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

Hiroki Takizawa, Hideki Aoyama, Song Cheol Won

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 International Symposium on Flexible Automation, ISFA 2020
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791883617
DOIs
Publication statusPublished - 2020
Event2020 International Symposium on Flexible Automation, ISFA 2020 - Virtual, Online
Duration: 2020 Jul 82020 Jul 9

Publication series

Name2020 International Symposium on Flexible Automation, ISFA 2020

Conference

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

Keywords

  • Artificial intelligence
  • Die and mold
  • Machining time

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Rapid estimation of die and mold machining time without NC data by AI based on shape data'. Together they form a unique fingerprint.

Cite this