Neural network modeling for prediction of weld bead geometry in laser microwelding

Mohd Idris Shah Ismail, Yasuhiro Okamoto, Akira Okada

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model.

Original languageEnglish
Article number415837
JournalAdvances in Optical Technologies
DOIs
Publication statusPublished - 2013

Fingerprint

beads
Welds
Neural networks
Geometry
Lasers
geometry
predictions
lasers
Steel sheet
automation
productivity
Backpropagation
Regression analysis
Joining
Industrial applications
regression analysis
mathematical models
education
Automation
Productivity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

Cite this

@article{20ea9b9fb4b4461fa51d0e4c3fd9214b,
title = "Neural network modeling for prediction of weld bead geometry in laser microwelding",
abstract = "Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model.",
author = "Ismail, {Mohd Idris Shah} and Yasuhiro Okamoto and Akira Okada",
year = "2013",
doi = "10.1155/2013/415837",
language = "English",
journal = "Advances in Optical Technologies",
issn = "1687-6393",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Neural network modeling for prediction of weld bead geometry in laser microwelding

AU - Ismail, Mohd Idris Shah

AU - Okamoto, Yasuhiro

AU - Okada, Akira

PY - 2013

Y1 - 2013

N2 - Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model.

AB - Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model.

UR - http://www.scopus.com/inward/record.url?scp=84893693443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893693443&partnerID=8YFLogxK

U2 - 10.1155/2013/415837

DO - 10.1155/2013/415837

M3 - Article

AN - SCOPUS:84893693443

JO - Advances in Optical Technologies

JF - Advances in Optical Technologies

SN - 1687-6393

M1 - 415837

ER -