{"id":"W1723345372","doi":"10.5267/j.ijiec.2015.7.003","title":"Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718","year":2015,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Indian National Science Academy","keywords":"Inconel; Surface roughness; Materials science; Carbide; Surface finish; Response surface methodology; Mechanical engineering; Metallurgy; Composite material; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006070292,0.0007610854,0.0006274972,0.0005324226,0.0002354335,0.0005202777,0.0003655015,0.0006073496,0.0009389404],"category_scores_gemma":[0.000625457,0.0003408962,0.0007601953,0.000294393,0.0001980308,0.0002091131,0.0002616302,0.0003581651,0.00008824778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000353956,"about_ca_system_score_gemma":0.0004546853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003508195,"about_ca_topic_score_gemma":0.00512851,"domain_scores_codex":[0.9997904,0.00005985931,0.00001196167,0.00003724769,0.00005590863,0.00004473637],"domain_scores_gemma":[0.9996576,0.0002178145,0.00004132256,0.0000119361,0.0000569252,0.00001427436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001402865,0.0001873211,0.001585154,0.0002177958,0.00006020568,0.00008199246,0.00003685968,0.9570017,0.01522093,0.0003004754,0.0001472593,0.02502012],"study_design_scores_gemma":[0.00001579118,0.0004325836,0.00278046,0.000008587926,0.00002995526,0.00001690519,0.00003711334,0.9909075,0.005361223,0.0001413078,0.0002616054,0.000007005024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.867196,0.0005339102,0.127981,0.00005795044,0.00002288891,0.00009895756,0.0001247841,0.0001417437,0.003842787],"genre_scores_gemma":[0.9692045,0.00009489846,0.02875549,0.00001267117,0.000003076136,0.00009369729,0.0001361917,0.000019562,0.001679864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003508195,"threshold_uncertainty_score":0.006975532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03395247119394403,"score_gpt":0.2576528264090358,"score_spread":0.2237003552150918,"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."}}