A neural network approach for the on-line estimation of workpiece height in WEDM

Liao, Yunn‐Shiuan; Yan, Mu-Tian; Chang, C. C.

doi:10.1016/s0924-0136(01)01252-3

Cited by 32 publications

(24 citation statements)

References 8 publications

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“…NN models compute the output as a sum of non-linear transformations of linear combinations of the inputs. To see the detailed descriptions of this type of the NN can refer to the references at the end of the paper [5][6][7][8][9].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Neural network modelling and parameters optimization of increased explosive electrical discharge grinding (IEEDG) process for large area polycrystalline diamond

Cao¹,

Zhang²

2004

Journal of Materials Processing Technology

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Section: Artificial Neural Networkmentioning

confidence: 99%

Neural network modelling and parameters optimization of increased explosive electrical discharge grinding (IEEDG) process for large area polycrystalline diamond

Cao¹,

Zhang²

2004

Journal of Materials Processing Technology

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“…According to the rule-based strategy, servo voltage and power settings can be adjusted correctly to suit the work piece profile. Experimental results demonstrate that high machining efficiency and stable machining can be achieved by means of the rule-based control strategy, Liao et al (2002).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Machining Performances Using MRA, GMDH and Artificial Neural Network in Wire EDM of EN-31

Ugrasen¹,

Ravindra²,

Prakash

et al. 2014

Procedia Materials Science

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Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation of machining performances using Multiple Regression Analysis (MRA), Group Method Data Handling Technique (GMDH) and Artificial Neural Network (ANN). Experimentation was performed as per Taguchi's L' 16 orthogonal array. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Three responses namely accuracy, surface roughness, volumetric material removal rate have been considered for each experiment. Estimation and comparison of responses was carried out using MRA, GMDH and ANN.

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“…That means a loss of performance, making it difficult to achieve the required part specifications [1,2]. Process optimization involves using complex approaches due to the large number of parameters affecting WEDM performance [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Industrial System for Estimation of Workpiece Height in WEDM

Conde¹,

Sánchez

Plaza

et al. 2015

Procedia Engineering

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The wire electrical-discharge machining process (WEDM) is a widely-used technology which can achieve the requirements needed by industry, such complex geometries and precision. Cutting process control in WEDM becomes essential in terms of surface finish and dimensional tolerances. When a cut is made in a variable workpiece thickness, the process becomes very unstable in the transition zone. The difference of energy related to the WEDM regime cannot be easily accommodated when changing to another thickness. In this work a study of the most important variables that take part in the process is presented. It has been found that the workpiece thickness determination can be accomplished from the analysis of the variable Gap error, which is built as a combination of Ionization time (Td) and Servo voltage (Servo). An industrial system has been developed to determine the workpiece thickness with less than 10% error in comparison with the nominal value of part thickness.

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A neural network approach for the on-line estimation of workpiece height in WEDM

Cited by 32 publications

References 8 publications

Neural network modelling and parameters optimization of increased explosive electrical discharge grinding (IEEDG) process for large area polycrystalline diamond

Neural network modelling and parameters optimization of increased explosive electrical discharge grinding (IEEDG) process for large area polycrystalline diamond

Estimation of Machining Performances Using MRA, GMDH and Artificial Neural Network in Wire EDM of EN-31

An Industrial System for Estimation of Workpiece Height in WEDM

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