Artificial neural networks applied to arc welding process modeling and control

Andersen, Kristinn; Cook, Gerald; Ramaswamy, K.; Karsai, Gábor

doi:10.1109/ias.1989.96968

Cited by 30 publications

(48 citation statements)

References 5 publications

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“…Andersen et al [25] have explained some concepts related to neural networks and how they can be used to model weld bead geometry, in terms of equipment parameters, in order to evaluate the accuracy of neural networks for weld modelling. They carried out a number of simulations and they used actual GTAW data for this purpose.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Benyounis

Olabi

2008

Advances in Engineering Software

370

128

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Welding input parameters play a very significant role in determining the quality of a weld joint.The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion.Nowadays, application of Design of Experiment (DoE), Evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein.This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Benyounis

Olabi

2008

Advances in Engineering Software

370

128

View full text Add to dashboard Cite

show abstract

“…This structure was also used in a temperature control system [63], [64], monitoring feed water flow rate and component thermal performance of pressurized water reactors [61], and fault diagnosis in a heat exchanger continuous stirred tank reactor system [102]. It was used in a controller for turbo generators [117], digital current regulation of inverter drivers [16], and welding process modeling and control [4], [32]. The MLP was used in modeling chemical process systems [12], to produce quantitative estimation of concentration of chemical components [74], and to select powder metallurgy materials and process parameters [23].…”

Section: A Mlpsmentioning

confidence: 99%

“…It has been found to be an effective system for learning discriminants, for patterns from a body of examples. MLP is used as the basic structure for a bunch of applications [4], [12], [17], [18], [32], [46], [56], [85], [94], [119], [127]. The Hopfield network can be used to identify problems of linear time-varying or time-invariant systems [28].…”

Section: A Modeling and Identificationmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

390

136

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Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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“…Predictive models attempt to estimate product parameters based on process conditions before product manufacture. Andersen et al used back-propagation networks to relate input parameters (arc current, arc voltage, travel speed and wire speed) predictively to quality measures of a weld (bead width, penetration, reinforcement height and cross section area) in a laboratory experiment (Andersen et al, 1990). Okafor pursued a similar approach for estimating surface roughness and bore tolerance in milling using input variables of cutting Quality of cast 2 -1 -1 0 0 0 brake linings to predict product quality and its variability (Smith, 1993).…”

Section: Predictive Neural Network In Manufacturingmentioning

confidence: 99%

Reducing waste in casting with a predictive neural model

1994

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This paper describes an interactive neural network model that predicts the quality of cast ceramic products using multiple quantitative and qualitative inputs. This has been done to enable a major sanitary ware manufacturer to reduce product waste by better control of the slip casting process. The input variables describe the raw materials, ambient conditions and line settings for the ceramic casting process. The neural network predictive model assigns one of seven quality categories to the cast based on the input data. This prediction is used by the quality control engineer to make a priori adjustments to materials and line settings so that a good quality cast is produced without trial and error. The neural model can also be used to determine optimum settings of each adjustable input variable in the light of values of non-adjustable input variables.

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Artificial neural networks applied to arc welding process modeling and control

Cited by 30 publications

References 5 publications

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Optimization of different welding processes using statistical and numerical approaches – A reference guide

A comprehensive review for industrial applicability of artificial neural networks

Reducing waste in casting with a predictive neural model

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