Paper 26: Survey of Factors Affecting Surface Finish

Dickinson, G.

doi:10.1243/pime_conf_1967_182_311_02

Cited by 12 publications

(7 citation statements)

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“…To this end, a number of experiments based on RSM have been carried out and linear and quadratic models have been formed to explain the relation between the parameters. Dickinson [15], Grieve et al [16], and Fischer and Elrod [17] developed a turning model in which tool nose radius and feed rate are taken into account but cutting speed is ignored. Thomas and a full factorial design, taking into account the three-level interactions between the independent variables has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Optimization of tool geometry parameters for turning operations based on the response surface methodology

2011

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Section: Introductionmentioning

confidence: 99%

Optimization of tool geometry parameters for turning operations based on the response surface methodology

2011

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“…La rugosidad superficial generada en los procesos de mecanizado ha sido estudiada desde [6] por Sata y en [7] por Dickinson. El efecto del avance de la herramienta, del radio de la punta y del ángulo del filo sobre la rugosidad superficial generada en el torneado fue descrita por Groover y nombrada «rugosidad ideal», enunciada como la mínima rugosidad que se genera en una pieza torneada [8].…”

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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“…In addition, the work hardness was also studied by Bhattacharya et al (1970) and Sundaram and Lambert (1979). The surface roughness models developed by Dickinson (1968), Grieve et al (1968), and Fischer and Elrod (1971) considered the effect of feed rate and nose radius. These models concluded that the effect of cutting speed is insignificant.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness predictive modeling: neural networks versus regression

Feng

Wang

2003

IIE Transactions

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Surface roughness plays an important role in product quality and manufacturing process planning. This research focuses on developing an empirical model for surface roughness prediction in finish turning. The model considers the following working parameters: work-piece hardness (material), feed, cutter nose radius, spindle speed and depth of cut. Two competing data mining techniques, nonlinear regression analysis and computational neural networks, are applied in developing the empirical models. The values of surface roughness predicted by these models are then compared with those from some of the representative models in the literature. Metal cutting experiments and tests of hypothesis demonstrate that the models developed in this research have a satisfactory goodness of fit. It has also presented a rigorous procedure for model validation and model comparison. In addition, some future research directions are outlined.

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Paper 26: Survey of Factors Affecting Surface Finish

Abstract: This paper discusses tool geometry, the reliability of surface measuring equipment in respect to feed/cutoff ratios and the relationship of fatigue and surface finish and other aspects of a surface which are not defined by B.S. 1134.

Cited by 12 publications

References 0 publications

Optimization of tool geometry parameters for turning operations based on the response surface methodology

Optimization of tool geometry parameters for turning operations based on the response surface methodology

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

Surface roughness predictive modeling: neural networks versus regression

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