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An Effective Temperature Profile Prediction in Additive Manufacturing Process Using Fractional GNU Global Herding Optimization-based Deep Learning Technique

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Abstract

Material is constructed in three dimensions using the additive manufacturing (AM) paradigm, which involves layer-by-layer depositing material to a computer-aided conceptual model. Due to its ability to produce functional components with complicated geometries quickly and easily utilizing techniques like laser metal deposition that would be challenging to produce with traditional machining, AM has gained a lot of popularity in the past ten years. This research designs and develops a key component of a scientific technique for creating data-driven model-based real-time control systems. The database is created and time-dependent heat equations are solved using finite element methods. The proposed model employs the fractional gnu global herding optimization algorithm, which successively uses temperatures of previous voxels and laser details as inputs to anticipate temperatures of following voxels, to solve problems with extremely unpredictable solutions. The fractional gnu global herding optimization-based neural network (FFGHO-NN) model achieved performance with the lowest error values using database1 of 3.787, 29.932%, 5.675, and 2.382, respectively, for the metrics MAE, MAPE, MSE, RMSE for database 2 the values are 14.59, 26.52%, 11.62, and 3.41.

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Authors and Affiliations

  1. Matoshri Pratishthan MPGI Nanded, Vishnupuri, Nanded, Maharashtra, 431606, India

    Shaikh Tauseef Ahmed & R. Sayyad Shafik

  2. Sandip School of Engineering and Technology, Nashik, Mahiravani, Trimbak Road, Nashik, Maharashtra, 422213, India

    Amol D. Lokhande

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  1. Shaikh Tauseef Ahmed
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Correspondence to Shaikh Tauseef Ahmed.

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Ahmed, S.T., Lokhande, A.D. & Shafik, R.S. An Effective Temperature Profile Prediction in Additive Manufacturing Process Using Fractional GNU Global Herding Optimization-based Deep Learning Technique. Int J Interact Des Manuf 17, 3069–3084 (2023). https://doi.org/10.1007/s12008-023-01349-x

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