Prompt estimation of die and mold machining time by AI without NC program

Hiroki Takizawa, Hideki Aoyama, Song Cheol Won

研究成果: Article査読

3 被引用数 (Scopus)


Machining time estimation is essential for the due-date estimation of products as well as for production plan-ning. Conventionally, machining time has been estimated by a computer aided manufacturing (CAM) system, which requires time and effort to create its nu-merical control (NC) program and requires machining expertise to operate it. In addition, among the problems with conventional methods, an error in the estimated machining time arises owing to the machine tool’s control characteristics. In this study, an artificial intelligence (AI)-based system capable of estimating machining time promptly and simply based on shape data without requiring any NC program is developed. The input data to the AI system are color information regarding the machined depths, which are used to estimate the rough-machining time, and color information regarding the machined surface curvature distributions to estimate the finish-machining time. Color information on the machined depths and machined surface curvature distributions is created using three-dimensional computer aided design (3D CAD) data. To build the AI system, the shape data and machining time data accumulated at the machining site are used, so that the machining time estimated reflects the machining method, machining expertise, and the machine tool characteristics employed.

ジャーナルInternational Journal of Automation Technology
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • 機械工学
  • 産業および生産工学


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