Multi-agent open architecture for process monitoring and part certification
Saleeby, Kyle Scott
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Methods to connect manufacturing machines, processes, and sensors have rapidly developed through the fourth industrial revolution, known as Industry 4.0. Data collection is now possible at every point in a production process, providing exceptional analysis opportunities to monitor and affect manufacturing operations. Digital manufacturing technologies can be applied to computer numeric control (CNC) manufacturing processes to measure and improve component quality. Various architectures have been proposed to leverage machine connection mechanisms and extract information in a logical manner. However, these architectures often rely on proprietary software, restricting flexibility for future changes and upgrades. Furthermore, additional capabilities are needed to combine data collected from different sensing modalities and provide a method of in-situ geometric verification. A multi-agent open architecture is proposed to collect, analyze, and communicate information of different formats and sampling characteristics in a strategic manner. This body of work evaluates the strategic combination and synchronization of information from multiple sensing modalities to improve the accuracy of digital twin models. A voxel modelling methodology is developed and investigated to create a digital twin of the component being produced. Information describing the machine’s current operations is strategically combined with information from additional sensing modalities, improving the accuracy of in-situ digital twin models by up to 52%. This research results in (1) a method to geometrically compare features of in-situ components from multiple sensing modalities against desired specifications, (2) a multi-agent architecture to support efficient communication, storage, and use of this information, resulting in (3) feedback methodologies for commercial CNC systems to affect the in-situ manufacturing process and correct geometric deviations.