Investigating the Machining Quality of Additively Manufactured Composite: Multi-Response Modeling and Evolutionary...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Polymer Composites

Background:

• Additive manufacturing (AM) offers novel material processing but requires optimization for post-processing.
• Turning is a critical finishing process for AM polymer composites.
• Controlling dimensional error (DE) and surface roughness (Ra) is essential for component quality.

Purpose of the Study:

• To investigate and optimize the turning performance of additive-manufactured polymer-based composites.
• To develop predictive models for dimensional error and surface roughness.
• To identify optimal machining parameters for minimizing both DE and Ra.

Main Methods:

• Experimental design with cutting speed, feed rate, and depth of cut as variables.
• Regression-based modeling to establish predictive equations for DE and Ra.
• Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization.

Main Results:

• High R-squared values (96.35% for DE, 92.88% for Ra) indicate robust predictive models.
• Depth of cut and cutting speed significantly influence DE (>86% explanatory power).
• Cutting speed, feed, and depth of cut are key factors for Ra (~90% contribution).
• NSGA-II identified optimal parameters: 120-180 m/min cutting speed, <0.52 mm depth of cut, 0.05-0.10 mm/rev feed.
• Validation experiments showed low prediction errors (3% for DE, 4.8% for Ra).

Conclusions:

• Regression models effectively predict turning performance (DE and Ra) for AM polymer composites.
• Optimal machining parameters were determined for simultaneous minimization of DE and Ra.
• The developed models and optimization strategy enhance the manufacturability of AM polymer composites.