Process Optimization and Predictive Modeling of Femtosecond Laser Precision Milling for Commercial PMMA Slices.

Area of Science:

• Materials Science and Engineering
• Manufacturing Processes
• Laser Technology

Background:

• Poly(methyl methacrylate) (PMMA) is a widely used polymer.
• Precision milling is crucial for fabricating PMMA components.
• Femtosecond laser milling offers high precision but requires careful process optimization.

Purpose of the Study:

• To optimize femtosecond laser precision milling parameters for PMMA slices.
• To develop predictive models for surface roughness (Ra) and milling depth (h).
• To provide guidance for controlling surface quality in PMMA laser milling.

Main Methods:

• Systematic analysis of pulse energy, scanning line speed, spacing, and repetition frequency.
• 3D surface morphology measurement using a laser confocal microscope.
• Development of quadratic response surface models using Box-Behnken design.

Main Results:

• Pulse energy and repetition frequency increase milling depth; scanning parameters decrease it.
• Surface roughness shows non-monotonic responses to scanning parameters.
• Developed models for Ra and h achieved high accuracy (R² > 0.99) with low prediction errors.

Conclusions:

• Femtosecond laser milling process parameters significantly impact PMMA surface quality.
• The developed predictive models accurately forecast milling depth and surface roughness.
• The models serve as a valuable tool for optimizing PMMA laser milling and ensuring surface quality.