A framework for process anomaly detection in 3D concrete printing

Extrusion-based 3D concrete printing offers a transformative opportunity for waste-free fabrication of complex and materially optimized building components. However, the current reliance on manual observation and ad-hoc parameter tuning introduces significant variability into the process and limits the scalability of 3D concrete printing. Existing systems offer no predictive insight into failure events and remain dependent on trial-and-error methods, resulting in increased material consumption, higher costs, and prolonged production times. To address this, the paper introduces a comprehensive framework for process anomaly detection tailored to two-component extrusion-based 3D concrete printing systems. The proposed framework integrates systematic sensor instrumentation, heterogeneous data acquisition, and signal feature characterization to enable real-time monitoring of system health. Non-destructive sensors are deployed to measure environmental conditions, slurry pumpability, hydration behavior, and workability. Collected signals are analyzed using a suite of processing techniques — including frequency analysis, amplitude envelope detection, curve fitting, and magnitude deviation analysis — to extract critical features indicative of emerging anomalies. The methodology presented in this paper establishes a generalizable framework for anomaly detection, using data collected from physical experiments, where the specific metric values derived from the data are tied to the feedstock used in this study for validation. The paper results demonstrate the ability of the proposed methods to detect failure modes — such as pump clogging, material segregation, and two-component mixing head clogging — ahead of human operator detection. These findings confirm the viability of the proposed framework for anomaly detection.