TY - JOUR
T1 - Development of accurate estimation method of machining time in consideration of characteristics of machine tool
AU - Yamamoto, Yuki
AU - Aoyama, Hideki
AU - Sano, Noriaki
N1 - Publisher Copyright:
© 2017 The Japan Society of Mechanical Engineers.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - In order to perform accurately scheduling of machining using a machine tool, it is necessary to estimate the actual machining time. The machining time is generally estimated by CAM systems. However, the error between the estimated and real cutting times is considerable because the systems do not consider the control and functional characteristics of the machine tool. In addition, control functions are installed in machine tools to achieve high precision and high speed motion while optimizing the tool paths and control parameters. The functions significantly affect the machining time. However, estimating machining time is difficult, thereby complicating optimization process. In this study, a method to identify the control characteristics and actual tool paths and a system to estimate the cutting time were developed. Furthermore, an estimation system using a deep neural network (DNN) was constructed to incorporate a control function. Finally, verification experiments were conducted wherein the estimation accuracy of the machining time was found to be within 5%.
AB - In order to perform accurately scheduling of machining using a machine tool, it is necessary to estimate the actual machining time. The machining time is generally estimated by CAM systems. However, the error between the estimated and real cutting times is considerable because the systems do not consider the control and functional characteristics of the machine tool. In addition, control functions are installed in machine tools to achieve high precision and high speed motion while optimizing the tool paths and control parameters. The functions significantly affect the machining time. However, estimating machining time is difficult, thereby complicating optimization process. In this study, a method to identify the control characteristics and actual tool paths and a system to estimate the cutting time were developed. Furthermore, an estimation system using a deep neural network (DNN) was constructed to incorporate a control function. Finally, verification experiments were conducted wherein the estimation accuracy of the machining time was found to be within 5%.
KW - CAM system
KW - Control function
KW - Deep neural network
KW - Machining time estimation
KW - NC machine tool
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U2 - 10.1299/jamdsm.2017jamdsm0049
DO - 10.1299/jamdsm.2017jamdsm0049
M3 - Article
AN - SCOPUS:85030853911
SN - 1881-3054
VL - 11
JO - Journal of Advanced Mechanical Design, Systems and Manufacturing
JF - Journal of Advanced Mechanical Design, Systems and Manufacturing
IS - 4
M1 - JAMDSM0049
ER -