{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004819437,0.0005926206,0.0007534901,0.0007370196,0.0001446879,0.00009491738,0.00032845,0.0002215886,0.000541607],"category_scores_gemma":[0.0001401957,0.0006254117,0.0001360244,0.0006273285,0.000125225,0.001519569,0.0002034143,0.0004657449,0.0001794562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000295629,"about_ca_system_score_gemma":0.00004544377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001175142,"about_ca_topic_score_gemma":0.0001321031,"domain_scores_codex":[0.9969308,0.0001191222,0.0008175173,0.0006656669,0.0004807146,0.0009861937],"domain_scores_gemma":[0.9987963,0.0004354532,0.000242085,0.0003069585,0.00008785809,0.0001312898],"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":[0.0003559839,0.0002557468,0.002348429,0.002162325,0.0003992626,0.0007728037,0.007891445,0.05062118,0.9107324,0.0001338636,0.0006821351,0.02364444],"study_design_scores_gemma":[0.0008405335,0.00003782896,0.06890412,0.0004713888,0.00003750727,0.00001135301,0.001279011,0.002128722,0.9242284,0.00106816,0.0003756623,0.0006172933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967838,0.00006665107,0.0003412881,0.00004561738,0.0005036298,0.000545127,0.000300426,0.000381727,0.001031712],"genre_scores_gemma":[0.9986348,0.0001161923,0.0004296414,0.00004907943,0.0002214228,0.00009829898,0.0002563723,0.0001412521,0.00005298477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06655569,"threshold_uncertainty_score":0.9996197,"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."}}