{"id":"W4392359608","doi":"10.1007/s00170-024-13348-7","title":"Development of an efficient multi-scale model to predict residual stresses and distortions in the laser powder bed fusion process for Inconel-718","year":2024,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; National Research Council Canada","keywords":"Residual stress; Materials science; Hardening (computing); Finite element method; Inconel; Residual; Strain hardening exponent; Structural engineering; Composite material; Computer science; Algorithm; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003678507,0.0006923695,0.0008860353,0.0003911179,0.0008103162,0.0007628934,0.001127739,0.001474739,0.0019336],"category_scores_gemma":[0.0007066383,0.0007307332,0.0009753202,0.0004218798,0.0003264529,0.000770211,0.0004364692,0.0008317735,0.000379763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079911,"about_ca_system_score_gemma":0.001986678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03391955,"about_ca_topic_score_gemma":0.02262227,"domain_scores_codex":[0.9998745,0.00001732415,0.0000081337,0.00002543415,0.00005291461,0.00002175409],"domain_scores_gemma":[0.9996754,0.0001474555,0.00003037151,0.00003105917,0.00009538048,0.00002036145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002140786,0.00003006985,0.00053094,0.00001751159,0.00001011389,0.00002965526,0.0000114159,0.9933008,0.00206416,0.0003141612,0.0001170682,0.003552681],"study_design_scores_gemma":[0.000002467484,0.000005959872,0.00008499182,5.963224e-7,0.000001601463,0.000001939373,0.000001692406,0.9993297,0.0004728471,0.0000373705,0.00005894802,0.000001882187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4366865,0.0004726526,0.5469902,0.0003690094,0.0001347017,0.0002337694,0.0007453562,0.003307058,0.01106079],"genre_scores_gemma":[0.9567606,0.0001430162,0.03890909,0.00004019824,0.00001052034,0.0001611812,0.0003132893,0.0001961214,0.003465957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03391955,"threshold_uncertainty_score":0.06744421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624640289228032,"score_gpt":0.2854383232195155,"score_spread":0.2691919203272352,"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."}}