{"id":"W4389001694","doi":"10.1007/s00170-023-12661-x","title":"Investigation of residual stresses of multi-layer multi-track components built by directed energy deposition: experimental, numerical, and time-series machine-learning studies","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Residual stress; Materials science; Cube (algebra); Deposition (geology); Material properties; Ultimate tensile strength; Residual; Layer (electronics); Finite element method; Composite material; Mechanical engineering; Structural engineering; Geometry; Algorithm; Computer science; Engineering; Mathematics; Geology","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":[0.0003974067,0.0003516365,0.0003702062,0.0004703857,0.0003510528,0.000468013,0.0005541851,0.0005209816,0.0007179925],"category_scores_gemma":[0.0007227162,0.0002637986,0.0002869796,0.0004753969,0.0006011237,0.0004318593,0.0002554987,0.00036346,0.00009860453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003686594,"about_ca_system_score_gemma":0.0003061803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393386,"about_ca_topic_score_gemma":0.002532593,"domain_scores_codex":[0.9998205,0.00001190967,0.000009189504,0.00003376893,0.0001031196,0.00002155901],"domain_scores_gemma":[0.9995371,0.0001388155,0.0000779356,0.00009789672,0.0001230275,0.00002522647],"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":[0.0005183315,0.0002673292,0.008828792,0.0002592999,0.00006532144,0.0003688299,0.0003733812,0.2149456,0.7488428,0.002004365,0.0003849566,0.02314103],"study_design_scores_gemma":[0.00001762957,0.00046144,0.01999796,0.0000181245,0.00005038777,0.0001726693,0.0001590071,0.5535103,0.4243221,0.0003443028,0.0009120807,0.00003400624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912819,0.0001964352,0.007657128,0.00002553394,0.00001522552,0.000006003177,0.00006444391,0.0001125856,0.0006406935],"genre_scores_gemma":[0.9975063,0.00004802817,0.002108577,0.000003421716,0.000001143008,0.000003105793,0.00003786785,0.0000119792,0.0002794165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001393386,"threshold_uncertainty_score":0.002770543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02624086543391164,"score_gpt":0.2745748673687531,"score_spread":0.2483340019348415,"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."}}