{"id":"W4407180700","doi":"10.1007/s11666-025-01934-4","title":"Aeroacoustic Process Monitoring and Anomaly Detection in Cold Spray Additive Manufacturing","year":2025,"lang":"en","type":"article","venue":"Journal of Thermal Spray Technology","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Standards and Technology; U.S. Department of Commerce","keywords":"Materials science; Anomaly detection; Gas dynamic cold spray; Manufacturing process; Process (computing); Spray drying; Spray forming; Metallurgy; Nanotechnology; Composite material; Engineering; Computer science; Data mining; Chemical engineering; Coating","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001224698,0.0001107098,0.0001967079,0.0006742258,0.00003638057,0.0000188984,0.0001353114,0.000178372,0.00000689551],"category_scores_gemma":[0.00004895694,0.0001086557,0.00003138492,0.0002804623,0.00004297418,0.0001273519,0.00003025065,0.0005413715,0.000001206225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001364866,"about_ca_system_score_gemma":0.00001676385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001821804,"about_ca_topic_score_gemma":0.00001892829,"domain_scores_codex":[0.9993801,0.00001162705,0.000280852,0.0000847078,0.00007451503,0.0001681684],"domain_scores_gemma":[0.9997182,0.00004034573,0.00008389848,0.0000810662,0.00004912982,0.00002736292],"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":[0.0001388227,0.00008568617,0.02171502,0.0002300182,0.0001980871,0.0001949416,0.0002102393,0.08317508,0.6871474,0.000336454,0.00001906207,0.2065492],"study_design_scores_gemma":[0.0008457584,0.0001140059,0.03007282,0.0002801717,0.00004519588,0.0001155017,0.0005870181,0.007468693,0.9593362,0.0004402442,0.000514978,0.0001794321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928372,0.000413462,0.005627007,0.00009007318,0.0005073908,0.0000770086,0.000001349601,0.00009826257,0.0003482546],"genre_scores_gemma":[0.9994451,0.0001084886,0.0003365217,0.000006943181,0.0000545036,0.000005573868,1.147315e-7,0.00001291915,0.00002986892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2721888,"threshold_uncertainty_score":0.4430852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00304008891658386,"score_gpt":0.2129786905913779,"score_spread":0.209938601674794,"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."}}