A Framework for Multivariate Statistical Quality Monitoring of Additive Manufacturing: Fused Filament Fabrication Process

Advances in additive manufacturing (AM) processes have increased the number of relevant applications various industries. To keep up with this development, process stability AM should be monitored, which is conducted through assessment outputs or product characteristics. However, use univariate control charts to monitor an might lead misleading results, as most additively manufactured products more than one correlated quality characteristic (QC). This paper proposes a framework for monitoring multivariate characteristics processes, and proposed was applied fused filament fabrication (FFF) process. In particular, specimens were designed produced using FFF process, their QCs identified. Then, critical data collected precise measurement system. Furthermore, we propose transformation algorithm ensure normality data. After examining correlations between investigated characteristics, exponential weighted moving average (MEWMA) chart used MEWMA parameters optimized novel heuristic technique. The results indicate that majority are not normally distributed. Consequently, efficacy technique demonstrated. addition, our findings illustrate QCs. It worth noting optimization confirm considered (i.e., FFF) relatively stable.