In-Process Monitoring of Continuous Fiber Additive Manufacturing through Force/Torque Sensing on the Nozzle

Backer, Wout De; Sinkez, Paul G.; Chhabra, Ishit; Tooren, Michael J. Van; Bergs, Arturs P.

doi:10.2514/6.2020-1632

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…The other sub-categories addressed are large-format MEX [58][59][60][61][62][63], bioprinting [64], and direct ink writing [65][66][67][68]. In addition to the processing of conventional filaments, some studies have examined manufacturing processes for continuous fibers [69,70], pastes [71], and pellets [60,62,63,72,73]. Publications addressing MEX machines with delta [74][75][76][77][78][79][80][81][82][83][84][85][86][87][88] and robot arm [58,72,73,[89][90][91][92] kinematics are exceptions to the considered Cartesian systems.…”

Section: Methodsmentioning

confidence: 99%

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Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

et al. 2021

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Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach.

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Section: Methodsmentioning

confidence: 99%

“…Furthermore, in the MEX of continuous fibers, fibers that are not fed at a sufficient rate by the delivery mechanism result in analyzable changes in forces. Exceedingly high forces, in turn, cause fiber pull-out and shearing [69].…”

Section: Force and Pressure Monitoringmentioning

confidence: 99%

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

et al. 2021

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Pre-process Optimisation of Filament Feed Rate in Fused Filament Fabrication by Using Digital Twins and Machine Learning

Rossi,

Moretti,

Senin

2023

Lecture Notes in Mechanical Engineering

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Machine condition monitoring for defect detection in fused deposition modelling process: a review

He,

Zhu,

Zhang

et al. 2024

Int J Adv Manuf Technol

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Additive manufacturing (AM), also known as 3D printing (3DP), refers to manufacturing technologies that build up the desired geometries by adding materials layer by layer. Common meltable and fusible materials such as polymers, metals, and ceramics could be used in 3DP processes. During decades of development, products made by 3DP can now achieve stringent industrial standards at comparable costs compared to those traditionally manufactured. Improving 3DP technologies is required to make them more competitive and acceptable than their counterparts. However, achieving this is challenging since the quality of printing products is still heavily dependent on many cost-driven factors. Inadequate quality, impaired functionality, and reduced service life are three main consequences of 3DP’s failures. To effectively detect and mitigate defects and failures of 3DP products, machine condition monitoring (MCM) technologies have been used to monitor 3D printing processes. With the help of those dedicated algorithms, it could also prevent failures from occurrence by alerting operators to take appropriate actions accordingly. This study systematically reviews the MCM technologies used in a typical 3DP process—the fused deposition modelling (FDM), identifying their advantages and disadvantages. The mentioned MCM technologies include but are not limited to traditional MCM (sensors only), aided with analytical and artificial intelligence (AI) tools. The MCM techniques focus on the defects of the 3DP process. The detection and identification of those defects are investigated. Furthermore, research trends on developing MCM technologies, including challenges and opportunities, are identified for improving the FDM process. This review highlights the developed methodologies of MCM that are applied to FDM processes to detect and identify abnormalities such as defects and failures. The evaluations of defects are elaborated to deepen the comprehension of the essence of the defects, including their cause, severity, and effect. A detailed deliberation about identifying the critical components for the successful application of 3DP MCM systems was done. Finally, this review indicates the technical barriers that need to be overcome to enhance the performance of monitoring, detection, and prediction by MCM and associated technologies.

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In-Process Monitoring of Continuous Fiber Additive Manufacturing through Force/Torque Sensing on the Nozzle

Cited by 8 publications

References 2 publications

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

Pre-process Optimisation of Filament Feed Rate in Fused Filament Fabrication by Using Digital Twins and Machine Learning

Machine condition monitoring for defect detection in fused deposition modelling process: a review

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