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ARIMA-GMDH: a low-order integrated approach for predicting and optimizing the additive manufacturing process parameters


This paper proposes a novel data-driven approach for predicting and optimizing the additive manufacturing process parameters. The integrated scheme consists of three popular algorithms: (1) group method for data handling (GMDH) as the engine of neural networks, (2) autoregressive integrated moving average (ARIMA) for characterizing spatial collinearity of the multiple response, and (3) indirect optimization on the basis of self-organization (IOSO) to adopt the emerged correlated multi-response optimization problem. As a numerical case study, a computer-generated fused deposition modeling data tested the introduced algorithms. The finite element (FE) simulation model consists the multi-layer residual stresses as targets, in respect of printing speeds as process parameters. The residual stresses predicted by the low-order integrated ARIMA-GMDH variants correlate well with the FE simulations. This approach provides a viable data-driven alternative for computationally based rapid prototyping and additive manufacturing processes.

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The authors thank Asia Haque and Megan Scribner for their contributions at the early stage of this research.


The authors gratefully acknowledge the support provided by the start-up funds and multidisciplinary seed funds at the University of Massachusetts Dartmouth.

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Correspondence to Jun Li.

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Aljarrah, O., Li, J., Huang, W. et al. ARIMA-GMDH: a low-order integrated approach for predicting and optimizing the additive manufacturing process parameters. Int J Adv Manuf Technol 106, 701–717 (2020).

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  • Additive manufacturing (AM)
  • Group method of data handling (GMDH)
  • Autoregressive integrated moving average (ARIMA)
  • Indirect optimization by self-organization (IOSO)
  • Residual stress
  • Printing speed
  • Correlated multi-response optimization