Integrated FDM optimization with multivariate capability analysis for dimensional and compressive mechanical...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Additive Manufacturing

Background:

• Fused Deposition Modeling (FDM) enables complex part production.
• Optimizing dimensional and mechanical quality simultaneously in FDM is challenging due to numerous process parameters.
• Existing methods struggle to integrate multivariate quality characteristics.

Purpose of the Study:

• To investigate the influence of key FDM parameters on multivariate dimensional and mechanical quality.
• To develop an integrated framework for simultaneous optimization of these quality characteristics.
• To enhance process capability and performance in FDM.

Main Methods:

• Response Surface Methodology (RSM) experimental design.
• Optimization of four FDM parameters: layer thickness, extruder temperature, plate temperature, and printing speed.
• Evaluation of dimensional (length, diameter) and mechanical (compressive strength, modulus) characteristics.
• Estimation of Multivariate Process Capability Indices (MPCIs).

Main Results:

• Layer thickness and extruder temperature were identified as the most influential parameters on MPCIs.
• Optimal dimensional MPCI achieved with low layer thickness, high extruder temperature, low plate temperature, and low printing speed.
• The developed model explained 88% of the variability in mechanical MPCI.

Conclusions:

• An integrated RSM-multivariate process capability analysis approach was successfully developed for FDM.
• This framework enables simultaneous optimization of correlated dimensional and mechanical quality characteristics.
• The research improves both dimensional precision and mechanical performance in additive manufacturing processes.