Prediction model of the depth of the femtosecond laser micro-milling of PMMA. (December 2019)

• Record Type: Journal Article
• Authors: Volpe, Annalisa; Trotta, Gianluca; Krishnan, Udith; Ancona, Antonio
• Is Part Of: Optics & laser technology. Volume 120(2019)
• Publication Date: 2019-12
• Subjects: Femtosecond laser -- Micromilling -- Design of Experiment (DoE) -- Predictive model -- PMMA; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2019.105713
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: The Power analysis defines a number of replicas equal to two. The pulse energy is the main factors affecting the depth. A regression predictive model has been identified and validated. The good prediction capability of the regression model has been proved. Abstract: The femtosecond laser technology is emerging as a powerful and flexible tool for the fabrication of miniaturized polymeric devices, thanks to the micrometric precision and the minimum thermal damage on the workpiece obtainable through ultrafast laser ablation. However, parametrization of femtosecond laser processes is often based on a trial and error approach, which requires a lot of expensive experimental efforts. The design of experiment (DoE) approach can offer a methodical way to quickly determine the laser process settings limiting the use of resources. In this work, we define an accurate DoE procedure to estimate the influence of the laser repetition rate, pulse energy, scanning speed, and hatch distance on the fs-laser micromilling process of PMMA specimens in terms of depth of removed material (Dh ). We show that the laser pulse energy is the parameter that mainly affects the milling depth. A predictive model describing the relationship between the response variable depth and the main laser parameters is defined and then validated.