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Polymers are being used in a wide range of Additive Manufacturing (AM) applications and have been shown to have tremendous potential for producing complex, individually customized parts. In order to improve part quality, it is essential to identify and monitor the process malfunctions of polymer-based AM. The present work endeavored to develop an a...
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... a negative skew, the tail on the left side of the probability density function is longer or fatter than the right side. On the contrary, a positive skew indicates that the tail on the right side is longer or fatter than the left side (Figure 3). Generally speaking, the probability density distribution of AE is an abnormal distribution, i.e., the skewness is not zero. ...
Citations
... Bolt loosening in FFF printers can be detected by AE [29]. Z. Yang developed a method using AE technology to identify filament breakage [30]. H. Li collected AE data under different flow rates to identify various flow states [31]. ...
In the fused filament fabrication (FFF) deposition process, the filament material undergoes a complex process of thermal melting and reshaping. Macroscopic geometric accuracy and mechanical strength are the main concerns of the printed model by most researchers. However, there is limited research on the microscopic raster bead process, especially on its dynamic evolution, even though this is crucial for ensuring accuracy and strength. In addition, the online monitoring system for FFF processes is not well-developed, the quality of finished samples being mainly evaluated by their appearance. To fill up these gaps, this study focuses on the investigation of the microprocesses of FFF to gain in-depth understanding of raster bead evolution and its acoustic emission (AE) generation characteristics. A model of material extrusion was developed based on the wall jet impingement model. A finite element (FE) analysis revealed a mutual microscopic compression between different raster beads, in which the deformed edges compress subsequent raster beads, leading to different dynamic stresses. Experimental studies based on AE show that raster compression becomes more severe with the increase in infill density, while higher material temperature exacerbates raster compression, and faster printing speed increases compression stress. The FE simulation and experiments reveal the cumulative effects of compression in continuous printing. This study demonstrated that AE can be an effective method for online monitoring of the micro-deposition process of raster beads. In summary, this study introduces an AE-based method for monitoring the interactions between raster beads during the deposition process. The research explores a method for online monitoring of the micro-deposition process of raster beads, which has the potential to improve the geometric accuracy of FFF.
... Currently, there is a single method for monitoring the 3D printing process (e.g., physical sensors or video monitoring [13,14]), and these methods have significant limitations. Most studies focus on local monitoring and defect detection of a single 3D printer [15,16], and there is a lack of research on remote monitoring of multiple printers. ...
... Although existing studies have made significant progress in 3D printing process monitoring, such as Kousiatza et al. using fiber Bragg grating sensors for strain and temperature monitoring [13], Yang et al. using acoustic emission technology for material fracture detection [14], and Kakade et al. solving the problem of material flow by means of a rotary encoder and a pressure sensor [30], these approaches all rely on a single monitoring method. In addition, visual monitoring techniques have received extensive attention from researchers, with Sánchez et al. using a Raspberry Pi for local video monitoring [31], Liu et al. developing an embedded remote monitoring system [32], and Nuchitprasitchai et al. utilizing a camera for comprehensive local monitoring [33]. ...
With the advancement of Industry 4.0, 3D printing has become a critical technology in smart manufacturing; however, challenges remain in the integrated management, quality control, and remote monitoring of multiple 3D printers. This study proposes an intelligent cloud monitoring system based on the SharkNet dynamic network, IoT, and artificial neural networks (ANNs). The system utilizes a SharkNet dynamic network to integrate low-cost sensors for environmental monitoring to enable low-latency data transmission and deploys ANN models on the cloud for print quality prediction and process parameter optimization. Next, we experimentally validated the system using the Taguchi design and ANN-based analysis, focusing on optimizing printing process parameters and improving surface quality. The main results show that the designed system has a communication delay of 40–50 ms and 99.8% transmission reliability under moderate load, and the system reduces the surface roughness prediction error to less than 17.2%. In addition, the ANN model outperforms conventional methods in capturing the nonlinear relationships of the variables, and the system can be based on the model to improve print quality and productivity by enabling real-time parameter adjustments. The system retains a high degree of scalability in terms of real-time monitoring and parallel or complex control of multiple devices, which demonstrates its potential for applications in smart manufacturing.
