Machine Tool Assignment Realized by Automated NC Program Generation and Machining Time Prediction

被引:1
|
作者
Nishida, Isamu [1 ]
Shirase, Keiichi [1 ]
机构
[1] Kobe Univ, Dept Mech Engn, Grad Sch Engn, Nada Ku, 1-1 Rokko Dai, Kobe, Hyogo 6578501, Japan
关键词
machine tool assignment; automated NC program generation; machining time prediction; CAD-CAM; autonomous manufacturing system; TOPOLOGICAL SIMILARITY; DATABASE SYSTEM;
D O I
10.20965/ijat.2019.p0700
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The present study proposed a method to automatically generate a numerical control (NC) program by referring to machining case data for each machine tool with only 3D-CAD models of a product and workpiece as the input data, and to select machine tools for machining the target removal region among several machine tools with different characteristics. The special features of the proposed method are described as follows. The removal volume can be automatically obtained from the total removal volume (TRV), which is extracted from the workpiece and product using a Boolean operation by dividing it on the XY plane. The removal region changed according to the determined machining sequence. The conditions for machining the removal region is automatically determined according to the machining case data, which is stored by linking the geometric properties of the removal region with the machining conditions determined by experienced operators. Furthermore, an NC program is automatically generated based on the machining conditions. The machine tools for machining the target region are selected according to the predicted machining time of each machine tool connected by a network. A case study was conducted to validate the effectiveness of the proposed system. The results confirm that machining can be conducted using only 3D-CAD models as input data. It was suggested that the makespan would be shortened by changing the machining sequence from the optimized machining sequence when machining a plurality of products.
引用
收藏
页码:700 / 707
页数:8
相关论文
共 50 条
  • [21] Efficient tool path planning for machining an impeller with a 5-axis NC machine
    Feng, Junran
    Dai, Xing
    Cheng, Yongzhi
    Xiong, Caihua
    ADVANCED MANUFACTURING TECHNOLOGY, PTS 1, 2, 2011, 156-157 : 570 - 574
  • [22] Generation of offset surface for tool path in NC machining through level set methods
    Hongyao Shen
    Jianzhong Fu
    Zichen Chen
    Yongqiang Fan
    The International Journal of Advanced Manufacturing Technology, 2010, 46 : 1043 - 1047
  • [23] NC tool path generation for 5-axis machining of free formed surfaces
    Jong-Yun Jung
    Rashpal S. Ahluwalia
    Journal of Intelligent Manufacturing, 2005, 16 : 115 - 127
  • [24] NC tool path generation for 5-axis machining of free formed surfaces
    Jung, JY
    Ahluwalia, RS
    JOURNAL OF INTELLIGENT MANUFACTURING, 2005, 16 (01) : 115 - 127
  • [25] New generation algorithm of tool swept volume and it's application of NC machining simulation
    Wang, Zhe
    Wang, Zhixing
    Zhong, Shisheng
    Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering, 2001, 37 (01): : 28 - 31
  • [26] Generation of offset surface for tool path in NC machining through level set methods
    Shen, Hongyao
    Fu, Jianzhong
    Chen, Zichen
    Fan, Yongqiang
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2010, 46 (9-12): : 1043 - 1047
  • [27] Prompt Estimation of Die and Mold Machining Time by AI Without NC Program
    Takizawa, Hiroki
    Aoyama, Hideki
    Won, Song Cheol
    INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY, 2021, 15 (03) : 350 - 358
  • [28] Direct generation of NC program supporting external-setup machining center work
    Ma'ruf, A
    INTERNATIONAL JOURNAL OF THE JAPAN SOCIETY FOR PRECISION ENGINEERING, 1999, 33 (02): : 143 - 148
  • [29] DEVELOPMENT OF AUTOMATIC SYSTEM FOR PROCESS PLANNING AND NC PROGRAM GENERATION ON TURNING-MILLING MACHINE TOOL WITH THREE TURRETS
    Fukuda, Ryo
    Kiyooka, Ririko
    Aoyama, Hideki
    PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2017, VOL 11, 2018,
  • [30] Automated Machine Learning for Time Series Prediction
    da Silva, Felipe Rooke
    Vieira, Alex Borges
    Bernardino, Heder Soares
    Alencar, Victor Aquiles
    Pessamilio, Lucas Ribeiro
    Correa Barbosa, Helio Jose
    2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2022,