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Machine learning approaches for predicting geometric and mechanical characteristics for single P420 laser beads clad onto an AISI 1018 substrate

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The final mechanical and physical properties should be predicted in tandem with the bead geometry characteristics for effective additive manufacturing (AM) solutions for processes such as directed energy deposition. Experimental approaches to investigate the final geometry and the mechanical properties are costly, and simulation solutions are time-consuming. Alternative artificial intelligent (AI) systems are explored as they are a powerful approach to predict such properties. In the present study, the geometrical properties as well as the mechanical properties (residual stress and hardness) for single bead clads are investigated. Experimental data is used to calibrate multi-physics finite element models, and both data sets are used to seed the AI models. The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (bead cross-sections), and 3D (complete bead) domains. The prediction results are evaluated using the mean relative error measure. The ANFIS predictions are more precise than those from the ANN for the 1D and 2D domains, but the ANN had less error for the 3D scenario. These models are capable of predicting the geometrical and the mechanical properties values very well, including capturing the mechanical properties in transient regions; however, this research should be extended for multi-bead scenarios before a conclusive “best approach” strategy can be determined.

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The support provided by MITACs, CAMufacturing Solutions, Inc., and Lincoln Laser Inc. is gratefully acknowledged.


This research was funded by Mitacs Accelerate (award number, IT18391, IT16398; grant recipients, Dr. Jill Urbanic, Dr. Ofelia Jianu) and also the NSERC grant (award number, RGPIN/04842–2018; grant recipients, Dr. Jill Urbanic).

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Authors and Affiliations



BM: simulation model setup, data collection, analysis, developing mathematical model, and paper write up. SEM: developing mathematical model and paper write up. AP: developing mathematical model and paper write up. RJU: experimental data collection and analysis, simulation data analysis, research paper writing and editing, and project funding.

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Correspondence to R. Jill Urbanic.

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The manuscript is not submitted to any other journal simultaneously. The manuscript will not be submitted elsewhere until the editorial process is completed. The submitted work is original. A single study “Machine learning approaches for predicting geometric and mechanical characteristics for single P420 laser beads clad onto an AISI 1018 substrate” has not been split up into several parts to increase the quantity of submissions. There is no any content being translated from other journals in other languages. Results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. No data, text, or theories by others are presented as if they were the author’s own (“plagiarism”). The authors have permission to use the applied software. This research has not been applied to pose a threat to public health or national security.

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Mohajernia, B., Mirazimzadeh, S.E., Pasha, A. et al. Machine learning approaches for predicting geometric and mechanical characteristics for single P420 laser beads clad onto an AISI 1018 substrate. Int J Adv Manuf Technol 118, 3691–3710 (2022).

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