{"id":"W4404943898","doi":"10.1007/s00170-024-14869-x","title":"Implementation of a robotic framework for multi-axis supportless fused filament fabrication via volume decomposition: a practical approach","year":2024,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Workspace; Fused deposition modeling; 3D printing; Process (computing); Volume (thermodynamics); Obstacle; Fused filament fabrication; Decomposition; Field (mathematics); Robot; Computer science; Fabrication; Mechanical engineering; Engineering; Manufacturing engineering; Engineering drawing; Systems engineering; Control engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002652394,0.0001689601,0.0002388073,0.0004595597,0.00005189524,0.00004919539,0.0005897771,0.000141877,0.00003661308],"category_scores_gemma":[0.0001298404,0.000133911,0.0001341122,0.0001145418,0.0001130625,0.000208503,0.0001173748,0.0004831939,0.000004237598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002129475,"about_ca_system_score_gemma":0.00003767733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003641597,"about_ca_topic_score_gemma":0.000002432715,"domain_scores_codex":[0.9988207,0.00001627766,0.000541963,0.0001720082,0.0002482826,0.0002008238],"domain_scores_gemma":[0.9990517,0.000238334,0.0002679303,0.0002195272,0.0001976136,0.00002490466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002126498,0.0002721062,0.0002260055,0.000493814,0.001970673,0.00007788426,0.0008935053,0.1668335,0.03947247,0.03290861,0.002220505,0.7544183],"study_design_scores_gemma":[0.0004950952,0.0001784499,0.00115519,0.0001949046,0.00008228307,0.0003298292,0.001102002,0.02829649,0.912416,0.05282924,0.002736731,0.0001837428],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2442838,0.0001951052,0.7517053,0.002508467,0.0007718824,0.0002087471,0.0000144045,0.0003012551,0.00001096787],"genre_scores_gemma":[0.7314126,0.0001091088,0.2682748,0.00001757232,0.00009875435,0.00004283215,0.00001077648,0.0000237635,0.000009715897],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8729436,"threshold_uncertainty_score":0.5460734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0248437852185248,"score_gpt":0.3411439990459919,"score_spread":0.3163002138274671,"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."}}