... For Metal AM, AE has been utilized for in-situ monitoring [22]. AE has been adapted to monitor small scale polymer AM for health monitoring including filament feed breakage/slippage and process failure diagnosis [23] [24]. To the best of the author's knowledge, no research to adapt AE to large-scale polymer AM has been performed. ...
... To monitor the FDM process, several authors opt for in-situ indirect monitoring approaches. These can vary from the use of optical cameras as found in [8], [9], and signal processing techniques associated with feature extraction of data acquired by vibration sensors [10], [11] and acoustic sensors [5], [12]. Examples on the use of signal processing techniques in FDM is the work of [12], who employed the instantaneous skewness of the raw acoustic signal to monitor the filament breakage defect, and the work of [14] that used several statistics including kurtosis and skewness to detect clogging and filament lack during the manufacturing of a monolayer part. ...
... These can vary from the use of optical cameras as found in [8], [9], and signal processing techniques associated with feature extraction of data acquired by vibration sensors [10], [11] and acoustic sensors [5], [12]. Examples on the use of signal processing techniques in FDM is the work of [12], who employed the instantaneous skewness of the raw acoustic signal to monitor the filament breakage defect, and the work of [14] that used several statistics including kurtosis and skewness to detect clogging and filament lack during the manufacturing of a monolayer part. ...
This work proposes a monitoring strategy based on
kurtosis and skewness of sound signals to detect and classify the
machine conditions in fused deposition modeling (FDM). The
methodology consisted in experimental tests conducted in a 3D
printer in which an electret microphone was attached to the
extruder support. The signals were acquired by an oscilloscope at
200 kHz, and then digitally processed in MATLAB. The results
showed that the proposed parameter along with machine learning
models produced a significant improvement when compared to the
use of the skewness and kurtosis alone.
... Exploring monitoring techniques and methods during the extrusion process will be a crucial step in advancing the understanding and control of the extrusion states in FFF technology, ultimately leading to improved product quality and broader applications of AM. However, the existing research on monitoring extrusion process mainly focuses on nozzle blockage [4,5], semi-blockage [6], filament runout [7][8][9] and filament breakage [10]. Bukkapatnam and Clark [11] used accelerometers to acquire data in the printing process. ...
... It is worth mentioning that, as our printer operates with the print head moving solely in the Z-direction and the print platform in the X-Y plane, the extrusion head remained fixed during the experiments. Similar handles can also be found in [4,6,7,10], where fixed extrusion heads were used to monitor issues such as filament breakage and nozzle clogs. Besides, these three extrusion speeds are used here as examples to assess the effectiveness of AE and the proposed method. ...
Fused filament fabrication (FFF) is one of the most popular techniques of additive manufacturing. However, product quality issues still limit the further application of FFF technology. Filament extrusion state has a great influence on the quality of FFF fabricated products, since both under-extrusion and over-extrusion can lead to the deterioration of product quality. Therefore, monitoring the filament extrusion states is vital and essential. This paper aims to monitor the filament extrusion state by acoustic emission (AE). To achieve this goal, experiments are conducted on a desktop FFF machine, where the states of under-extrusion and over-extrusion are induced by different extrusion speeds. Original AE signals are collected during the experiments. Confronted with the challenge posed by the susceptibility of AE signals to noise during the complex extrusion process and different conditions, one calculates the statistical distribution of the features defined on the raw AE signals, without the need for noise reduction steps. The k-nearest neighbor (KNN) algorithm is then adopted to identify the different extrusion states, where the Bhattacharyya distance is employed to measure the distances or similarities of the calculated distributions. The findings demonstrate the successful identification of various extrusion states induced by different extrusion speeds through the presented method. The outcomes of this study pave the way for the development of an affordable in-situ FFF monitoring system with comprehensive capabilities.
... Table 3 briefly summarizes acoustic emission testing used for process monitoring during fused deposition modeling. It is observed that the AET can identify the defects caused by insufficient printing parameters like printing velocity, improper layer height, blockage in the extruding system, etc. [70][71][72][73]. The fault in the extruder can be detected and classified using the data obtained from AET. ...
