{"id":"W4410807818","doi":"10.1007/s40516-025-00296-7","title":"Optimization of LPBF Processing and Aging Treatment Parameters for Maraging Steel Using Genetic Algorithms: Experimental Validation and Fracture Behavior Analysis","year":2025,"lang":"en","type":"article","venue":"Lasers in Manufacturing and Materials Processing","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Maraging steel; Algorithm; Genetic algorithm; Materials science; Computer science; Metallurgy; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007912152,0.0007547871,0.0005376941,0.0007096893,0.000451193,0.0005896251,0.0005295201,0.001041564,0.001125765],"category_scores_gemma":[0.00132276,0.0002786052,0.0005291878,0.0004526463,0.0003484539,0.0004649701,0.0002322564,0.0004822906,0.0002096915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008221858,"about_ca_system_score_gemma":0.000986371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004930771,"about_ca_topic_score_gemma":0.006747089,"domain_scores_codex":[0.9998267,0.00003349089,0.000008370446,0.00004133128,0.00006223993,0.00002797305],"domain_scores_gemma":[0.9993464,0.0002805549,0.0001128695,0.0000442928,0.0002000465,0.00001591736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002145337,0.0003672468,0.001480839,0.0001624284,0.00002936793,0.00004890492,0.00005710496,0.9101595,0.0464103,0.0006795719,0.0004167466,0.03997346],"study_design_scores_gemma":[0.00002517142,0.0003887732,0.001274097,0.00001193739,0.00003395529,0.00001620781,0.0000363969,0.9695999,0.0278875,0.0002508681,0.0004625776,0.00001253122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8730232,0.0007748738,0.120819,0.0001801074,0.00005717763,0.0001190774,0.0001851153,0.0005208941,0.004320773],"genre_scores_gemma":[0.9522437,0.0001254142,0.04667636,0.00001984051,0.000003959728,0.00005588999,0.00009435743,0.00004350717,0.0007369414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004930771,"threshold_uncertainty_score":0.00980413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428179068992901,"score_gpt":0.268460618553174,"score_spread":0.254178827863245,"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."}}