Journal of Materials Engineering and Performance

, Volume 21, Issue 8, pp 1611–1619 | Cite as

Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks

  • Emad S. Al-Momani
  • Ahmad T. Mayyas
  • Ibrahim Rawabdeh
  • Rajaa Alqudah
Article
First Online:
Received:
Revised:

Abstract

The design of blanking processes requires the availability of a procedure able to deal with both tooling and mechanical properties of the workpiece material (blank thickness, hardness, ductility, etc.). This research presents the development and comparison of two models to predict the quality of the blanked edge represented by burrs height, the first model is an artificial neural network (ANN) based, while the second model is a multiple regression analysis (MRA) based. Finite Element modeling of the blanking process was used to generate the data for both models. Both ANN and MRA are able to give good prediction results, however, ANN still more accurate because it deals efficiently with hidden nonlinear relations when compared to MRA. The comparison between experimental and model results shows that average absolute relative error in the case of ANN was <2.20% for carbon steel and 4.85% for corrosion-resistant steel (CRES) compared to 15.18% for carbon steel and 14.22% for CRES obtained from the second order MRA. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.

Keywords

artificial neural networks blanking burrs height regression steel 
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Copyright information

© ASM International 2011

Authors and Affiliations

  • Emad S. Al-Momani
    • 1
  • Ahmad T. Mayyas
    • 2
    • 5
  • Ibrahim Rawabdeh
    • 3
  • Rajaa Alqudah
    • 4
  1. 1.Department of Systems Science and Industrial EngineeringBinghamton University, SUNYBinghamtonUSA
  2. 2.Department of Automotive EngineeringClemson UniversityClemsonUSA
  3. 3.Department of Industrial EngineeringUniversity of JordanAmmanJordan
  4. 4.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA
  5. 5.International Center for Automotive Research (CU-ICAR), CGECClemson UniversityGreenvilleUSA

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