Design of an In-Process Quality Monitoring Strategy for FDM-Type 3D Printer Using Deep Learning

Sampedro, Gabriel; Agron, Danielle Jaye S.; Amaizu, Gabriel Chukwunonso; Kim, Dong-Seong; Lee, Jae-Min

doi:10.3390/app12178753

Cited by 23 publications

(7 citation statements)

References 28 publications

(28 reference statements)

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“…These systems provide real-time feedback and enable modification of process parameters during printing. A study has proposed the use of sensors and machine learning algorithms to prevent nozzle clogging, achieving an impressive prediction accuracy rate of 97.2% for the future state of the 3D print [213]. Such systems aim to eliminate the need for operator monitoring and enhance print success.…”

Section: In-process Monitoringmentioning

confidence: 99%

Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy

Kassab,

Al Nabhani,

Mohanty

et al. 2023

Polymers

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The concept of the circular economy has emerged as a promising solution to address the mounting concerns surrounding plastic waste and the urgent need for sustainable resource management. While conventional centralized recycling remains a common practice for plastic waste, centralized facilities may prove inadequate in handling the ever-increasing volumes of plastic waste generated globally. Consequently, exploring alternative recycling methods, such as distributed recycling by additive manufacturing, becomes paramount. This innovative approach encompasses actively involving communities in recycling practices and promotes a circular economy. This comprehensive review paper aims to explore the critical aspects necessary to realize the potential of distributed recycling by additive manufacturing. In this paper, our focus lies on proposing schemes that leverage existing literature to harness the potential of distributed recycling by additive manufacturing as an effective approach to plastic waste management. We explore the intricacies of the recycling process, optimize 3D printing parameters, address potential challenges, and evaluate the mechanical properties of recycled materials. Our investigation draws heavily from the literature of the last five years, as we conduct a thorough critical assessment of DRAM implementation and its influence on the properties of 3D printing structures. Through comprehensive analysis, we reveal the potential of recycled materials in delivering functional components, with insights into their performance, strengths, and weaknesses. This review serves as a comprehensive guide for those interested in embracing distributed recycling by additive manufacturing as a transformative approach to plastic recycling. By fostering community engagement, optimizing 3D printing processes, and incorporating suitable additives, it is possible to collectively contribute to a more sustainable future while combatting the plastic waste crisis. As progress is made, it becomes essential to further delve into the complexities of material behavior, recycling techniques, and the long-term durability of recycled 3D printed components. By addressing these challenges head-on, it is feasible to refine and advance distributed recycling by additive manufacturing as a viable pathway to minimize plastic waste, fostering a circular economy and cultivating a cleaner planet for generations to come.

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Section: In-process Monitoringmentioning

confidence: 99%

Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy

Kassab,

Al Nabhani,

Mohanty

et al. 2023

Polymers

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“…One common printing error is nozzle clogging [ 13 , 14 ]. In order to minimize this detrimental issue, numerous efforts have been made by researchers to overcome nozzle clogging during the printing process [ 13 , 21 , 22 ]. Gutierrez et al proposed a method to decrease the clogging deposition rate of alumina inclusions in continuous casting nozzles through three simultaneous measures: flow modification, the use of raw materials with a low impurity content, and smoothed internal surfaces [ 13 ].…”

Section: Experimental Workmentioning

confidence: 99%

“…Plant studies with these nozzles, together with water modeling showed that the current strategy may significantly reduce clogging occurrence. More recently, as a response to the problem of nozzle clogging, Sampedro et al created a real-time process monitoring system capable of accurately forecasting abnormal behaviors in the FDM printing method [ 21 ]. A network of collaborative sensors was utilized to collect time series data, which were then processed by the suggested machine learning method.…”

Section: Experimental Workmentioning

confidence: 99%

Thermal Effects on Mechanical Strength of Additive Manufactured CFRP Composites at Stable and Cyclic Temperature

et al. 2022

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Additive manufacturing (AM) techniques can be applied to produce carbon-fiber-reinforced polymer (CFRP) elements. Such elements can be exposed to different environmental factors, e.g., temperature, moisture, and UV radiation, related to their operational conditions. From a variety of environmental factors, the temperature is one of the most typical. Temperature strongly influences matrix material joining together CFRP components, resulting in material strength reduction. Therefore, it is important to understand processes in the composite material caused by temperature. This experimental work investigated the thermal effects on the performances of AM CFRP composites. Specimens with unidirectional (UD) alignments of the fiber reinforcement were printed using the fused deposition modeling (FDM) technique. The printed specimens were subjected to two different thermal conditions: stable continuous at 65 ∘C and cyclic temperature between 50 and 70 ∘C. Tensile testing was performed to study the mechanical strength and Young’s modulus of AM UD-CFRPs. In order to investigate the morphological structure on the surface of AM specimens, an optical microscope, scanning electron microscope (SEM), and digital microscope were utilized. Untreated (intact) samples attained the highest average tensile strength value of 226.14 MPa and Young’s modulus of 28.65 GPa. The ultimate tensile strength of the sample group subjected to stable heat treatment decreased to 217.99 MPa, while the thermal cycling group reduced to 204.41 MPa. The Young’s modulus of the sample group subjected to stable thermal exposure was decreased to 25.39 GPa, while for the thermal cycling group, it was reduced to 20.75 GPa. The visual investigations revealed that the intact or untreated specimen group exhibited lateral damage in top failure mode (LAT), the thermally stable group underwent edge delamination in the middle (DGM) as the nominated failure mode, and the explosive breakage at gauge in the middle (XGM) failure mode occurred in the sample from the thermal cycling group. Based on morphological observations at the microscale, the delamination, fiber pull-out, and matrix cracking were the dominant damages in the 3D-printed tensile-tested specimens. The molecular chains of the polymer changed their structure into an amorphous one, and only local motions of stretching occurred when the specimens were exposed to stable heating (prolonged). In the case of thermal cycling, the strain gradients were accumulated in the matrix material, and the local stresses increased as a result of the reheating and re-cooling exposure of the polymeric composites; the molecular motion of the long-range polymer structure was reactivated several times. Micro-cracking occurred as a result of internal stresses, which led to material failure and a reduction of the mechanical properties.

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“…By recording data with sensors [23] within a soundproof room [24], the ANN can anticipate future signals and vibrations, thereby identifying print results and avoiding filament damage. In conclusion, the integration of machine learning techniques [17] into 3D printing monitoring systems has the potential to significantly improve the reliability, efficiency, and quality of the additive manufacturing process [25]. By effectively detecting and predicting machine conditions, manufacturers can reduce the likelihood of failures and optimize their operations for maximum output and quality.…”

Section: Introductionmentioning

confidence: 99%

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

Dirhamsyah,

Riza,

Rizal

2023

J. meas. eng.

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This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes.

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Design of an In-Process Quality Monitoring Strategy for FDM-Type 3D Printer Using Deep Learning

Cited by 23 publications

References 28 publications

Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy

Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy

Thermal Effects on Mechanical Strength of Additive Manufactured CFRP Composites at Stable and Cyclic Temperature

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

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