Improvement in Manufacturing Welded Products through Multiple Response Surface Methodology and Data Mining Techniques

  • Rubén Escribano-García
  • Rubén Lostado-Lorza
  • Roberto Fernández-Martínez
  • Pedro Villanueva-Roldán
  • Bryan J. Mac Donald
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)


Gas Metal Arc Welding (GMAW) is an industrial process commonly used in manufacturing welded products. This manufacturing process is normally done by an industrial robot, which is controlled through the parameters of speed, current and voltage. These control parameters strongly influence the residual stress and the strength of the welded joint, as well as the total cost of manufacturing the welded components. Residual stress and tensile strength are commonly obtained via standardized hole-drilling and tensile tests which are very expensive to routinely carry out during the mass production of welded products. Over the past few decades, researchers have concentrated on improving the quality of manufacturing welded products using experimental analysis or trial-and-error results, but the cost of this methodology has proved unacceptable. Likewise, regression models based on Data Mining (DM) techniques have been used to improve various manufacturing processes, but usually require a relatively large amount of data in order to obtain acceptable results. By contrast, multiple response surface (MRS) methodology is a method for modelling and optimizing, which aims to identify the combination of input parameters that give the best output responses with a reduced number of data sets. In this paper, power consumption, cord area, tensile strength and tensile stress were modelled with quadratic regression (QR) models using Response Surface Methodology (RSM) and were compared with regression models based on DM (linear regression (LR), isotonic regression (IR), Gaussian processes (GP), artificial neural networks (ANN), support vector machines (SVM) and regression trees (RT)). The optimization of the parameters was conducted using RSM with quadratic regression and desirability functions, and was achieved when the residual stresses and power consumption were as low as possible, while strength and process speed were as high as possible.


Optimization Welding Process multiple response surface methodology Data Mining techniques 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rubén Escribano-García
    • 1
  • Rubén Lostado-Lorza
    • 1
  • Roberto Fernández-Martínez
    • 2
  • Pedro Villanueva-Roldán
    • 3
  • Bryan J. Mac Donald
    • 4
  1. 1.Department of Mechanical EngineeringUniversity of La RiojaLogroñoSpain
  2. 2.Department of Material ScienceUniversity of Basque Country UPV/EHUBilbaoSpain
  3. 3.Rural Engineering Department and ProjectsPublic University of NavarrePamplonaSpain
  4. 4.School of Mechanical & Manufacturing EngineeringDublin City UniversityDublinIreland

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