{"id":"W4315705626","doi":"10.1007/s00170-023-10814-6","title":"An overview of surface roughness enhancement of additively manufactured metal parts: a path towards removing the post-print bottleneck for complex geometries","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"","keywords":"Bottleneck; Surface roughness; Macro; Mechanical engineering; Quality (philosophy); Process (computing); Surface finish; Surface (topology); 3D printing; Nanotechnology; Materials science; Manufacturing engineering; Process engineering; Computer science; Engineering; Engineering drawing; Composite material; Operations management","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":[0.0006880278,0.00107754,0.0009554474,0.001433432,0.0002649614,0.001643875,0.001421135,0.00124212,0.0031848],"category_scores_gemma":[0.0005798932,0.0007593156,0.0009116508,0.001237317,0.0003929647,0.001553416,0.0007148464,0.001628752,0.002249944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005486177,"about_ca_system_score_gemma":0.0004999426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004117604,"about_ca_topic_score_gemma":0.0006595404,"domain_scores_codex":[0.9993358,0.00005050852,0.00003831223,0.0001300013,0.0003860603,0.00005921387],"domain_scores_gemma":[0.9995394,0.000157511,0.00006276755,0.00005403362,0.0001597937,0.00002648914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001612372,0.0002831996,0.0004454684,0.01232709,0.0001135316,0.0002520045,0.0001999333,0.003915626,0.3761382,0.01476983,0.00890459,0.5824893],"study_design_scores_gemma":[0.00001901344,0.001303733,0.002566759,0.001075441,0.0001894924,0.002426036,0.0001514201,0.0134103,0.3155815,0.005810303,0.6573249,0.0001412557],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02163826,0.8229944,0.1195216,0.0009643309,0.001013857,0.0001382219,0.0002669178,0.0008334736,0.0326289],"genre_scores_gemma":[0.1357419,0.7031859,0.1292301,0.001243442,0.00134537,0.0001880994,0.0006971823,0.0003905744,0.02797743],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0031848,"threshold_uncertainty_score":0.01065421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03985192771305871,"score_gpt":0.3047756276713747,"score_spread":0.264923699958316,"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."}}