Predictive modeling of surface and dimensional features of vapour-smoothened FDM parts using self-adaptive cuckoo...

Area of Science:

• Additive Manufacturing
• Materials Science
• Computational Intelligence

Background:

• Fused Deposition Modeling (FDM) offers advantages but suffers from surface roughness and dimensional variability.
• These limitations restrict the use of FDM parts in critical applications.
• Vapor smoothing (chemical finishing) is a post-processing technique to enhance FDM part quality.

Purpose of the Study:

• To determine optimal FDM and vapor smoothing parameters.
• To maximize surface finish, hardness, and dimensional accuracy of FDM parts.
• To develop a predictive model for post-processed FDM prototypes using a self-adaptive cuckoo search algorithm.

Main Methods:

• Vapor smoothing of FDM-printed hip prostheses using acetone vapors.
• Design of Experiments (DOE) with six varying parameters and three repetitions.
• Implementation of a self-adaptive cuckoo search algorithm with five objective functions derived from regression analysis.

Main Results:

• Development of a predictive model correlating input and output parameters for vapor-smoothed FDM parts.
• Successful optimization of parameters to address surface finish, hardness, and dimensional accuracy.
• Confirmatory experiments showed a strong agreement between predicted and actual measurements.

Conclusions:

• The self-adaptive cuckoo search algorithm effectively optimizes FDM and vapor smoothing parameters.
• Achieved significant improvements in surface finish, hardness, and dimensional accuracy for FDM parts.
• The developed methodology enhances the suitability of FDM for functional prototypes and critical applications.