A multi-domain mixture density network for tool wear prediction under multiple machining conditions

Saved in:
Bibliographic Details
Published in:International journal of production research 2023-12, p.1-20
Main Authors: Kim, Gyeongho, Yang, Sang Min, Kim, Sinwon, Kim, Do Young, Choi, Jae Gyeong, Park, Hyung Wook, Lim, Sunghoon
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3
cites cdi_FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3
container_end_page 20
container_issue
container_start_page 1
container_title International journal of production research
container_volume
creator Kim, Gyeongho
Yang, Sang Min
Kim, Sinwon
Kim, Do Young
Choi, Jae Gyeong
Park, Hyung Wook
Lim, Sunghoon
description
doi_str_mv 10.1080/00207543.2023.2289076
format article
fullrecord <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_1080_00207543_2023_2289076</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1080_00207543_2023_2289076</sourcerecordid><originalsourceid>FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3</originalsourceid><addsrcrecordid>eNo1kMtKAzEYhYMoWKuPIOQFpv65zWSWpXgpFNwouBvSXDQ6k5QkQ-3b26H1LM7ZfJzFh9A9gQUBCQ8AFBrB2YICPRaVLTT1BZoRVteVkPLjEs0mppqga3ST8zccIySfIb3Ew9gXX5k4KB_w4H_LmCw2NmRfDjjYso_pB7uYcImxx3urEt4la7wuPgY8BmPT6WPXWzwo_eWDD59Yx2D8hORbdOVUn-3deefo_enxbfVSbV6f16vlptIMoFRUcKq4M6YVjgtKgAhq2kY4CrYGTrVRvKVcciq2lrRbypvW8C3TTjuQzLI5EqdfnWLOybpul_yg0qEj0E2mun9T3WSqO5tifwtaXYs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A multi-domain mixture density network for tool wear prediction under multiple machining conditions</title><source>Business Source Ultimate【Trial: -2024/12/31】【Remote access available】</source><source>Taylor and Francis Science and Technology Collection</source><creator>Kim, Gyeongho ; Yang, Sang Min ; Kim, Sinwon ; Kim, Do Young ; Choi, Jae Gyeong ; Park, Hyung Wook ; Lim, Sunghoon</creator><creatorcontrib>Kim, Gyeongho ; Yang, Sang Min ; Kim, Sinwon ; Kim, Do Young ; Choi, Jae Gyeong ; Park, Hyung Wook ; Lim, Sunghoon</creatorcontrib><identifier>ISSN: 0020-7543</identifier><identifier>EISSN: 1366-588X</identifier><identifier>DOI: 10.1080/00207543.2023.2289076</identifier><language>eng</language><ispartof>International journal of production research, 2023-12, p.1-20</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3</citedby><cites>FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Kim, Gyeongho</creatorcontrib><creatorcontrib>Yang, Sang Min</creatorcontrib><creatorcontrib>Kim, Sinwon</creatorcontrib><creatorcontrib>Kim, Do Young</creatorcontrib><creatorcontrib>Choi, Jae Gyeong</creatorcontrib><creatorcontrib>Park, Hyung Wook</creatorcontrib><creatorcontrib>Lim, Sunghoon</creatorcontrib><title>A multi-domain mixture density network for tool wear prediction under multiple machining conditions</title><title>International journal of production research</title><issn>0020-7543</issn><issn>1366-588X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo1kMtKAzEYhYMoWKuPIOQFpv65zWSWpXgpFNwouBvSXDQ6k5QkQ-3b26H1LM7ZfJzFh9A9gQUBCQ8AFBrB2YICPRaVLTT1BZoRVteVkPLjEs0mppqga3ST8zccIySfIb3Ew9gXX5k4KB_w4H_LmCw2NmRfDjjYso_pB7uYcImxx3urEt4la7wuPgY8BmPT6WPXWzwo_eWDD59Yx2D8hORbdOVUn-3deefo_enxbfVSbV6f16vlptIMoFRUcKq4M6YVjgtKgAhq2kY4CrYGTrVRvKVcciq2lrRbypvW8C3TTjuQzLI5EqdfnWLOybpul_yg0qEj0E2mun9T3WSqO5tifwtaXYs</recordid><startdate>20231206</startdate><enddate>20231206</enddate><creator>Kim, Gyeongho</creator><creator>Yang, Sang Min</creator><creator>Kim, Sinwon</creator><creator>Kim, Do Young</creator><creator>Choi, Jae Gyeong</creator><creator>Park, Hyung Wook</creator><creator>Lim, Sunghoon</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20231206</creationdate><title>A multi-domain mixture density network for tool wear prediction under multiple machining conditions</title><author>Kim, Gyeongho ; Yang, Sang Min ; Kim, Sinwon ; Kim, Do Young ; Choi, Jae Gyeong ; Park, Hyung Wook ; Lim, Sunghoon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Gyeongho</creatorcontrib><creatorcontrib>Yang, Sang Min</creatorcontrib><creatorcontrib>Kim, Sinwon</creatorcontrib><creatorcontrib>Kim, Do Young</creatorcontrib><creatorcontrib>Choi, Jae Gyeong</creatorcontrib><creatorcontrib>Park, Hyung Wook</creatorcontrib><creatorcontrib>Lim, Sunghoon</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of production research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Gyeongho</au><au>Yang, Sang Min</au><au>Kim, Sinwon</au><au>Kim, Do Young</au><au>Choi, Jae Gyeong</au><au>Park, Hyung Wook</au><au>Lim, Sunghoon</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multi-domain mixture density network for tool wear prediction under multiple machining conditions</atitle><jtitle>International journal of production research</jtitle><date>2023-12-06</date><risdate>2023</risdate><spage>1</spage><epage>20</epage><pages>1-20</pages><issn>0020-7543</issn><eissn>1366-588X</eissn><doi>10.1080/00207543.2023.2289076</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0020-7543
ispartof International journal of production research, 2023-12, p.1-20
issn 0020-7543
1366-588X
language eng
recordid cdi_crossref_primary_10_1080_00207543_2023_2289076
source Business Source Ultimate【Trial: -2024/12/31】【Remote access available】; Taylor and Francis Science and Technology Collection
title A multi-domain mixture density network for tool wear prediction under multiple machining conditions
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T10%3A25%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20multi-domain%20mixture%20density%20network%20for%20tool%20wear%20prediction%20under%20multiple%20machining%20conditions&rft.jtitle=International%20journal%20of%20production%20research&rft.au=Kim,%20Gyeongho&rft.date=2023-12-06&rft.spage=1&rft.epage=20&rft.pages=1-20&rft.issn=0020-7543&rft.eissn=1366-588X&rft_id=info:doi/10.1080/00207543.2023.2289076&rft_dat=%3Ccrossref%3E10_1080_00207543_2023_2289076%3C/crossref%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c300t-2542a4fdd95f45210152d975f20e6042cda49248425be19b2479d4b3cfcf083e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true