ceramic tool at various cutting parameters (depth of cut, feed rate, cutting speed and radius nose). A small central composite design (SCCD) including 16 basics runs replicated three times (48 runs), was adopted and graphically evaluated using Fraction of design space (FDS) graph, completed by a statistical analysis of variance (ANOVA). Mathematical models for surface roughness and productivity were developed and normality was improved using the Box-Cox transformation. Results show that surface roughness criterion Ra was mainly influenced by cutting speed, radius nose and feed rate, and that the depth of cut had major effect on productivity. Finally, ranges of optimized cutting conditions were proposed for serial industrial production. Industrial benefit was illustrated in terms of high surface quality accompanied with high productivity. Indeed, results show that the use of optimal cutting condition had an industrial benefit to 46.9 % as an improvement in surface quality Ra and 160.54 % in productivity MRR.

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Section: Introductionmentioning

confidence: 80%

Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach

Tebassi¹,

Yallese²,

Belhadi³

et al. 2017

10.5267/j.ijiec

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“…Powders of cBN, Si 3 N 4 , and SiCw were mixed in isopropyl alcohol using Si 3 N 4 ball mill of the Pulverisette 6 Model, (Fritsch) for 2 h. As a result five mixtures were prepared. Then powder mixtures were dried at room temperature for 12 h. All patterns were taken of the composites HPHT-sintered using a toroid-type high-pressure apparatus with 30 mm diameter of the central cavity and 2.55 cm 3 working volume of a high pressure cell [11]. Experimental mixtures were placed in toroidal gaskets and compressed between two cemented tungsten carbide anvils, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In [2] thermome-chanical properties of a SiC whiskers/Al 2 O 3 matrix composite were investigated as a function of the whisker surface quality. The paper [3] presented the results of the investigation of surface damage produced by whisker-reinforced ceramic cutting tools in the finish turning of Inconel 718 alloy. Whisker-reinforced aluminum oxide inserts are used successfully in the machining of various alloys.…”

Section: Introductionmentioning

confidence: 99%

Composites of the cBN–Si3N4 system reinforced by SiCw for turning tools

Osipov

Klimczyk

Cygan

et al. 2016

J. Superhard Mater.

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“…La utilización de bajas velocidades de corte, conduce a una baja eficiencia en la producción y consecuentemente altos costos de producción [5]. Al ser este rango improductivo en las condiciones actuales de la tecnología, es necesario determinar el comportamiento de la rugosidad superficial durante el proceso de mecanizado de alta velocidad (HSM).…”

Section: Introductionunclassified

Comparación entre redes neuronales artificiales y regresión múltiple para la predicción de la rugosidad superficial en el torneado en seco

Morales-Tamayo

Hernández

Carrera

et al. 2018

ings

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Recibido: 30-11-2017, aprobado tras revisión: 18-12-2017 Forma sugerida de citación: Morales, Y.; Zamora, Y.; Vásquez, P.; Porras, M.; Bárzaga, J.; López, R. (2018). «Com-paración entre redes neuronales artificiales y regresión múltiple para la predicción de la rugosidad superficial en el torneado en seco». Ingenius. N.• 19, (enero-junio Resumen AbstractLos métodos de regresión múltiple y redes neuronales artificiales son técnicas usadas en muchas aplicaciones de la industria. En este trabajo se utilizaron dos métodos de predicción: regresión múltiple y redes neuronales artificiales (perceptrón multicapa) con el objetivo de predecir la rugosidad superficial en el torneado en seco del acero AISI 316l. En su implementación fueron considerados varios parámetros de corte como la velocidad, el avance y el tiempo de mecanizado. Las ecuaciones obtenidas por ambos métodos fueron comparadas desarrollando un diseño factorial completo para aumentar la fiabilidad de los valores registrados de rugosidad superficial. En el análisis se puede comprobar mediante los valores de coeficientes de determinación que los modelos propuestos son capaces de predecir la rugosidad superficial. Los modelos obtenidos demuestran que la técnica de redes neuronales artificiales tiene mejor precisión que la regresión múltiple para este estudio.The simple regression and artificial neural network methods are techniques used in many industrial aplications. This work developed two models in order to predict the surface roughness in dry turning of AISI 316L stainless steel. In its implementation they were considered various cutting parameters such as cutting speed, feed, and machining time. The models obtained by both methods were compared to develop a full factorial design to increase reliability of the recorded values of roughness. The analysis can be corroborated by the values of coefficients of determination that the proposed models are able to predict for surface roughness. The obtained results show that the neural networks techniques are more accurate than the multiple regression techniques for this study.Palabras clave: acero inoxidable AISI 316L, análisis de varianza y regresión, redes neuronales artificiales, rugosidad superficial, torneado de alta velocidad.

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An investigation of surface damage in the high speed turning of Inconel 718 with use of whisker reinforced ceramic tools

Cited by 110 publications

References 17 publications

Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach

Quality-productivity decision making when turning of Inconel 718 aerospace alloy: A response surface methodology approach

Composites of the cBN–Si3N4 system reinforced by SiCw for turning tools

Comparación entre redes neuronales artificiales y regresión múltiple para la predicción de la rugosidad superficial en el torneado en seco

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