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@article{154595,
        author = {Eakansh Mahto and Dharmendra Tyagi},
        title = {Application of Deep Learning for Design Optimization in Additive Manufacturing Process},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {842-850},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154595},
        abstract = {Additive Manufacturing (AM) is an advanced fabrication technique that uses computerised three dimensional design information to construct components by accumulating textures chronologically. Due to “ their achievement in constructing complex and difficult design concepts, rapid prototype testing, and reduced-volume or one-of-a-kind manufacturing throughout many industry sectors, AM methodologies are becoming much more popular comparison to traditional initiatives. Computational simulations are indeed an important part of AM design and optimization because they completely remove costly manufacturing process trial and error. This motivates the development of a predictive tool based on machine learning (ML) that can produce simulation results instantly rather than requiring expensive physics-based simulations.},
        keywords = {Additive Manufacturing, machine learning, Topology Optimization, Compliance, Density Distribution.},
        month = {},
        }