Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

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García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio;
  • Publisher: MDPI
  • Journal: Materials,volume 9,issue 2 (issn: 1996-1944, eissn: 1996-1944)
  • Related identifiers: pmc: PMC5456480, doi: 10.3390/ma9020082
  • Subject: QC120-168.85 | statistical learning techniques | regression analysis | multivariate adaptive regression splines (MARS) | Engineering (General). Civil engineering (General) | Technology | Article | TA1-2040 | artificial bee colony (ABC) | hyperparameter selection | T | Electrical engineering. Electronics. Nuclear engineering | TK1-9971 | Microscopy | milling tool wear monitoring | QH201-278.5 | Descriptive and experimental mechanics

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and ex... View more
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