{"id":"https://openalex.org/W4392796959","doi":"https://doi.org/10.1109/tim.2024.3376018","title":"A Time-Series Data-Driven Method for Milling Force Prediction of Robotic Machining","display_name":"A Time-Series Data-Driven Method for Milling Force Prediction of Robotic Machining","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4392796959","doi":"https://doi.org/10.1109/tim.2024.3376018"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2024.3376018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3376018","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101921132","display_name":"Kai Wu","orcid":"https://orcid.org/0000-0002-9475-0659"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Wu","raw_affiliation_strings":["Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China","Pazhou Lab, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9475-0659","affiliations":[{"raw_affiliation_string":"Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"Pazhou Lab, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005224171","display_name":"Yuan Lu","orcid":"https://orcid.org/0000-0001-7403-3137"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Lu","raw_affiliation_strings":["Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China","Pazhou Lab, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7403-3137","affiliations":[{"raw_affiliation_string":"Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"Pazhou Lab, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082065343","display_name":"Ruyi Huang","orcid":"https://orcid.org/0000-0003-0586-1195"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruyi Huang","raw_affiliation_strings":["Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China","Pazhou Lab, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0586-1195","affiliations":[{"raw_affiliation_string":"Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"Pazhou Lab, Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056446916","display_name":"Bernd Kuhlenk\u00f6tter","orcid":"https://orcid.org/0000-0002-5015-7490"},"institutions":[{"id":"https://openalex.org/I904495901","display_name":"Ruhr University Bochum","ror":"https://ror.org/04tsk2644","country_code":"DE","type":"education","lineage":["https://openalex.org/I904495901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bernd Kuhlenk\u00f6tter","raw_affiliation_strings":["Chair of Production Systems, Ruhr-University Bochum, Bochum, Germany"],"raw_orcid":"https://orcid.org/0000-0002-5015-7490","affiliations":[{"raw_affiliation_string":"Chair of Production Systems, Ruhr-University Bochum, Bochum, Germany","institution_ids":["https://openalex.org/I904495901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010684849","display_name":"Weihua Li","orcid":"https://orcid.org/0000-0002-7493-1399"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihua Li","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7493-1399","affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6901,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.62453416,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10188","display_name":"Advanced machining processes and optimization","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10188","display_name":"Advanced machining processes and optimization","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11583","display_name":"Advanced Measurement and Metrology Techniques","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machining","display_name":"Machining","score":0.7767516374588013},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6668051481246948},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5090364813804626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45485806465148926},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.38426434993743896},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.34487470984458923},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.32742568850517273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.10243386030197144}],"concepts":[{"id":"https://openalex.org/C523214423","wikidata":"https://www.wikidata.org/wiki/Q192047","display_name":"Machining","level":2,"score":0.7767516374588013},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6668051481246948},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5090364813804626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45485806465148926},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.38426434993743896},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.34487470984458923},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.32742568850517273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.10243386030197144},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2024.3376018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