{"id":"https://openalex.org/W4416249922","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228148","title":"Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test","display_name":"Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249922","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228148"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228148","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5023716533","display_name":"Zhengni Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengni Yang","raw_affiliation_strings":["Xi’an Jiaotong-Liverpool University,School of Advanced Technology,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong-Liverpool University,School of Advanced Technology,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030499511","display_name":"Rui Yang","orcid":"https://orcid.org/0000-0002-5634-5476"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Yang","raw_affiliation_strings":["Xi’an Jiaotong-Liverpool University,School of Advanced Technology,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong-Liverpool University,School of Advanced Technology,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113412215","display_name":"Weijian Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160779","display_name":"Institute of New Materials","ror":"https://ror.org/04s8x9847","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210142748","https://openalex.org/I4210160779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijian Han","raw_affiliation_strings":["Materials Bigdata and Applications Division Materials Academy JITRI,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Materials Bigdata and Applications Division Materials Academy JITRI,Suzhou,China,215123","institution_ids":["https://openalex.org/I4210160779"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101541510","display_name":"Qixin Liu","orcid":"https://orcid.org/0009-0006-1842-7370"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qixin Liu","raw_affiliation_strings":["Xi’an Jiaotong-Liverpool University,School of Advanced Technology,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi’an Jiaotong-Liverpool University,School of Advanced Technology,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30359166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10840","display_name":"High Temperature Alloys and Creep","score":0.4487000107765198,"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/T10840","display_name":"High Temperature Alloys and Creep","score":0.4487000107765198,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.11110000312328339,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10396","display_name":"Fatigue and fracture mechanics","score":0.0763000026345253,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/field","display_name":"Field (mathematics)","score":0.4404999911785126},{"id":"https://openalex.org/keywords/design-of-experiments","display_name":"Design of experiments","score":0.385699987411499},{"id":"https://openalex.org/keywords/test-method","display_name":"Test method","score":0.2957000136375427},{"id":"https://openalex.org/keywords/experimental-data","display_name":"Experimental data","score":0.2612000107765198}],"concepts":[{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4375999867916107},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4323999881744385},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.385699987411499},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3224000036716461},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3122999966144562},{"id":"https://openalex.org/C132519959","wikidata":"https://www.wikidata.org/wiki/Q3077373","display_name":"Test method","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2775999903678894},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C77246614","wikidata":"https://www.wikidata.org/wiki/Q1409400","display_name":"Gramian matrix","level":3,"score":0.24420000612735748}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228148","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2012970675","https://openalex.org/W2041723890","https://openalex.org/W2070692407","https://openalex.org/W2522734374","https://openalex.org/W2894260520","https://openalex.org/W2898890350","https://openalex.org/W3084200341","https://openalex.org/W3108882937","https://openalex.org/W3144806028","https://openalex.org/W3199258042","https://openalex.org/W3216007425","https://openalex.org/W4283791177","https://openalex.org/W4378804743","https://openalex.org/W4381250019","https://openalex.org/W4385259713","https://openalex.org/W4385453382","https://openalex.org/W4386008286","https://openalex.org/W4401635109","https://openalex.org/W4408077195"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,41,86],"novel":[4],"deep-learning":[5],"approach":[6,24,63],"to":[7,30,54,89],"predict":[8],"true":[9,102],"stress-strain":[10,103],"curves":[11],"of":[12,75,101],"high-strength":[13],"steels":[14],"from":[15],"small":[16],"punch":[17],"test":[18],"(SPT)":[19],"load-displacement":[20,32],"data.":[21],"The":[22,82],"proposed":[23,62,83],"uses":[25],"Gramian":[26],"Angular":[27],"Field":[28],"(GAF)":[29],"transform":[31],"sequences":[33],"into":[34],"images,":[35],"capturing":[36],"spatial-temporal":[37],"features":[38],"and":[39,70,78,99],"employs":[40],"Sequence-to-Sequence":[42],"(Seq2Seq)":[43],"model":[44],"with":[45,68],"an":[46],"LSTM-based":[47],"encoder-decoder":[48],"architecture,":[49],"enhanced":[50],"by":[51],"multi-head":[52],"cross-attention":[53],"improved":[55],"accuracy.":[56],"Experimental":[57],"results":[58],"demonstrate":[59],"that":[60],"the":[61,97],"achieves":[64],"superior":[65],"prediction":[66],"accuracy,":[67],"minimum":[69],"maximum":[71],"mean":[72],"absolute":[73],"errors":[74],"0.15":[76],"MPa":[77],"5.58":[79],"MPa,":[80],"respectively.":[81],"method":[84],"offers":[85],"promising":[87],"alternative":[88],"traditional":[90],"experimental":[91],"techniques":[92],"in":[93],"materials":[94],"science,":[95],"enhancing":[96],"accuracy":[98],"efficiency":[100],"relationship":[104],"predictions.":[105]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}