Rapid estimation of die and mold machining time without NC data by AI based on shape data

Hiroki Takizawa, Hideki Aoyama, Song Cheol Won

研究成果: Conference contribution

抄録

Rapid estimation of machining time is necessary for flexible production scheduling and early responses regarding delivery date. It is also important for selecting the most suitable of a factory's many machine tools. Usually, machining time is estimated based on an NC program. However, this takes time to generate and its estimation accuracy is not ideal because it cannot consider the control characteristics of the machine tool. This study proposes a new method for rapidly estimating die and mold machining time without generating an NC program: inputting curvature and machining depth distributions into AI as color information.

本文言語English
ホスト出版物のタイトル2020 International Symposium on Flexible Automation, ISFA 2020
出版社American Society of Mechanical Engineers (ASME)
ISBN(電子版)9780791883617
DOI
出版ステータスPublished - 2020
イベント2020 International Symposium on Flexible Automation, ISFA 2020 - Virtual, Online
継続期間: 2020 7 82020 7 9

出版物シリーズ

名前2020 International Symposium on Flexible Automation, ISFA 2020

Conference

Conference2020 International Symposium on Flexible Automation, ISFA 2020
CityVirtual, Online
Period20/7/820/7/9

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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