3376018","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G397272184","display_name":null,"funder_award_id":"52205100","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7068005340","display_name":null,"funder_award_id":"2022M711197","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2008440268","https://openalex.org/W2034406244","https://openalex.org/W2068535810","https://openalex.org/W2075615206","https://openalex.org/W2086113803","https://openalex.org/W2108640380","https://openalex.org/W2111504956","https://openalex.org/W2125226428","https://openalex.org/W2152195021","https://openalex.org/W2217672928","https://openalex.org/W2794297466","https://openalex.org/W2911725274","https://openalex.org/W2921549109","https://openalex.org/W2946790203","https://openalex.org/W2969482699","https://openalex.org/W3106901053","https://openalex.org/W3108603363","https://openalex.org/W3190368733","https://openalex.org/W3210643180","https://openalex.org/W4205129187","https://openalex.org/W4221038176","https://openalex.org/W4226301314","https://openalex.org/W4280500395","https://openalex.org/W4287891049","https://openalex.org/W4293791171","https://openalex.org/W4312253323","https://openalex.org/W4312451760","https://openalex.org/W4319069025","https://openalex.org/W4322102361","https://openalex.org/W4322774658","https://openalex.org/W4323057938","https://openalex.org/W4362604044","https://openalex.org/W4379382323","https://openalex.org/W4382281483","https://openalex.org/W4385814100","https://openalex.org/W4386234158","https://openalex.org/W4387437370","https://openalex.org/W4388281938"],"related_works":["https://openalex.org/W2004173252","https://openalex.org/W2353148039","https://openalex.org/W4239676163","https://openalex.org/W2364861740","https://openalex.org/W2894533098","https://openalex.org/W2355929502","https://openalex.org/W4366504056","https://openalex.org/W2095092528","https://openalex.org/W2718189852","https://openalex.org/W2371803155"],"abstract_inverted_index":{"The":[0,127,162,201],"machining":[1,38,82],"chatter":[2,39],"is":[3,27,99,129,165,204,222],"the":[4,20,30,81,91,102,141,151,177,187,197,207,212,216,242,249,254],"major":[5],"factor":[6],"that":[7,34,241],"results":[8,239],"in":[9,66,87,247],"low":[10],"dimensional":[11],"accuracy,":[12],"poor":[13],"product":[14],"quality,":[15],"and":[16,41,62,74,121,148,184,218,238],"even":[17],"downtime":[18],"when":[19,159],"industrial":[21,85],"robots":[22],"mill":[23],"parts.":[24],"Milling":[25],"force":[26,52,104,251],"one":[28,156,164],"of":[29,84,106,113,199,258],"highest":[31],"responsive":[32],"parameters":[33],"can":[35,149],"depict":[36],"whether":[37],"occurs":[40],"has":[42],"widely":[43],"been":[44],"used":[45,224],"for":[46,68,101,215,225],"monitoring":[47],"purposes.":[48],"However,":[49],"most":[50],"milling":[51,64,103,160,194,236,250],"prediction":[53,105,114,136,154,157,172],"methods":[54],"focus":[55],"on":[56,117,232],"offline":[57,188],"prediction,":[58],"which":[59,109,139,175,221],"cannot":[60],"model":[61,191],"predict":[63,193],"forces":[65,195],"real-time":[67,259],"complex":[69],"systems":[70],"with":[71,143,179],"varying":[72],"dynamics":[73],"poses,":[75],"making":[76],"it":[77],"difficult":[78],"to":[79,192,210],"reflect":[80],"process":[83],"robotics":[86],"time.":[88],"To":[89],"address":[90],"above":[92],"challenge,":[93],"a":[94,130,166,233],"time":[95,134],"series":[96],"data-driven":[97],"method":[98],"proposed":[100,243],"robotic":[107,235],"machining,":[108],"explores":[110],"two":[111],"types":[112],"modes":[115],"based":[116],"Particle":[118],"Swarm":[119],"Optimization":[120],"Long":[122],"Short-Term":[123],"Memory":[124],"Network":[125],"(PSO-LSTM).":[126],"first":[128],"sequence-to-sequence":[131],"mode,":[132,168],"called":[133],"interval":[135],"mode":[137,173],"(TIP),":[138],"updates":[140,176],"network":[142,178,190],"actual":[144],"values":[145,181],"during":[146,182],"deployment":[147,183],"perform":[150],"next":[152,255],"cycle":[153,171],"after":[155],"step":[158],"starts.":[161],"second":[163,257],"point-to-sequence":[167],"named":[169],"single-step":[170],"(SCP),":[174],"predicted":[180],"only":[185],"requires":[186],"optimized":[189],"at":[196],"beginning":[198],"milling.":[200,260],"PSO":[202],"algorithm":[203],"utilized":[205],"as":[206],"optimization":[208],"component":[209],"determine":[211],"optimal":[213],"hyperparameters":[214],"TIP":[217],"SCP":[219],"model,":[220],"subsequently":[223],"online":[226],"prediction.":[227],"Experimental":[228],"validation":[229],"was":[230],"performed":[231],"self-constructed":[234],"platform,":[237],"indicate":[240],"approach":[244],"performs":[245],"well":[246],"predicting":[248],"characteristics":[252],"within":[253],"1":[256]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}