{"id":"W4387569662","doi":"10.1016/j.addma.2023.103823","title":"In-situ observation of powder spreading in powder bed fusion metal additive manufacturing process using particle image velocimetry","year":2023,"lang":"en","type":"article","venue":"Additive manufacturing","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Japan Science and Technology Agency; Technology Research Association for Future Additive Manufacturing; New Energy and Industrial Technology Development Organization; Tohoku University; Swine Innovation Porc; Innovative Structural Materials Association","keywords":"Materials science; Particle image velocimetry; Discrete element method; Particle (ecology); Metal powder; Deposition (geology); Composite material; Fusion; Rotation (mathematics); Oxide; Powder metallurgy; Metallurgy; Metal; Sintering; Mechanics; Turbulence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000147893,0.0002476927,0.0002977202,0.0002385294,0.0002422589,0.000339784,0.0003020159,0.0004371312,0.000677784],"category_scores_gemma":[0.0002028713,0.0002054897,0.0002750632,0.0002565623,0.0003044037,0.0003925951,0.0001958057,0.0005577753,0.0001341688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002644531,"about_ca_system_score_gemma":0.0001936591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001058118,"about_ca_topic_score_gemma":0.001063866,"domain_scores_codex":[0.9998364,0.00001161667,0.000007859694,0.00004238292,0.00007319871,0.00002850223],"domain_scores_gemma":[0.9998682,0.00004959231,0.00003078916,0.00001112897,0.00003083429,0.000009430547],"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":[0.00006212079,0.00001785222,0.0003569024,0.00002023907,0.000002677829,0.00003949467,0.00004464321,0.0003737866,0.9971833,0.00008737558,0.00003200426,0.001779497],"study_design_scores_gemma":[0.000006937225,0.00008506328,0.003601899,0.00000204136,0.000007679966,0.00004118204,0.00006473736,0.01366897,0.982139,0.00004673729,0.0003278298,0.000007993612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988135,0.0002439877,0.0102677,0.00004760827,0.00002422101,0.00001171542,0.0001037666,0.00009844157,0.001067462],"genre_scores_gemma":[0.9939094,0.0001481111,0.005182711,0.00001530905,0.000007254427,0.00001076674,0.00006145692,0.0000141629,0.0006508256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001058118,"threshold_uncertainty_score":0.00226742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02205363920475534,"score_gpt":0.2602060549830034,"score_spread":0.238152415778248,"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."}}