Machine learning-based shape error estimation in micromilling by using motor current

Kenta Mizuhara; Daisuke Nakamichi; Wataru Yanagihara; Yasuhiro Kakinuma

Machine learning-based shape error estimation in micromilling by using motor current

Kenta Mizuhara, Daisuke Nakamichi, Wataru Yanagihara, Yasuhiro Kakinuma

Department of System Design Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

The requirement for Mass production of Microlens array (MLA) mold is increasing. MLA molds are manufactured by 5-axis ultra-precision machining center using an ultra-small diameter end mill. Since visual examination is not good at judging quality of product while machining, the development of effective process monitoring technology is required. The ultra-precision machining center used in this study employs a linear motor and has low sliding resistance while machining. Therefore, machining information is directly reflected in servo information. Focusing on this point, we developed a machine learning-based shape error estimation model using servomotor current alone and discussed its usefulness.

Original language	English
Pages	594-596
Number of pages	3
Publication status	Published - 2021
Event	10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021 - Virtual, Online Duration: 2021 Nov 14 → 2021 Nov 18

Conference

Conference	10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021
City	Virtual, Online
Period	21/11/14 → 21/11/18

Keywords

Machine learning
Micro lens array
Sensorless
Servo motor current
Shape error estimation

ASJC Scopus subject areas

Artificial Intelligence
Industrial and Manufacturing Engineering
Electronic, Optical and Magnetic Materials

Cite this

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title = "Machine learning-based shape error estimation in micromilling by using motor current",

abstract = "The requirement for Mass production of Microlens array (MLA) mold is increasing. MLA molds are manufactured by 5-axis ultra-precision machining center using an ultra-small diameter end mill. Since visual examination is not good at judging quality of product while machining, the development of effective process monitoring technology is required. The ultra-precision machining center used in this study employs a linear motor and has low sliding resistance while machining. Therefore, machining information is directly reflected in servo information. Focusing on this point, we developed a machine learning-based shape error estimation model using servomotor current alone and discussed its usefulness.",

keywords = "Machine learning, Micro lens array, Sensorless, Servo motor current, Shape error estimation",

author = "Kenta Mizuhara and Daisuke Nakamichi and Wataru Yanagihara and Yasuhiro Kakinuma",

note = "Funding Information: This work was supported by Strategic Core Technology Advancement Program (Supporting Industry Program), in Ministry of Economy, Trade, and Industry. Publisher Copyright: {\textcopyright} 2021 The Japan Society of Mechanical Engineers.; 10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021 ; Conference date: 14-11-2021 Through 18-11-2021",

year = "2021",

language = "English",

pages = "594--596",

}

TY - CONF

T1 - Machine learning-based shape error estimation in micromilling by using motor current

AU - Mizuhara, Kenta

AU - Nakamichi, Daisuke

AU - Yanagihara, Wataru

AU - Kakinuma, Yasuhiro

N1 - Funding Information: This work was supported by Strategic Core Technology Advancement Program (Supporting Industry Program), in Ministry of Economy, Trade, and Industry. Publisher Copyright: © 2021 The Japan Society of Mechanical Engineers.

PY - 2021

Y1 - 2021

N2 - The requirement for Mass production of Microlens array (MLA) mold is increasing. MLA molds are manufactured by 5-axis ultra-precision machining center using an ultra-small diameter end mill. Since visual examination is not good at judging quality of product while machining, the development of effective process monitoring technology is required. The ultra-precision machining center used in this study employs a linear motor and has low sliding resistance while machining. Therefore, machining information is directly reflected in servo information. Focusing on this point, we developed a machine learning-based shape error estimation model using servomotor current alone and discussed its usefulness.

AB - The requirement for Mass production of Microlens array (MLA) mold is increasing. MLA molds are manufactured by 5-axis ultra-precision machining center using an ultra-small diameter end mill. Since visual examination is not good at judging quality of product while machining, the development of effective process monitoring technology is required. The ultra-precision machining center used in this study employs a linear motor and has low sliding resistance while machining. Therefore, machining information is directly reflected in servo information. Focusing on this point, we developed a machine learning-based shape error estimation model using servomotor current alone and discussed its usefulness.

KW - Machine learning

KW - Micro lens array

KW - Sensorless

KW - Servo motor current

KW - Shape error estimation

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M3 - Paper

AN - SCOPUS:85124807061

SP - 594

EP - 596

T2 - 10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021

Y2 - 14 November 2021 through 18 November 2021

ER -

Machine learning-based shape error estimation in micromilling by using motor current

Abstract

Conference

Keywords

ASJC Scopus subject areas

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