{"id":"W3186551427","doi":"10.1115/msec2021-63965","title":"Development of Intertwined Infills to Improve Multi-Material Interfacial Bond Strength","year":2021,"lang":"en","type":"article","venue":"Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Slicing; Interlacing; Computer science; Tracing; Material properties; Interlocking; Material flow; Process (computing); Material efficiency; Joint (building); Engineering drawing; Mechanical engineering; Structural engineering; Materials science; Engineering; Artificial intelligence; Composite material; Computer graphics (images)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.000684832,0.002527084,0.002404287,0.001621981,0.0006529463,0.0007406865,0.001279283,0.0008702801,0.001002086],"category_scores_gemma":[0.0003468673,0.002691013,0.0004366849,0.000272291,0.0003089536,0.001363695,0.001922089,0.001038818,0.0002134426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008178339,"about_ca_system_score_gemma":0.0001922491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004927471,"about_ca_topic_score_gemma":0.00003985104,"domain_scores_codex":[0.9906197,0.0001455715,0.003148994,0.002407179,0.001195652,0.002482926],"domain_scores_gemma":[0.9958919,0.0003145924,0.0009310332,0.001632523,0.0002025771,0.001027369],"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":[0.0005128055,0.0005650302,0.00002156988,0.003582292,0.001518753,0.0002082822,0.002792126,0.02645649,0.7431592,0.00009080324,0.0007784955,0.2203142],"study_design_scores_gemma":[0.002399627,0.0002279921,0.01712687,0.0008528347,0.00015566,0.00006285375,0.0005613074,0.001183218,0.9546119,0.0001455865,0.01992037,0.002751772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700154,0.0001417191,0.0174398,0.0001341147,0.004865606,0.001693292,0.001534384,0.003957481,0.0002182032],"genre_scores_gemma":[0.9369115,0.0001588014,0.05995,0.0001128996,0.0006030759,0.0005384754,0.0009786298,0.0004737944,0.0002728083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2175624,"threshold_uncertainty_score":0.9999111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008239807507881527,"score_gpt":0.216076703861344,"score_spread":0.2078368963534624,"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."}}