Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach

Sangwan, Kuldip Singh; Saxena, Shalini; Garg, Girish Kant

doi:10.1016/j.procir.2015.02.002

Cited by 135 publications

(63 citation statements)

References 19 publications

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“…However, many other previous works have focused on the determination of the optimal cutting conditions for different objectives [20][21][22][23][24][25][26][27]. Zuperl and Cus [20] described a method for optimizing multi-purpose turning cutting conditions with the help of neural networks aimed at increasing productivity and reducing costs, and providing an acceptable surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of highstrength steel featuring three factors-cutting speed V, feed rate f, and depth of cut t-on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

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Section: Introductionmentioning

confidence: 99%

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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“…Los factores relacionados con las herramientas son el material, el radio de la punta, el ángulo de ataque, la geometría de la arista de corte, la vibración de la herramienta, etc., mientras que entre las variables relacionadas con el material de la pieza de trabajo se tiene la dureza, las propiedades físicas y mecánicas, entre otras. Por otro lado, las condiciones de corte que influyen son la velocidad de corte, el avance y la profundidad [18]. La selección adecuada de los parámetros de corte y de la geometría de la herramienta resulta compleja y difícil para lograr la calidad superficial requerida [19].…”

Section: Modelos De Rugosidad Superficialunclassified

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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“…35,36 This algorithm makes a binary coding system to characterize the variables such as rotational speed (RS), tool angle (TA) and workpiece thickness (WT). All of the process variables are symbolized by a ten-bit binary equivalent.…”

Section: Optimization Of the Bushing Length Using A Gamentioning

confidence: 99%

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Rajesh¹,

Hynes²,

Kumar³

et al. 2017

Mater. Tehnol.

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Thermal drilling is a novel sheet-metal-hole-making technique that utilizes the heat produced at the interface of the rotating conical tool and workpiece in order to soften the workpiece and pierce a hole into it. In this work, experiments with thermal drilling of galvanized steel were conducted based on the Taguchi L27 orthogonal array. Significant process parameters such as rotational speed, tool angle and workpiece thickness were varied during the experimentation. In thermal drilling, the thermal-drill tool pushes aside a large amount of workpiece material to form a sleeve, which is often referred to as the bushing length. A predictive model for the bushing length was developed using a feed-forward artificial neural network based on experimental data. As the bushing length is closely associated with the tapping process, the influences of the input process parameters play a vital role in fastening galvanized steel with threaded fasteners in diverse engineering applications. The optimization problem was solved by implementing a genetic algorithm under constraint limits to maximize the bushing length. Further, a confirmation test was conducted with the intention to compare the optimum value and its corresponding bushing length predicted by the genetic algorithm. Good agreement was observed between the predicted and the experimental values. Keywords: thermal drilling, artificial neural network, genetic algorithm, galvanized steel, bushing length Termi~no vrtanje je nova, za vrtanje lukenj v plo~evino, uporabljena tehnika, ki izkori{~a toploto, proizvedeno na povr{ini vrte~e se konice orodja na obdelovancu z namenom, da ga zmeh~a in vanj naredi luknjo. V delu so bili izvedeni preizkusi na osnovi metode Taguchi L27 z ortogonalno matriko s termi~nim vrtanjem galvaniziranega jekla. Pomembni parametri postopka, kot so: hitrost vrtenja, kot orodja in debelina obdelovanca, so se med eksperimentiranjem spreminjali. Pri toplotnem vrtanju, vrtanje orodja potisne stran ve~materiala obdelovanca tako, da se tvori navarek (rokav) okoli luknje, ki se pogosto omenja kot dol`ina {ablone. Napovedni model za dol`ino {ablone, je razvit z uporabo umetne nevronske mre`e, ki temelji na znanstvenih podatkih. Ker je dol`ina {ablone precej povezana s procesom izdelave navoja, vplivi teh vhodnih procesnih parametrov igrajo klju~no vlogo pri pritrditvi galvaniziranega jekla z navojem pritrdilnih elementov v razli~nih in`enirskih aplikacijah. Problem optimizacije je bil re{en z implementacijo genetskega algoritma na podlagi omejitev za pove~anje dol`ine {ablone. Ugotovljeno je bilo dobro ujemanje med napovedano in eksperimentalno vrednostjo. Klju~ne besede: termi~no vrtanje, umetna nevronska mre`a, genetski algoritem, galvanizirano jeklo, dol`ina {ablone

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Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach

Cited by 135 publications

References 19 publications

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

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

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

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