Implementing an open-source sensor data ingestion, fusion, and analysis capabilities for smart manufacturing

Wang, Kerry; Dave, Parth; Hanchate, Abhishek; Sagapuram, Dinakar; Gautam, Natarajan; Bukkapatnam, Satish

doi:10.1016/j.mfglet.2022.07.109

Cited by 8 publications

(1 citation statement)

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“…It is crucial to establish an appropriate data collection pipeline to ensure the value and usefulness of the collected data for subsequent analysis. Implementing a well-designed data collection pipeline is paramount as it ensures that the acquired data adheres to appropriate protocols, preventing it from becoming inconsequential for analysis purposes (Wang et al, 2022). By collecting data with a well-defined protocol, researchers can ensure data integrity and accuracy.…”

Section: Frontiers In Manufacturing Technologymentioning

confidence: 99%

Imaging systems and techniques for fusion-based metal additive manufacturing: a review

Balhara,

Karthikeyan,

Hanchate

et al. 2023

Front. Manuf. Technol.

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This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.

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Section: Frontiers In Manufacturing Technologymentioning

confidence: 99%