{"id":"W2898626266","doi":"10.20944/preprints201811.0025.v1","title":"Effect of SLM Process Parameters on the Quality of Al Alloy Parts; Part I: Powder Characterization, Density, Surface Roughness, and Dimensional Accuracy","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Selective laser melting; Materials science; Surface roughness; Porosity; Aerospace; Process (computing); Design of experiments; Surface finish; Process window; Quality (philosophy); Characterization (materials science); Microstructure; Mechanical engineering; Process engineering; Composite material; Computer science; Nanotechnology; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001459472,0.0004552098,0.0007635563,0.00004747446,0.00008608331,0.00003279121,0.0004033842,0.0002314613,0.0003837325],"category_scores_gemma":[0.0008052292,0.0003386388,0.0001169372,0.00007509172,0.0002701227,0.0001186148,0.0006755993,0.0003664631,0.00004468829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002927989,"about_ca_system_score_gemma":0.00005056343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005562683,"about_ca_topic_score_gemma":0.000003611258,"domain_scores_codex":[0.9976192,0.0003891609,0.0007112957,0.0005916749,0.0004206172,0.0002680343],"domain_scores_gemma":[0.9974105,0.0009970125,0.0005315242,0.0007406795,0.0002460106,0.00007431243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001803617,0.0003827307,0.5041722,0.02111072,0.00165854,0.000009586252,0.003116468,0.06213975,0.4038446,0.0002328085,0.0004665558,0.001062406],"study_design_scores_gemma":[0.0002031353,0.00004863903,0.2002584,0.0004912079,0.00005635623,0.00000154299,0.000008845785,0.0002836374,0.7976936,0.0003539846,0.000304721,0.0002959215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978341,0.00003753796,0.00005364826,0.0001530215,0.0006583185,0.0007369156,0.0002411948,0.0001242466,0.0001610222],"genre_scores_gemma":[0.9993398,0.0001337999,0.00002266152,0.000071901,0.00008870407,0.00007693451,0.0001841468,0.0000551571,0.00002694514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.393849,"threshold_uncertainty_score":0.9999065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06765173955887464,"score_gpt":0.3300885113332535,"score_spread":0.2624367717743789,"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."}}