{"id":"https://openalex.org/W4285410141","doi":"https://doi.org/10.1145/3532213.3532216","title":"Removal Rate Prediction of Multiway Material Data Using the Deep Learning Approach","display_name":"Removal Rate Prediction of Multiway Material Data Using the Deep Learning Approach","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W4285410141","doi":"https://doi.org/10.1145/3532213.3532216"},"language":"en","primary_location":{"id":"doi:10.1145/3532213.3532216","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3532213.3532216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Computing and Artificial Intelligence","raw_type":"proceedings-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/A5100452614","display_name":"Han Li","orcid":"https://orcid.org/0000-0003-0276-9756"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Han Li","raw_affiliation_strings":["Qiyuan Laboratory, China"],"affiliations":[{"raw_affiliation_string":"Qiyuan Laboratory, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100693811","display_name":"Wenhui Fan","orcid":"https://orcid.org/0000-0002-0040-5759"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhui Fan","raw_affiliation_strings":["Qiyuan Laboratory, China and Department of Automation, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Qiyuan Laboratory, China and Department of Automation, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100452614"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0801,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34258409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"12","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11301","display_name":"Advanced Surface Polishing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11301","display_name":"Advanced Surface Polishing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10188","display_name":"Advanced machining processes and optimization","score":0.9988999962806702,"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/T11451","display_name":"Advanced Machining and Optimization Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/deep-learning","display_name":"Deep learning","score":0.8233442306518555},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7746784687042236},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7261675596237183},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7193278074264526},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5892665386199951},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5546073317527771},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.49342837929725647},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4793894290924072},{"id":"https://openalex.org/keywords/polishing","display_name":"Polishing","score":0.4738383889198303},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.44088226556777954},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1604231894016266}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8233442306518555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7746784687042236},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7261675596237183},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7193278074264526},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5892665386199951},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5546073317527771},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.49342837929725647},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4793894290924072},{"id":"https://openalex.org/C138113353","wikidata":"https://www.wikidata.org/wiki/Q611639","display_name":"Polishing","level":2,"score":0.4738383889198303},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.44088226556777954},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1604231894016266},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3532213.3532216","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3532213.3532216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W3109101710","https://openalex.org/W3137197933","https://openalex.org/W4248245878"],"related_works":["https://openalex.org/W2355958986","https://openalex.org/W2359200184","https://openalex.org/W2037225514","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067","https://openalex.org/W2979433843","https://openalex.org/W3208304128"],"abstract_inverted_index":{"Chemical":[0],"Mechanical":[1],"Polishing":[2],"(CMP)":[3],"process":[4,11],"is":[5,34],"one":[6],"of":[7,38,60,63,85],"the":[8,58,83,96,106],"most":[9],"critical":[10],"steps":[12],"in":[13,99],"advanced":[14],"packaging":[15],"manufacturing.":[16],"The":[17,90],"material":[18],"removal":[19],"rate":[20],"(MRR)":[21],"value,":[22],"which":[23],"affects":[24],"polishing":[25],"accuracy,":[26],"can":[27],"be":[28],"predicted":[29],"by":[30,75,95],"data-driven":[31],"method.":[32],"There":[33],"no":[35],"general":[36],"study":[37],"predicting":[39],"MRR":[40,70],"value":[41],"based":[42],"on":[43],"different":[44],"deep":[45,64,76,107,120],"learning":[46,65,77,88,108,114,121,126],"neural":[47,66,78,109],"networks.":[48],"In":[49],"this":[50],"paper,":[51],"multiway":[52],"data":[53],"are":[54,80,93],"directly":[55],"employed":[56],"as":[57,82],"input":[59,84],"three":[61],"types":[62],"network":[67,79],"to":[68],"predict":[69],"value.":[71],"Afterwards,":[72],"features":[73],"extracted":[74],"applied":[81],"ensemble":[86],"machine":[87,113,125],"model.":[89],"proposed":[91],"approaches":[92],"verified":[94],"public":[97],"dataset":[98],"2016":[100],"PHM":[101],"Challenge.":[102],"Experiments":[103],"results":[104],"show":[105],"networks":[110],"outperform":[111],"traditional":[112,124],"model,":[115],"and":[116,123],"integration":[117],"model":[118,122,127],"between":[119],"pursues":[128],"better":[129],"performance.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
