{"id":"W4247334339","doi":"10.1504/ijmms.2009.024346","title":"Optimal process planning for compound laser cutting and punch using Genetic Algorithms","year":2009,"lang":"en","type":"article","venue":"International Journal of Mechatronics and Manufacturing Systems","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Nesting (process); Machining; Genetic algorithm; Sheet metal; Motion planning; Tool path; Path (computing); Ant colony optimization algorithms; Engineering; Ant colony; Algorithm; Process (computing); Sequence (biology); Computer science; Engineering drawing; Mathematical optimization; Mechanical engineering; Mathematics; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002311827,0.000157922,0.0002198799,0.0001695758,0.00008086293,0.0002447973,0.0001576585,0.00007074936,0.000001625469],"category_scores_gemma":[0.000008859238,0.0001438771,0.00004514419,0.00001711931,0.00001132896,0.0002600212,0.00001789996,0.0001461204,1.233709e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007420817,"about_ca_system_score_gemma":0.00002239255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004900255,"about_ca_topic_score_gemma":1.244699e-7,"domain_scores_codex":[0.9990062,0.000009503414,0.000411657,0.0001305426,0.0002570153,0.0001851167],"domain_scores_gemma":[0.9994777,0.00004101065,0.0002126579,0.00005240713,0.0001380137,0.00007825181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002216519,0.000009630982,0.00006737508,0.000124928,0.0001126768,0.00001089127,0.0002928727,0.993551,0.0001597176,0.00008673214,0.00001316705,0.005548852],"study_design_scores_gemma":[0.001064461,0.0001495889,0.0004849673,0.0004684792,0.0000632728,0.0005696507,0.0003884744,0.9828618,0.01197963,0.0005370838,0.001178248,0.0002544016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7308666,0.001525944,0.2666333,0.00004414898,0.0007503797,0.0001182801,0.000007610612,0.00002710318,0.00002664587],"genre_scores_gemma":[0.9829001,0.0001934404,0.01639452,0.00001824762,0.0004519426,0.000002845433,0.000004072845,0.00002130008,0.00001353871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2520335,"threshold_uncertainty_score":0.5867137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420396903120876,"score_gpt":0.26251089278624,"score_spread":0.2483069237550312,"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."}}