Towards the development of a smart fused filament fabrication system using multi-sensor data fusion for in-process monitoring


          

刊名：Rapid prototyping journal
作者：Moretti, Michele（Univ Perugia, Dept Engn, Perugia, Italy）
Bianchi, Federico（Univ Perugia, Dept Engn, Perugia, Italy）
Senin, Nicola（Univ Perugia, Dept Engn, Perugia, Italy）
刊号：780C0104
ISSN：1355-2546
出版年：2020
年卷期：2020, vol.26, no.7
页码：1249-1261
总页数：13
分类号：TH
关键词：Additive manufacturing；Multi-sensor data fusion；Fused filament fabrication；In-process monitoring
参考中译：
语种：eng
文摘：Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.



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