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

Oleff, Alexander; Küster, Benjamin; Stonis, Malte; Overmeyer, Ludger

doi:10.1007/s40964-021-00192-4

Cited by 79 publications

(33 citation statements)

References 237 publications

(191 reference statements)

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“…Their flowchart is limited to in-situ printer health and product quality monitoring. Oleff et al do systematic literature research in order to find quality-related research gaps, giving examples for a few FDM-process errors [19]. Song and Telenko examine FDM-print failures in a university makerspace [20].…”

Section: Related Workmentioning

confidence: 99%

Development of a Quality Gate Reference Model for FDM Processes

Randermann¹,

Hinrichs²,

Jochem³

2023

Quality Control - An Anthology of Cases

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Additive manufacturing (AM) enables industries to accomplish mass customization by creating complex products in small batches. For this purpose, fused deposition modeling (FDM) is widely used in 3D printing where the material is applied layer-by-layer from a digital model to form a three-dimensional object. There still exist problems in FDM processes regarding the failure rate of printed parts. Failures vary from deformed geometry, clogged nozzles, and dimensional inaccuracies to small parts not being printed that may be attributed to various process steps (e.g., poor quality CAD models, converting issues, overheating, poor quality filament, etc.). The majority of these defects are preventable and are caused by imprudent try-and-error print processes and troubleshooting quality control. The aim of this chapter is to propose a quality gate reference process with defined requirement criteria to prevent the occurrence of defects. The framework shall be applied in quality control and in-situ process monitoring to enhance overall manufacturing quality.

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Section: Related Workmentioning

confidence: 99%

Development of a Quality Gate Reference Model for FDM Processes

Randermann¹,

Hinrichs²,

Jochem³

2023

Quality Control - An Anthology of Cases

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“…Cerro et al [17] also developed a machine learning model to predict surface roughness of printed parts manufactured by using FDM 3D printer. Moreover, in-situ monitoring of FFF is reviewed [18,19], which then derives the FFF technology to the next generation of systems by enabling robust closed-loop control scheme in 3D printing.…”

Section: Related Workmentioning

confidence: 99%

A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach

Tamir

Xiong

Fang

et al. 2022

Int J Adv Manuf Technol

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Fused deposition modelling (FDM) 3D printing, as a supporting technology in social manufacturing and cloud manufacturing, is a rapidly growing technology in the era of industry 4.0. It produces objects with the layer-by-layer material accumulation technique. However, qualitative uncertainties are the common challenges yet. In order to assure print quality, studying the error causing parameters and minimizing their effects is important. This paper presents a feedback-based error compensation strategy, which integrates fuzzy inference system and grey wolf optimization algorithm. The objectives are twofold. First, the possible errors in FDM 3D printing are discussed in detail and optimal error causing parameters are obtained in percentage. This is used to understand the effects of the printing errors in every phase of the 3D printing process. From the nine optimization configuration trials used, Config-6 that has 100 number of iterations and 60 wolves is

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“…The schemes for BC at all other surface orientations resemble those for the ±x directions (Eqs. (10) and (11) for the O(Δx) scheme, Eqs. ( 12) and ( 13) for the O Δx 2 scheme).…”

Section: Boundary Condition Treatmentmentioning

confidence: 99%

“…infrared thermography [9,10]). However, each technique has its limitations, and they can provide only partial information with limited accuracy [11]. On the other hand, numerical modelling provides a fast, economical yet powerful means to probe the temperature variations in FFF parts.…”

Section: Introductionmentioning

confidence: 99%

T4F3: temperature for fused filament fabrication

2022

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Temperature fields and their variations in printed parts are the basis for understanding the physical process of fused filament fabrication (FFF). However, reliable temperature data are still rather limited to date. This article presents a three-dimensional transient-state model to simulate the temporal and spatial temperature variations in FFF printed parts. Model variables range from geometry dimensions and (dynamic) material properties to process parameters, covering all important physical phenomena, including conduction anisotropy and radiant heat transfer. The validation of the model is performed against six sets of experimental temperature data obtained with different geometries, machines, materials, processes, temperature measuring methods, etc. Insights in the thermal process are also reported. For example, the heat penetration depth in printing with poly(lactic acid) is limited to 3 mm, and the Biot number intimately characterises the reheating peaks in temporal profiles. This model shows the potential to become a standardised tool to study the thermal characteristics of FFF printed parts. It is made openly available on website https://iiw.kuleuven.be/onderzoek/aml/technologyoffer. Graphic abstract

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

Cited by 79 publications

References 237 publications

Development of a Quality Gate Reference Model for FDM Processes

Development of a Quality Gate Reference Model for FDM Processes

A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach

T4F3: temperature for fused filament fabrication

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