Improvement of Additive Manufacturing Processes Through Cognitive Ergonomics Analyses: A Case Study in Fused Deposition Modeling

This article presents an approach to improving additive manufacturing (AM) processes by examining mental workload and human error during component fabrication using fused deposition modeling (FDM). A cross-sectional study involving experts and novice participants was conducted using a multi-stage approach based on Hierarchical Task Analysis (HTA), the NASA TLX Workload, and the Systematic Human Error Prediction Approach (SHERPA). A sample of two experts and two novice participants was studied. The HTA technique analyzed three main tasks: canister disk replacement, software configuration, and nozzle change. Each was divided into several subtasks. The mental workload results indicate that, among expert participants, the NASA-TLX dimensions with the highest average weighted scores were performance (77.5), time demand (65), and frustration (65). Among novice participants, the dimensions with the greatest impact were effort and frustration (90) and mental demand (87.5). The SHERPA method identified 19 human errors: 13 were action errors (68.4%), 3 were verification (checking) errors (15.7%), 2 were recovery errors (10.5%), and 1 was a selection error (5.2%). These results indicate differences in mental workload dimensions between experts and novices that may affect performance and human–machine interaction during additive manufacturing processes. Accordingly, preventive and corrective actions were recommended to minimize errors that can lead to material waste and financial losses. The study also identified low-to-high demand across key dimensions and a substantial number of errors in interactions with AM interfaces.