... These states are further examined by combining the features extracted from time and frequency domain data. Linear discriminant analysis is suggested to overcome randomness and reduce data dimension (a) AE sensor mounted at extruder shell to monitor filament breakage [70] (b) AE sensor attached to platform for status recognition [77] (c) AE sensor mounted on the extruder to monitor machine condition [71] (d) AE sensor mounted on step-per motor of the extruder to monitor filament feeding process [72] [78]. Fourier transform and frequency domain analysis are other techniques to identify filament feeding issues [74] Li et al. [79] suggest mounting an AE sensor on the platform to identify and diagnose the printed elements. ...
... A hidden semi-Markov model can also be utilized for fault diagnosis [73]. The probability distribution of AE signals proves valuable in distinguishing different machine conditions in the AM process [72]. In the realm of feature reduction techniques, the LDA approach has shown superior performance compared to traditional methods like neighborhood components analysis (NCA), sparse filtering, neighborhood preserving embedding (NPE), locality preserving projection (LPP), and PCA. ...
Additive manufacturing transforms the industry by integrating innovative and intelligent technology, resulting in less material waste and faster prototyping. However, qualitative ambiguities are a significant barrier to digital fabrication methods to manufacture essential parts that require great precision and accuracy. However, qualitative ambiguities are a substantial barrier to digital fabrication methods to manufacture crucial parts that demand higher precision and accuracy. As a result, process monitoring techniques during production are becoming increasingly important. Acoustic emission testing is a prominent nondestructive testing approach that has demonstrated its capacity to detect and locate minute and internal developing cracks, allowing for real-time damage monitoring. This study briefly discussed different additive manufacturing processes, their influential parameters, and monitoring techniques, with particular emphasis on acoustic emission techniques. This study provides extensive recommendations for process monitoring of fused deposition modeling, powder bed fusion and directed energy deposition methods using acoustic emission testing. The different approaches used for handling the acoustic emission data and the effect of defects on acoustic emission signal parameters are also reviewed in this study.
... In [21], a realtime monitoring of the flow state in the nozzle of FFF was proposed, where the authors claim that the method can effectively monitor the acoustic emission signal of the internal state to distinguish different extrusion shapes in real time. Filament breakage in FDM was investigated by [37] through an acoustic emission technique. The results indicated that the instantaneous skewness could be used as a preliminary indicator for filament breakage. ...
The fused deposition modeling (FDM) process, also known as 3D printing, deals with the manufacture of parts by adding layers of fused filament. Research on manufacturing process monitoring is on the rise, with an emphasis on investigating low-cost transducers as substitutes for the traditional, pricier options. The present study addresses a critical gap in the literature concerning the monitoring of the FDM process using acoustic signals from an electret microphone attached to the extruder. By employing an extensive signal processing and feature extraction analysis, including RMS values, ratio of power (ROP), and count statistics, this research uncovers distinguishable patterns in raw signals that relate to different machine conditions such as normal operation, extruder clogging, and filament shortages. Additionally, machine learning algorithms, specifically neural networks and support vector machine (SVM), are utilized to classify these machine conditions. Notably, signal filtering is found to significantly improve the classification models. The spectral analysis further contributes to characterizing the printing process, especially in identifying frequency values associated with defects. In conclusion, the methodology developed in this study holds promise for real-time monitoring systems, as it showcases high accuracy in classifying machine conditions and offers the potential to ensure quality and detect anomalies early in the printing process. Future research is encouraged to refine the methodology and explore its scalability across different FDM systems and materials.
... In fact, AE has previously been introduced in MEX 3D-printed samples [31,32], even some have applied AE in rigid materials to distinguish between different printing configurations [33,34]. However, most of the references do not apply AE during the mechanical testing of the samples, instead, the authors usually use AE to evaluate the process of 3D printing in different situations such as for the detection of filament breaking during the printing [35], to diagnose machine faults like extruder blockage or running out of material [36], for the detection and identification of possible failures during the printing of the first layer [37,38], or the recognition of common MEX distortions as warping [39]. Therefore, the challenge is to analyze the mechanical behavior of the MEX 3D printed samples through AE. ...
The use of the Material Extrusion technique with Thermoplastic Elastomers is currently growing because of the large number of benefits of this family of materials. They are processable materials with high flexibility, which makes them very useful, for example, in biomedical applications that require flexible objects with complex
geometries. This study aims to characterize a specific polymer, namely polyether-block-amide-based polymer (PEBA), by analyzing its anisotropic behavior in printed samples and investigating the mechanical properties based on different printing orientations. Three orientations (X, Y, and Z) were used to relate the printing orientation to the type of bonds formed in the samples: intra-layer bonds, inter-layer bonds, and the deposited
filament. Tensile tests following ASTM D638 were conducted to measure sample rigidity, while Acoustic Emission, an advanced Non-Destructive Technique, was employed to examine the trend of the failure process.
