Data mining from endmill tool catalog information based on the use of a machine learning method

Akihito Asakura, Toshiki Hirogaki, Eiichi Aoyama, Hiroyuki Kodama

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

Abstract

In recent years, the needs associated with the development of new technologies in the manufacturing industry that utilize big data typified by the Internet-of-Things (IoT) and artificial intelligence (AI) have been increasing. Recent computer-aided manufacturing (CAM) systems have evolved so that unskilled technicians can create tool paths relatively easily with numerically controlled (NC) programs, but tool-cutting conditions used for machining cannot be automatically determined. Therefore, many unskilled technicians often set the cutting conditions based on the recommended conditions described in the tool catalog. However, given that the catalog contains large-scale data on machining technology, setting the proper conditions becomes a time-consuming and inefficient process. In this study, we aimed to construct a system to support unskilled technicians to determine the optimum machining conditions. To this end, we constructed a prediction model using a random forest machine learning method to predict the cutting conditions. It was confirmed that the prediction with the random forest method can be performed with high accuracy based on the cutting conditions recommended by the tool maker. Thus, the effectiveness of this method was verified.

Original languageEnglish
Title of host publication40th Computers and Information in Engineering Conference (CIE)
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791883983
DOIs
Publication statusPublished - 2020
EventASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020 - Virtual, Online
Duration: Aug 17 2020Aug 19 2020

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume9

Conference

ConferenceASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020
CityVirtual, Online
Period8/17/208/19/20

Keywords

  • Data mining
  • Decision tree
  • Machine learning
  • Radius Endmill
  • Random forest

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modelling and Simulation

Fingerprint Dive into the research topics of 'Data mining from endmill tool catalog information based on the use of a machine learning method'. Together they form a unique fingerprint.

Cite this