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Despite continued advances, MAM processes still face huge uncertainty, resulting in variable part quality. Real-time sensing for MAM processing helps quantify uncertainty by detecting build failure and process anomalies. While the high volume of multidimensional sensor data\u2014such as melt-pool geometries and temperature gradients\u2014is beginning to be explored, sensor selection does not yet effectively link sensor data to part quality. To begin investigating such connections, we propose network-based models that capture in real-time (1) sensor data's association with process variables and (2) as-built part qualities\u2019 association with related physical phenomena. These sensor models and networks lay the foundation for a comprehensive framework to monitor and manage the quality of MAM process outcomes.<\/jats:p>","DOI":"10.1115\/1.4055853","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T05:20:17Z","timestamp":1664947217000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":13,"title":["Ontology Network-Based In-Situ Sensor Selection for Quality Management in Metal Additive Manufacturing"],"prefix":"10.1115","volume":"22","author":[{"given":"Byeong-Min","family":"Roh","sequence":"first","affiliation":[{"name":"The University of Oklahoma School of Industrial Systems and Engineering, , Norman, OK 73019"}]},{"given":"Soundar R. 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