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Data-Driven Model for Predicting Tensile Properties of Wire Arc Additive Manufactured 316L Steels and Its Validation

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Abstract

This paper develops a regression based and neural network machine learning (ML) model for the prediction of mechanical properties of wire arc additively manufactured (WAAMed) steel. The presented data driven approach studies the correlation between input parameters and ensuing mechanical properties to increase the competency in terms of efficiency and result optimality. In this work, the interaction of various process parameters i.e. current, voltage, wire feed speed, travel speed and gas flow rate on arriving the mechanical properties (YS and UTS) in different orientations is investigated. The model utilizes 137 experimental datasets for machine learning models. The developed machine learning models were validated through inhouse experimentation. Quantitative results suggested that the both models predicted the mechanical properties with good accuracy, but the random forest model outperform the neural network model in term of percentage error values. The occurrence of inhomogeneity in the microstructure and mechanical properties of the as-built wall is investigated. The microstructure of 316L thin wall consists of ferrite and austenite as main constituent phases, and ferrite morphology and presence of σ-phase inclusion have an impact on hardness and mechanical properties. The average hardness, YS and UTS values are 185 Hv0.2, 303 ± 6 MPa and 490 ± 18 MPa respectively.

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  1. Mechanical Engineering Department, National Institute of Technology Warangal, Hanamkonda, India

    Ramesh Mamedipaka & Shivraman Thapliyal

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  1. Ramesh Mamedipaka
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Mamedipaka, R., Thapliyal, S. Data-Driven Model for Predicting Tensile Properties of Wire Arc Additive Manufactured 316L Steels and Its Validation. J. of Materi Eng and Perform 33, 1083–1091 (2024). https://doi.org/10.1007/s11665-023-08071-5

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