A framework for fused deposition modelling process evaluation using multivariate process capability analysis.

Area of Science:

• Manufacturing Engineering
• Quality Control
• Materials Science

Background:

• Additive Manufacturing (AM) processes require continuous improvement for advanced applications.
• Evaluating AM process outputs against specifications is crucial for quality assurance.
• Univariate process capability indices can be misleading for AM's multi-correlated quality characteristics.

Purpose of the Study:

• To propose a general methodological framework for evaluating AM processes using a multivariate process capability index (MPCI).
• To assess the capability of the Fused Deposition Modeling (FDM) process using the developed MPCI framework.
• To identify areas for improvement in AM processes based on variation analysis.

Main Methods:

• Developed a methodological framework for AM process evaluation using MPCI.
• Selected Fused Deposition Modeling (FDM) as the investigation case.
• Established specification limits for critical quality characteristics (QCs).
• Examined QC data for normality, stability, and correlation to meet MPCI assumptions.
• Estimated MPCI via large sample simulation and calculated percent of nonconforming (PNC).
• Conducted sensitivity analysis of the FDM process and identified assignable causes of variation.

Main Results:

• The FDM process mean is shifted for all QCs, with part diameter exhibiting the most variation.
• Process data were not normally distributed; a transformation algorithm effectively reduced skewness.
• The FDM process was found incapable with most specification limits, except for very coarse tolerances.
• Sensitivity analysis provided insights into process performance under varying conditions.

Conclusions:

• The proposed MPCI framework offers a robust method for evaluating complex AM processes.
• The FDM process requires significant improvements to meet typical manufacturing specifications.
• Understanding data distribution and variation is key to effective AM process control and enhancement.