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

Hiroki Takizawa, Hideki Aoyama, Song Cheol Won

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)350-358
Number of pages9
JournalInternational Journal of Automation Technology
Issue number3
Publication statusPublished - 2021


  • Artificial intelli-gence
  • Die
  • Machining time estimation
  • Mold
  • No NC-data

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Prompt estimation of die and mold machining time by AI without NC program'. Together they form a unique fingerprint.

Cite this