This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.,Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.,Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.,Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.,Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.,While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

中文翻译：

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使用多传感器数据融合进行过程监控的智能熔丝制造系统的开发

本文旨在说明将多个异构传感器集成到熔丝制造 (FFF) 系统中，以及多传感器数据融合技术的实施，以支持能够监控制造过程和零件质量的“智能”机器的开发，如它正在构建中。从现成的 FFF 组件开始，本文讨论了与机器架构和 FFF 过程本身必须如何重新设计以适应异构传感器以及如何集成来自此类传感器的数据相关的问题。通过说明可检测的示例缺陷来讨论该方法的有用性。,通过异构过程中数据的聚合，特别关注在同一制造过程中获得的传感器数据在空间和时间上实现共定位。这允许检测单独传感器无法可靠检测的问题。