A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method

Çaydaş, Ulaş; Hasçalik, Ahmet

doi:10.1016/j.jmatprotec.2007.10.024

Cited by 215 publications

(52 citation statements)

References 25 publications

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“…Both methodologies were successfully applied for surface roughness modeling, however when compared to one another, different conclusions were drawn. Çaydas and Hasçalik (2008) founded that the MRA model yielded slightly superior results for surface roughness prediction than the ANN model. On the other hand, for Asiltürk and Çunkaş (2011) and Paulo Davim et al (2008), ANN modelling offers several advantages over MRA such as simplicity, speed and modeling complex nonlinearities and interactions.…”

Section: Introductionmentioning

confidence: 92%

Basic Statistical Inference

2009

Applied Statistical Inference With MINITAB

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In this paper, empirical modeling of surface roughness in CO 2 laser cutting of mild steel using the multiple regression analysis (MRA) and artificial neural network (ANN) was presented. To cover wider range of laser cutting parameters such as cutting speed, laser power and assist gas pressure as well as to obtain experimental database for MRA and ANN model development, Taguchi's L 25 orthogonal array was implemented for experimental plan. The average surface roughness was chosen as a measure of surface quality. The mathematical models of surface roughness developed by MRA and ANN were expressed as explicit nonlinear functions of the selected input parameters. The comparison between experimental results and models predictions showed that ANN model provided more accurate predictions when compared with the MRA model. The use of MRA for surface roughness prediction in CO 2 laser cutting was of limited applicability and reliability. Powerful modeling ability of the ANNs justified the use of the ANN models for accurate modeling of the complex processes with many nonlinearities and interactions such as CO 2 laser cutting. Finally, based on the derived ANN equation, the effects of the laser cutting parameters on surface roughness were examined.

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Section: Introductionmentioning

confidence: 92%

Basic Statistical Inference

2009

Applied Statistical Inference With MINITAB

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“…The logistic activation function is used in this network. Training the BP-ANN is an iterative way where errors are minimized by backward propagation through network to update weight and thresholds between two layers [17]. The following function is used to calculate error between target output (T pk ) and calculation output (O pk ).…”

Section: B Construction Of Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

Choudhuri¹,

Sen²,

Ghosh³

et al. 2016

IJET

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H21 steel is one of the hot work tool steel, which exhibits superior red hardness, high mechanical strength and difficult-to-machine. Wire electrical discharge machining (WEDM) always demands high-speed and high-precision machining to fulfill productivity and accuracy of machining hard materials. Cutting speed determines the productivity of machining and the width of kerf determines the tolerance of finished product. Two methodologies viz. response surface method (RSM) and artificial neural network (ANN) are compared for their modeling, sensitivity analysis and optimization abilities. The predictability of ANN model is better than RSM which indicating the advantage of ANN in mapping the nonlinear behavior of the system. Finally, the ANN fitness function is integrated with particle swarm optimization (PSO) algorithm to optimize the process parameters of the WEDM process.

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“…Abrasive waterjet machining Çaydaş and Hasçalik (2008) investigated the abrasive waterjet machining process through the application of artificial neural networks and regression analysis. Using the obtained experimental data the authors developed mathematical models to predict surface roughness (R a ) using machining parameters of traverse speed (V), waterjet pressure (P), standoff distance (h), abrasive grit size (d) and abrasive flow rate (m).…”

Section: Single Objective Machining Optimization Examplesmentioning

confidence: 99%

Optimization of machining processes using pattern search algorithm

Madić¹,

Radovanović²

2014

10.5267/j.ijiec

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Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS) algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.

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A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method

Cited by 215 publications

References 25 publications

Basic Statistical Inference

Basic Statistical Inference

A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

Optimization of machining processes using pattern search algorithm

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