{"id":"W3129736317","doi":"10.1007/s00170-021-06724-0","title":"Exploration of hardness variations for additive manufactured thin-walled components built by multi-axis tool paths","year":2021,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Mitacs","keywords":"Materials science; Wedge (geometry); Surface finish; Surface roughness; Mechanical engineering; Computer science; Engineering drawing; Geometry; Composite material; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002427161,0.0002527456,0.0003989017,0.0003497289,0.0001109756,0.00004402779,0.001057634,0.000206597,0.00004015122],"category_scores_gemma":[0.0005817641,0.0002084328,0.0001857005,0.0001119274,0.0001452889,0.0004042743,0.0002359636,0.0005149515,0.000004172494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001706705,"about_ca_system_score_gemma":0.0000422697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004279321,"about_ca_topic_score_gemma":0.00000907943,"domain_scores_codex":[0.9984446,0.00003306003,0.0007026421,0.0002180775,0.0003413412,0.0002602592],"domain_scores_gemma":[0.9981949,0.0003226194,0.0005232797,0.0003638771,0.0005646489,0.00003068999],"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":[0.0005600788,0.0006265356,0.00007724041,0.0001716968,0.003155862,0.0001458384,0.0013461,0.1763857,0.4507348,0.008015293,0.00979824,0.3489826],"study_design_scores_gemma":[0.001311671,0.00007204625,0.0004977438,0.0001181804,0.00004474449,0.00007374512,0.00055457,0.001844292,0.9516192,0.0282738,0.01538929,0.0002006962],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6207976,0.0003166258,0.3738286,0.002555201,0.001590074,0.000255053,0.0002632336,0.0003461202,0.00004757775],"genre_scores_gemma":[0.9583921,0.000563836,0.04059338,0.00005367582,0.00009214687,0.00005215266,0.00008003334,0.00004058597,0.0001321069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5008844,"threshold_uncertainty_score":0.8499642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02313437120521923,"score_gpt":0.2644282319318279,"score_spread":0.2412938607266086,"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."}}