Intelligent Digital Twin Applications for Feature-Based Parameter Selection in Additive Manufacturing
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Abstract
The current industrial transition coins additive manufacturing and smart machines towards digital manufacturing. Wire arc additive manufacturing method is one such potential method with the aid of industrial robots with less buy-to-fly ratio. In the present work, a systematic framework is adopted for feature-based auto-selection of process parameters with the advent of machine learning models and with the implementation of digital twin. The performance of the proposed model was validated by building a thin wall structure with the proposed technology. Forward and reverse implementation of strategies were adopted to determine suitable parameters for the required feature. Implementation of these optimal parameters with the proposed methodology adopts the generative model framework. A thin wall structure was fabricated with the novel framework. The thin wall structure characterization was performed to determine the structural integrity developed and found to be superior to conventional bead formation methods.
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