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Investigation of residual stresses of multi-layer multi-track components built by directed energy deposition: experimental, numerical, and time-series machine-learning studies

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Abstract

In the process of manufacturing various components using direct energy deposition (DED) with multiple layers and tracks, the accumulation of heat can result in high residual tensile stresses within the parts. Understanding these stresses and their physical properties is crucial to producing parts with optimal resistance. This paper examines the effects of melting and heat on residual stresses in multi-joint, multi-track, and multi-layer geometries. A hexagonal geometry with a material of AISI 1018 steel with multiple joints was fabricated by laser wire direct energy deposition with one-layer and five-layer depths, and their mechanical properties were measured. Additionally, three samples with the same process parameters and geometrical parameters were built in the form of a cube geometry with five layers, using 316L steel as the deposited material. Two commercial finite element platforms were used to evaluate physical assumptions in modeling DEDs. According to the numerical solution, failure to account for the penetration/mixing between the first layer and the substrate results in a shift in the predicted location of compressive residual stress depths. Nonetheless, The numerical method is proficient in forecasting the behavior of stress variation in accordance with the experimentally measured values. Statistical analysis of the three samples of the cube parts indicates that both depth and location have a significant impact on the local residual stress values. To conclude with rapid-predictor models, two machine learning methods were investigated as rapid-predictor models. The first approach employed a Long Short-Term Memory (LSTM) neural network to predict local stress using temperature history. The second approach involved training a Random Forest regression model to predict the residual stress based on the mathematical characteristics of the temperature history curves, rather than using the entire time-sequential temperature histories as inputs. The latter approach is advantageous over the former approach in terms of accuracy and reduced training resources.

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Acknowledgements

The authors express their gratitude for the funding provided by MITACS, NSERC, and CAMufacturing Solutions Inc. They would also like to acknowledge Phillips Corporation for their assistance in fabricating the experimental parts, as well as Proto Mfg group for their support with the residual stress measurements. This research was made possible in part by the Digital Research Alliance of Canada.

Funding

This work was supported by MITACS and NSERC Canada. Also, research support from CAMufacturing Solution Inc has been received for this research.

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Author notes

  1. Seyedeh Elnaz Mirazimzadeh and Bita Mohajernia contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical, Automotive and Materials Engineering, University of Windsor, Sunset Ave, Windsor, ON, N9B 3P4, Canada

    Seyedeh Elnaz Mirazimzadeh, Bita Mohajernia, Syamak Pazireh, Jill Urbanic & Ofelia Jianu

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  1. Seyedeh Elnaz Mirazimzadeh
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  2. Bita Mohajernia
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  3. Syamak Pazireh
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  4. Jill Urbanic
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Contributions

All authors contributed to the study conception and design. Material preparation and data collection were carried out jointly by Seyedeh Elnaz Mirazimzadeh and Bita Mohajernia, who both contributed equally as first authors to this paper. The analysis were performed by all authors. The first draft of the paper was written by Syamak Pazireh. All authors read and approved the final manuscript.

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Correspondence to Syamak Pazireh.

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Mirazimzadeh, S.E., Mohajernia, B., Pazireh, S. et al. Investigation of residual stresses of multi-layer multi-track components built by directed energy deposition: experimental, numerical, and time-series machine-learning studies. Int J Adv Manuf Technol 130, 329–351 (2024). https://doi.org/10.1007/s00170-023-12661-x

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