{"id":"W2921499278","doi":"10.1002/mdp2.56","title":"Short review on modeling approaches for metal additive manufacturing process","year":2019,"lang":"en","type":"article","venue":"Material Design & Processing Communications","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Residual stress; Distortion (music); Process (computing); Economic shortage; Computer science; Process modeling; Residual; Manufacturing process; Manufacturing engineering; Process engineering; Work in process; Engineering; Materials science; Algorithm; Metallurgy; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004580904,0.000376636,0.000486741,0.0001003325,0.0003302603,0.0002265648,0.0008491134,0.0001108791,0.0001147654],"category_scores_gemma":[0.00004396681,0.0003463578,0.00008585615,0.0001080513,0.00006071563,0.0004156425,0.0001058243,0.0001845084,0.00005938452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007036638,"about_ca_system_score_gemma":0.00005007157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001891145,"about_ca_topic_score_gemma":8.293066e-7,"domain_scores_codex":[0.9985312,0.00009980947,0.000427828,0.0003556194,0.0001967752,0.0003888232],"domain_scores_gemma":[0.9987748,0.0001576477,0.00008861488,0.000794344,0.000111528,0.0000730942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006528485,0.0005489617,0.000006628683,0.05104808,0.0006408296,0.000003935287,0.002080151,0.5982523,0.02060073,0.001680855,0.002928554,0.3215561],"study_design_scores_gemma":[0.0005888609,0.0002274606,0.00003164015,0.007265261,0.0003277941,0.00002104324,0.0002508862,0.3560442,0.6157136,0.006818294,0.01109534,0.001615637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1553146,0.01883281,0.784561,0.001261778,0.002021821,0.01101364,0.001486475,0.004179773,0.0213281],"genre_scores_gemma":[0.9863314,0.001691133,0.009871754,0.0001237304,0.0001389233,0.001162714,0.0004950703,0.0001209921,0.00006428263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8310168,"threshold_uncertainty_score":0.9998989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058679132124328,"score_gpt":0.2905715238276149,"score_spread":0.1847036106151821,"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."}}