The presence of voids was also observed to assess printing quality, which is influenced by the printing orientation and alters the quality of bonds. The results revealed that samples printed horizontally exhibited higher Young’s Modulus values and fewer voids in the inner parts. Vertically printed samples displayed inferior mechanical properties and a greater number of voids. Consequently, the intra-layer yielded better bond formation and
minimized voids. Acoustic Emission analysis corroborated these findings by demonstrating distinct energy distribution patterns among the different printing orientations. Hits were concentrated at maximum stresses, primarily observed in the vertically printed samples, which experienced macroscopic failure. Furthermore, this particular specimen exhibited a vertical asymptote near the maximum stress level. The analysis of the energy of Acoustic Emission hits demonstrated a reasonably good fit with the Gutenberg-Richter (GBR) law based on the printing direction.
... Filament breakage due to nozzle clogging or other failures can be identified using AE signals. Yang et al. [76] proposed a mathematical and experimental approach (M3, M4) wherein they identified AE signals to have different probability distributions after breakage. The results indicate that instantaneous skewness could be used as an indicator for detecting broken filament. ...
Additive Manufacturing (AM) is critical for the fourth industrial revolution (i.e., Industry 4.0). It involves printing a 3D object layer-by-layer from scratch. Fused filament fabrication (FFF), one of the most widely used AM technology, has been adopted by commercial and domestic consumers. With the recent addition of metal filaments, FFF caters to a broad spectrum of manufacturing industry requirements. Cybersecurity and Quality Assurance (QA) of the FFF process is an active research area. Like any other cyber-physical system, FFF exhibits many side channels (SCs), including acoustic and thermal emissions, vibrations, etc. Researchers in the QA domain use SCs to predict defects in the printed parts. Cybersecurity researchers, on the other hand, utilize SCs to identify malicious anomalies in the process. While the aims are different, there are definite overlaps in both communities' acquisition and analysis methodologies. As the two communities bring distinct skill sets and expertise , we find an opportunity to bring them closer through a systematic study of available work and identifying the commonalities and distinctions to motivate the consumption of cross-domain knowledge. Our approach to systematizing the knowledge is based on identifying the available SC, the acquisition and analysis methodologies, performance statistics , associated challenges, and future research directions. This knowledge consolidation and systematization exercise will not only help the new researchers aiming to explore SCs in the FFF process but also highlight collaboration opportunities between QA and cybersecurity communities.
... Filament breakage due to nozzle clogging or other failures can be identified using AE signals. Yang et al. [76] proposed a mathematical and experimental approach (M3, M4) wherein they identified AE signals to have different probability distributions after breakage. The results indicate that instantaneous skewness could be used as an indicator for detecting broken filament. ...
Additive Manufacturing (AM) is critical for the fourth industrial revolution (i.e., Industry 4.0). It involves printing a 3D object layer-by-layer from scratch. Fused filament fabrication (FFF), one of the most widely used AM technology, has been adopted by commercial and domestic consumers. With the recent addition of metal filaments, FFF caters to a broad spectrum of manufacturing industry requirements. Cybersecurity and Quality Assurance (QA) of the FFF process is an active research area. Like any other cyber-physical system, FFF exhibits many side channels (SCs), including acoustic and thermal emissions, vibrations, etc. Researchers in the QA domain use SCs to predict defects in the printed parts. Cybersecurity researchers, on the other hand, utilize SCs to identify malicious anomalies in the process. While the aims are different, there are definite overlaps in both communities' acquisition and analysis methodologies. As the two communities bring distinct skill sets and expertise , we find an opportunity to bring them closer through a systematic study of available work and identifying the commonalities and distinctions to motivate the consumption of cross-domain knowledge. Our approach to systematizing the knowledge is based on identifying the available SC, the acquisition and analysis methodologies, performance statistics , associated challenges, and future research directions. This knowledge consolidation and systematization exercise will not only help the new researchers aiming to explore SCs in the FFF process but also highlight collaboration opportunities between QA and cybersecurity communities.
