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2b6bb5e97b66dbc51d04 · SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING

SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING

Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control

Официальный источник · 2022-11-11

Полные импортированные сведения карточки
{
  "title_en": "SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2022-11-11",
  "date_original": "November 11, 2022",
  "url": "https://www.nist.gov/publications/spatial-temporal-modeling-using-deep-learning-real-time-monitoring-additive",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Jaehyuk Kim , Yan Lu , Zhuo Yang , Hyunwoong Ko, Dongmin Shin, Yosep Oh",
  "abstract_excerpt_en": "Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control",
  "id": "2b6bb5e97b66dbc51d04",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "SPATIAL-TEMPORAL MODELING USING DEEP LEARNING FOR REAL-TIME MONITORING OF ADDITIVE MANUFACTURING",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2022-11-11",
      "date_original": "November 11, 2022",
      "url": "https://www.nist.gov/publications/spatial-temporal-modeling-using-deep-learning-real-time-monitoring-additive",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Jaehyuk Kim , Yan Lu , Zhuo Yang , Hyunwoong Ko, Dongmin Shin, Yosep Oh",
      "abstract_excerpt_en": "Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control"
    }
  ],
  "translation_state": "none"
}