Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

Garg, Girish Kant; Sangwan, Kuldip Singh

doi:10.1016/j.procir.2015.03.043

Cited by 125 publications

(54 citation statements)

References 21 publications

(24 reference statements)

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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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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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show abstract

“…There are the different predictive techniques; which were applied by various researchers aregivenbelow: [1] proposed an alternative method to conventional method for prediction of the optimum value of machining parameters that leads to minimize the surface roughness. They developed a predictive and optimization model by coupling of the two artificial intelligence approaches, one is ANN and another is genetic algorithm (GA).…”

Section: Different Predective Techniquesmentioning

confidence: 99%

Prediction of Surface Roughness on CNC Machine: A Review

Kumar¹,

Sarathe²

2016

IJERT

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In present rapidly changing scenario in production industries, application of predictive techniques is essential for metal cutting industry. It is a challenge for every industry to achieve predefined performance parameter in first trial.Predictive model is very essential in production industry to respond efficiently to severe competitiveness and continuously increasing demand of quality product with minimum cost in the market. Predictive models in metal cutting industries are considered as a vital tool for continuous improvement of product quality as well as minimizing the product cost. Optimization can be carried out with the help of predictive model. Determination of optimum input parameter for predefined performance parameter through cost-effective mathematical model is a very complex task. However, over the years, the predictive techniques have undergone various development and expansion. In this review paper, various predictive techniques like ANN, ANFIS, fuzzy logic, response surface method, taguchi method which are used for prediction of surface roughness has been critically appraised.A comparative study is carried out in tabular form of these predictive models. Surface roughness is taken as performance parameter for this paper.

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“…Nowadays, modern mechanical industries are constantly trying to design products and processes that can run faster, last longer and operate more precisely. Contemporary highperformance machines that undergo higher loads and increase moving speed of the moving parts are requiring that bearings, seals, shafts, machine guides, gears and other mechanical elements have to be dimensionally and geometrically accurate or the surface texture of the manufactured parts must be precise [1]. Therefore, the objective of machining operations is to produce mechanical elements with specified quality as productive as possible.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis and Optimization of the Controllable Parameters in Turning of en Aw-2011 Alloy; Dry Machining and Alternative Cooling Techniques

2020

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The latest trends in machining research show that great efforts are being made to understand the impact of different cooling and lubrication techniques as well as cutting parameters on machining performances. This paper presents the investigation results of different cutting parameters and different cutting environments such as dry machining, minimum quantity lubrication (MQL) and minimum quantity lubrication with compressed cold air (MQL+CCA) on average surface roughness, cutting force and material removal rate. The experiments were designed based on three input parameters and three different cutting environments when turning of EN AW-2011 alloy. Taguchi-based grey relational analysis was used to identify the optimal process parameters by which minimum values of surface roughness, minimum value of cutting force and maximum value of material removal rate will be achieved. The results showed that minimum quantity lubrication in the stream of compressed cold air, in comparison to dry and minimum quantity lubrication machining, gives the best machining performances. Therefore, the use of MQL + CCA method, which reduces the amount of lubricant may represent in the described extent of turning operations an alternative to turning processes most often carried out by wet method that causes considerable costs for purchasing, maintaining and using cutting fluids.

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Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

Cited by 125 publications

References 21 publications

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

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

Prediction of Surface Roughness on CNC Machine: A Review

Experimental Analysis and Optimization of the Controllable Parameters in Turning of en Aw-2011 Alloy; Dry Machining and Alternative Cooling Techniques

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