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Forward and backward modeling of direct metal deposition using metaheuristic algorithms tuned artificial neural network and extreme gradient boost

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Abstract

To enhance the automation in additive manufacturing technology, establishment of accurate relationship between the process parameters and responses is extremely important. Direct metal deposition (DMD) or laser metal deposition (LMD) is an ever-emerging field in additive manufacturing spectrum because of its higher build-up rate with flexibility at multiple scales and reduced material wastage. In this work, a feed-forward neural network-based model is proposed for predicting the clad height and capture efficiency while building austenitic steel part through DMD process with variations in input parameters, namely laser power, scan speed, and powder flow rate. With an aim to enhance the prediction performance, the model is hybridized with ancient metaheuristic algorithms, namely genetic algorithm and particle swarm optimization as well as some rare and new metaheuristic algorithms, namely firefly algorithm, bio-geography-based optimization, flower pollination algorithm, dragonfly algorithm, and gray wolf optimization. The backward mapping model is also established along similar lines using the same hybridization schema and all the approaches are comparatively studied. The bi-directional models are further investigated by applying extreme gradient boost (XGBoost), a new and emerging paradigm in the field of machine learning. The comparison is further emphasized by employing a statistical test known as ‘technique for order of preference by similarity to ideal solution (TOPSIS).’ The novelty of the present study lies in utilizing the aforementioned rare and new metaheuristic algorithms for training artificial neural networks in order to develop predictive models in the domain of DMD as well as application of XGBoost for the same. The results clearly recommend the application of hybridized computational intelligence-based approaches in case of the forward mapping model and XGBoost in case of the backward mapping model.

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Acknowledgements

This work acknowledges the constant help and co-operation from Department of Science and Technology (DST), Government of India and the scientists and staffs at Tribology lab, CMERI Durgapur.

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Authors and Affiliations

  1. Research Scholar, Department Mechanical Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Ananda Rabi Dhar, Dhrubajyoti Gupta & Shibendu Shekhar Roy

  2. Senior Principal Scientist, Surface Engineering and Tribology Department, Central Mechanical Engineering Research Institute, Durgapur, West Bengal, 713209, India

    Aditya Kumar Lohar

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  1. Ananda Rabi Dhar
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  2. Dhrubajyoti Gupta
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  3. Shibendu Shekhar Roy
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Correspondence to Shibendu Shekhar Roy.

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Dhar, A.R., Gupta, D., Roy, S.S. et al. Forward and backward modeling of direct metal deposition using metaheuristic algorithms tuned artificial neural network and extreme gradient boost. Prog Addit Manuf 7, 627–641 (2022). https://doi.org/10.1007/s40964-021-00251-w

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  • DOI: https://doi.org/10.1007/s40964-021-00251-w

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