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

Abstract

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.

The Application of Multisensor Information Fusion Technology in Environmental Restoration

“…Using the collected monitoring data, the fusion algorithm is used to measure the weight vector A of each environmental factor of the road near Zhongkai University of Agriculture and Engineering, and the fuzzy relation matrix R is as follows. According to formula (12), the fuzzy comprehensive evaluation result vector C = f0:3067, 0:3464, 0:2483, 0:0914 , 0:0071g is calculated. "Good" accounted for a relatively high proportion.…”

“…In [11], the weighted least squares method is used for multisensor fusion. Reference [12] uses the second-order adaptive weight particle filter method for multisensor information fusion. Reference [13] uses Bayesian method for data fusion.…”

Filament Transport Control for Enhancing Mechanical Properties of Parts Realised by Fused Filament Fabrication

“…In this way, the correct extrudate flow rate was ensured [ 12 ]. A FFF prototype machine [ 34 ] was used to fabricate both specimens suitable for tensile tests [ 35 ] and dynamic mechanical analysis (DMA) [ 36 ]. The FFF machine features a system designed to control the filament transport exiting the cold end.…”

“…The machine is controlled by a Megatronics 3.2 control board [ 37 ] which communicates with a PC via a serial port through the software Repetier Host [ 38 ]. The prototype is equipped with heterogeneous sensors [ 34 ] acquired by a data acquisition system developed on the LabVIEW ® platform [ 39 ].…”

“…The and signals, collected using a LabVIEW ® -based acquisition system with a sample rate of 100 Hz [ 34 , 39 ], were used to determine slippage, as described in Section 2.2 . The CLC system can be manually enabled or disabled; when the CLC is disabled, no corrections are applied to the filament transport.…”