Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production)
This article suggests the machine operation mode can be estimated from sensors attached to the machine in the production line. An injection molding machine (IMM), which is widely operated in the plastics industry, was used in this research within an actual production line. Four strain sensors were a...
Main Authors: | , |
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Format: | Article |
Language: | Japanese |
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The Japan Society of Mechanical Engineers
2017-12-01
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Series: | Nihon Kikai Gakkai ronbunshu |
Subjects: | |
Online Access: | https://www.jstage.jst.go.jp/article/transjsme/84/857/84_17-00417/_pdf/-char/en |
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author | Tsuneo NAGANUMA Koichi HASHIMOTO |
author_facet | Tsuneo NAGANUMA Koichi HASHIMOTO |
author_sort | Tsuneo NAGANUMA |
collection | DOAJ |
description | This article suggests the machine operation mode can be estimated from sensors attached to the machine in the production line. An injection molding machine (IMM), which is widely operated in the plastics industry, was used in this research within an actual production line. Four strain sensors were attached to the IMM to measure the clamping force in each production cycle. Auto or semi-auto mode was estimated from the clamping force sequence. Auto mode is continuous production by the machine itself, while semi-auto mode involves intermittent human operation and includes such functions as machine start, machine finish, and trouble shooting. Approximately 340000 cycle data were obtained in 10-week production. The changing value was calculated by the binomial filter statistical technique, as well as the differencing of time series data from the clamping force in each cycle. And the relationship between changing value and operation mode is evaluated by the area under the curve (AUC) calculated from the receiver operating characteristic curve (ROC curve). As a result, we obtained that AUC value was 0.86 from 10-week production data, which means operation mode can be estimated from the changing value of clamping force in a high level. This estimating method has a low computational cost; therefore it is easy to install on a machine's board control system. We expect that this method will be used to detect the defective product, the machine malfunction or the mold failure. |
first_indexed | 2024-04-11T08:15:37Z |
format | Article |
id | doaj.art-1a2eb05c9cae44f8a523b5136d7eeba7 |
institution | Directory Open Access Journal |
issn | 2187-9761 |
language | Japanese |
last_indexed | 2024-04-11T08:15:37Z |
publishDate | 2017-12-01 |
publisher | The Japan Society of Mechanical Engineers |
record_format | Article |
series | Nihon Kikai Gakkai ronbunshu |
spelling | doaj.art-1a2eb05c9cae44f8a523b5136d7eeba72022-12-22T04:35:11ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612017-12-018485717-0041717-0041710.1299/transjsme.17-00417transjsmeEstimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production)Tsuneo NAGANUMA0Koichi HASHIMOTO1Graduate School of Information Science, Tohoku UniversityGraduate School of Information Science, Tohoku UniversityThis article suggests the machine operation mode can be estimated from sensors attached to the machine in the production line. An injection molding machine (IMM), which is widely operated in the plastics industry, was used in this research within an actual production line. Four strain sensors were attached to the IMM to measure the clamping force in each production cycle. Auto or semi-auto mode was estimated from the clamping force sequence. Auto mode is continuous production by the machine itself, while semi-auto mode involves intermittent human operation and includes such functions as machine start, machine finish, and trouble shooting. Approximately 340000 cycle data were obtained in 10-week production. The changing value was calculated by the binomial filter statistical technique, as well as the differencing of time series data from the clamping force in each cycle. And the relationship between changing value and operation mode is evaluated by the area under the curve (AUC) calculated from the receiver operating characteristic curve (ROC curve). As a result, we obtained that AUC value was 0.86 from 10-week production data, which means operation mode can be estimated from the changing value of clamping force in a high level. This estimating method has a low computational cost; therefore it is easy to install on a machine's board control system. We expect that this method will be used to detect the defective product, the machine malfunction or the mold failure.https://www.jstage.jst.go.jp/article/transjsme/84/857/84_17-00417/_pdf/-char/eninjection moldingbig dataclamping forcereceiver operating characteristicbinomial filter |
spellingShingle | Tsuneo NAGANUMA Koichi HASHIMOTO Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production) Nihon Kikai Gakkai ronbunshu injection molding big data clamping force receiver operating characteristic binomial filter |
title | Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production) |
title_full | Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production) |
title_fullStr | Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production) |
title_full_unstemmed | Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production) |
title_short | Estimation of operation mode from the clamping force variation of injection molding machine (Application of big data from the injection molding machine under a production) |
title_sort | estimation of operation mode from the clamping force variation of injection molding machine application of big data from the injection molding machine under a production |
topic | injection molding big data clamping force receiver operating characteristic binomial filter |
url | https://www.jstage.jst.go.jp/article/transjsme/84/857/84_17-00417/_pdf/-char/en |
work_keys_str_mv | AT tsuneonaganuma estimationofoperationmodefromtheclampingforcevariationofinjectionmoldingmachineapplicationofbigdatafromtheinjectionmoldingmachineunderaproduction AT koichihashimoto estimationofoperationmodefromtheclampingforcevariationofinjectionmoldingmachineapplicationofbigdatafromtheinjectionmoldingmachineunderaproduction |