Selective laser melting (SLM) is modernizing the production of highly complex metal parts across the manufacturing industry. However, achieving material homogeneity and controlling thermal deformation remain major challenges for metal-based, additive manufacturing. Therefore, adequate control systems are needed to monitor build processes, and ensure part quality throughout production. Traditionally, control designs relied on physics-based knowledge in analyzing, characterizing, and modeling complex, nonstationary patterns. Recent advancements in machine learning techniques harness the abundance of data to discover effective control designs. In this paper, we investigate the efficacy of a data-driven approach towards in-situ modeling of melt-pool geometry. Specifically, we propose a new methodology that uses a deep neural network architecture to predict melt pool geometries from linear regression models that manifest during in-situ processes. Experimental results show that our deep neural network model with multiple input features produced %84 goodness of fit score, outperforming the model with a single feature that scored %37 for the given dataset, and the monitored regression models. These&nbsp;outcomes promote further investigation into new and efficient ways for acquiring real-time data from in-situ processes. Our contribution complements the way we understand properties of in-situ data, and predict patterns of melt pools, based on artificial cognition.

Prediction of Melt Pool Geometry Using Deep Neural Networks

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.