{"id":"W4409170190","doi":"10.1007/s00170-025-15471-5","title":"Enhanced melt pool temperature prediction by leveraging its temperature history in directed energy deposition using machine learning","year":2025,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canada Research Chairs","keywords":"Deposition (geology); Fused deposition modeling; Energy (signal processing); Materials science; Computer science; Artificial intelligence; Process engineering; Mechanical engineering; Engineering; Metallurgy; Geology; 3D printing; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001512905,0.0002260561,0.0002727518,0.0006074063,0.00008565896,0.00005561174,0.0005049241,0.0001956348,0.00005190727],"category_scores_gemma":[0.00009251039,0.0001865478,0.00005925142,0.0001375498,0.00004774621,0.0003813221,0.00009021021,0.000739559,7.192953e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007637831,"about_ca_system_score_gemma":0.00004818511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002401557,"about_ca_topic_score_gemma":0.00002366347,"domain_scores_codex":[0.9988602,0.00004534058,0.0004671422,0.0001778916,0.0002323292,0.0002170493],"domain_scores_gemma":[0.9993908,0.00007781867,0.000219052,0.0001156288,0.000171109,0.0000256136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001107713,0.00001993867,0.00001347657,0.00003358012,0.0001244175,0.00002581287,0.00008532681,0.09947809,0.8931671,0.00007188351,0.0002931644,0.006576467],"study_design_scores_gemma":[0.0005909211,0.00003455273,0.0001665444,0.000356757,0.00002195451,0.00009722132,0.00009794337,0.002790741,0.9877808,0.0007900379,0.007128024,0.000144489],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884225,0.004852698,0.003870344,0.0004811105,0.00187,0.00007932027,0.00002087448,0.0002781435,0.000125048],"genre_scores_gemma":[0.9975319,0.001321004,0.0006563351,0.0001336979,0.0001327767,0.00001066058,0.00003092095,0.00002959818,0.0001531435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09668735,"threshold_uncertainty_score":0.76072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004366908952018053,"score_gpt":0.2047206054206182,"score_spread":0.2003536964686001,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}