{"id":"W4366268673","doi":"10.1007/s00170-023-11403-3","title":"Laser-directed energy deposition of CuCrZr alloy: from statistical process parameter optimization to microstructural analysis","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Laser power scaling; Microstructure; Laser; Deposition (geology); Indentation hardness; Process variable; Response surface methodology; Thermal conductivity; Alloy; Titanium alloy; Composite material; Optics; Process (computing); Computer science","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":[0.00008201214,0.0001509459,0.0002928814,0.0006739676,0.00003844752,0.00003851529,0.0005500605,0.00008327585,0.00012337],"category_scores_gemma":[0.0001100273,0.0001154897,0.00007120161,0.0003400344,0.0000607953,0.0001455277,0.00008521628,0.0001447433,0.000003730646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006405086,"about_ca_system_score_gemma":0.00001492418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002358628,"about_ca_topic_score_gemma":0.00002365542,"domain_scores_codex":[0.9989522,0.00001990429,0.0004471263,0.0001356785,0.0002764117,0.0001686738],"domain_scores_gemma":[0.999166,0.0002100976,0.0002270209,0.0001374582,0.0002193667,0.00004002469],"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":[0.0001117338,0.000009950297,0.00004262273,0.00001153632,0.0006658829,0.00002791494,0.00007986223,0.9400592,0.03876073,0.00004236088,0.0001138686,0.02007434],"study_design_scores_gemma":[0.0002602936,0.00006643873,0.001719911,0.00004471692,0.0001198901,0.00002363955,0.00009109551,0.01708943,0.9739984,0.006237085,0.0002252238,0.0001238372],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8782955,0.00003535704,0.1204324,0.0003379976,0.0004569759,0.00004897747,0.0001538296,0.000223561,0.00001548777],"genre_scores_gemma":[0.9761482,0.0001356618,0.02344106,0.00004092801,0.00009197262,0.000009226427,0.0001004398,0.00002313352,0.000009312632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9352377,"threshold_uncertainty_score":0.4709535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005729627108022788,"score_gpt":0.2411771120579026,"score_spread":0.2354474849498798,"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."}}