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Includes: (1) DOE measurements (N=40, Rz and Ra), (2) time-series Rz data (424 measurements at 6 cutting speeds), (3) flank wear VB data (42 measurements), and (4) complete MATLAB pipeline (S0-S7) for DOE analysis, ML modelling, Bayesian optimisation, and tool wear 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Learning"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-249568332","label":"Manufacturing"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-253139503","label":"Surface Roughness"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-253654484","label":"Wear Modeling"}],"institutions":[{"id":"984567d5-4fc2-4c60-a728-4e53186f3b50","name":"University of West Bohemia in Pilsen","ror_id":"https://ror.org/040t43x18"}],"metrics":{"views":0,"file_downloads":0,"file_previews":0},"available":true,"method":"EXPERIMENTAL DATA COLLECTION\n\nSurface roughness (Rz, Ra) was measured using a Mitutoyo \nSurftest SJ-301 contact profilometer (ISO 4287, evaluation \nlength lm = 12.5 mm, cut-off lambda_c = 2.5 mm, Gaussian \nfilter). Workpiece material: C45 steel (EN 10083-2, AISI 1045), \ncylinder diameter 60 mm. Cutting tool: KNUX 190408-EL P20 \ninsert (ISO 1832) in MWLNR holder, nose radius r_eps = 0.80 mm. \nMachine: centre lathe MUS 25 (SU 50). All experiments dry \n(no cutting fluid).\n\nDOE experiment: 2^3 full factorial design, factors: cutting \nspeed vc (8.792 / 351.680 m/min), feed f (0.10 / 0.50 mm), \ndepth of cut ap (0.10 / 3.00 mm), 5 replicates per run, \nN = 40 total measurements (sheet: 5_DOE_RzRa).\n\nTime-series experiment: fixed f = 0.10 mm, ap = 0.20 mm, \n6 cutting speeds vc = {6, 25, 70, 110, 140, 240} m/min. \nRz measured after each pass (424 records, sheet: 2_Rz_TimeSeries). \nFlank wear VB measured optically at 20x magnification after \neach pass, ISO 3685, VBcrit = 0.30 mm (42 records, \nsheet: 3_VB_TimeSeries).\n\nDATA ANALYSIS — MATLAB SCRIPTS\n\nRequirements: MATLAB R2021b or later, Statistics and Machine \nLearning Toolbox, Global Optimization Toolbox.\n\nRun scripts in this order:\n1. S0_LoadData.m       — loads DATA_Final.xlsx, saves DATA.mat\n2. S1_DOE_Analysis.m   — ANOVA, effects, regression models\n3. S2_DOE_ML.m         — GPR, ANN, SVR with LOO-CV (~5 min)\n4. S3_DOE_Optimization.m — Bayesian opt., GA, Pareto (~5 min)\n5. S4_Rz_TimeSeries.m  — exponential model Rz = A*exp(b*tau)\n6. S5_VB_Analysis.m    — power-law VB, Taylor equation\n7. S6_Rz_VB_Correlation.m — VB-Rz correlation analysis\n8. S7_Figures.m        — composite publication figures\n\nAll figures saved as PDF (vector) and PNG (300 DPI) \nin subfolder 'figures/'. Random seed fixed (rng(42)) \nfor reproducibility. Total runtime ~15-20 minutes.\n\nNOTE: vc = 25 m/min excluded from Taylor analysis \n(R2 = -0.34, built-up edge regime). vc = 140 m/min \nexcluded (experiment ended at VB = 0.15 mm < VBcrit).","size":204813,"owner":{"profile_id":"1fd75892-5166-420f-8647-9eb0a49f783c","first_name":"Miroslav","last_name":"Gombár"},"channel":"WEB","owner_id":"1fd75892-5166-420f-8647-9eb0a49f783c","publish_date":"2026-06-01T15:28:09.946Z","data_licence":{"id":"01d9c749-3c4d-4431-9df3-620b2dcfe144","description":"You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.","url":"http://creativecommons.org/licenses/by/4.0","category":"Creative","short_name":"CC BY 4.0","full_name":"Creative Commons Attribution 4.0 International"},"related_links":[],"funders":[],"customer_id":"7377952c-6404-4428-bbfc-77242cfc301e","modified_on":"2026-05-31T06:05:14.194Z","created_on":"2026-05-31T05:52:49.661Z","confidential":false,"links":{"view":"https://data.mendeley.com/datasets/yz37pvtztx"},"repository":{"id":"MENDELEY_DATA","name":"Mendeley Data"}}