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1. Effects of infill patterns on the strength and stiffness of 3D printed topologically optimized geometries

   https://doi.org/10.1108/RPJ-11-2019-0290  

   Hmeidat, Nadim S. ; Brown, Bailey ; Jia, Xiu ; Vermaak, Natasha ; Compton, Brett ( August 2021 , Rapid Prototyping Journal)

   Purpose Mechanical anisotropy associated with material extrusion additive manufacturing (AM) complicates the design of complex structures. This study aims to focus on investigating the effects of design choices offered by material extrusion AM – namely, the choice of infill pattern – on the structural performance and optimality of a given optimized topology. Elucidation of these effects provides evidence that using design tools that incorporate anisotropic behavior is necessary for designing truly optimal structures for manufacturing via AM. Design/methodology/approach A benchmark topology optimization (TO) problem was solved for compliance minimization of a thick beam in three-point bending and the resulting geometry was printed using fused filament fabrication. The optimized geometry was printed using a variety of infill patterns and the strength, stiffness and failure behavior were analyzed and compared. The bending tests were accompanied by corresponding elastic finite element analyzes (FEA) in ABAQUS. The FEA used the material properties obtained during tensile and shear testing to define orthotropic composite plies and simulate individual printed layers in the physical specimens. Findings Experiments showed that stiffness varied by as much as 22% and failure load varied by as much as 426% between structures printed with different infill patterns. The observed failure modes weremore »also highly dependent on infill patterns with failure propagating along with printed interfaces for all infill patterns that were consistent between layers. Elastic FEA using orthotropic composite plies was found to accurately predict the stiffness of printed structures, but a simple maximum stress failure criterion was not sufficient to predict strength. Despite this, FE stress contours proved beneficial in identifying the locations of failure in printed structures. Originality/value This study quantifies the effects of infill patterns in printed structures using a classic TO geometry. The results presented to establish a benchmark that can be used to guide the development of emerging manufacturing-oriented TO protocols that incorporate directionally-dependent, process-specific material properties.« less
2. Effects of Size and Surface Treatment on Fatigue Life of Fused Filament Fabrication Manufactured Acrylonitrile Butadiene Styrene Parts

   https://doi.org/10.1115/1.4050178  

   Huang, Jianchi ; Miscles, Eduardo ; Mellor, Tara ; Ma, Chao ; Kuttolamadom, Mathew ; Wang, Jyhwen ( August 2021 , Journal of Manufacturing Science and Engineering)

   Abstract An experimental study was conducted to study the effects of geometric size and surface treatment on the fatigue life of fused filament fabrication (FFF) manufactured acrylonitrile butadiene styrene (ABS) parts. Moore rotating-beam fatigue tests were conducted with four different levels of loadings to obtain the S–N curves. Two different sizes (control size and large size) and three different surface treatment methods (as-printed, acetone-treated, and sandpaper polished) were studied. The larger specimens had significantly decreased fatigue life because of a larger volume, and hence a greater probability of defects for crack initiation and propagation, as compared with the control specimen. The acetone-treated specimen had a smooth surface. Its fatigue life, however, decreased significantly because the acetone treatment caused internal damage that weakened the specimen and was reported for the first time. The sandpaper polished specimen also had a smooth surface, but its effect on the fatigue life was insignificant because the extruded filament direction on the specimen surface was parallel to the loading direction. The present results lead to a better understanding of the effects of geometric size and surface treatment on the fatigue performance of FFF specimens. The study also provides important insights for the design of part sizemore »and surface treatment of three-dimensional (3D) printed plastic components for fatigue loading end-use applications.« less
3. Modeling resistance increase in a composite ink under cyclic loading

   https://doi.org/10.1088/2058-8585/acbaab  

   Li, Q. ; Chung, E. ; Antoniou, A. ; Pierron, O. ( February 2023 , Flexible and Printed Electronics)

   The electrical performance of stretchable electronic inks degrades as they undergo cyclic deformation during use, posing a major challenge to their reliability. The experimental characterization of ink fatigue behavior can be a time-consuming process, and models allowing accurate resistance evolution and life estimates are needed. Here, a model is proposed for determining the electrical resistance evolution during cyclic loading of a screen-printed composite conductive ink. The model relies on two input specimen-characteristic curves, assumes a constant rate of normalized resistance increase for a given strain amplitude, and incorporates the effects of both mean strain and strain amplitude. The model predicts the normalized resistance evolution of a cyclic test with reasonable accuracy. The mean strain effects are secondary compared to strain amplitude, except for large strain amplitudes (>10%) and mean strains (>30%). A trace width effect is found for the fatigue behavior of 1 mm vs 2 mm wide specimens. The input specimen-characteristic curves are trace-width dependent, and the model predicts a decrease inN_fby a factor of up to 2 for the narrower trace width, in agreement with the experimental results. Two different methods are investigated to generate the rate of normalized resistance increase curves: uninterrupted fatigue tests (requiring ∼6–7more »cyclic tests), and a single interrupted cyclic test (requiring only one specimen tested at progressively higher strain amplitude values). The results suggest that the initial decrease in normalized resistance rate only occurs for specimens with no prior loading. The minimum-rate curve is therefore recommended for more accurate fatigue estimates.

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4. Characterization of Additively Manufactured Beta Materials

   Dana, E. ; Kumpaty, S. ; Weston, J. ( November 2022 , ASME International Mechanical Engineering Conference and Exposition)

   IMECE2022-88301 Additive manufacturing (AM) is transforming industrial production. AM can produce parts with complex geometries and functionality. However, one of the biggest challenges in the AM world is limited material options. The purpose of this research is to develop new material mixtures and determine their mechanical properties for use at the MSOE Rapid Prototyping Center and provide valuable insight into beta materials for use in AM industry. Elastomeric polyurethane (EPU 40) and Rigid polyurethane (RPU 70), resins developed by Carbon3D, are employed for this research. Initially, EPU 40 (100%) and RPU 70 (100%) were used to print tensile and hardness test specimens so that their mechanical properties could be compared to the standard values presented by Carbon3D and used as benchmarks for newly developed material. Mixtures of the two materials, EPU 40 and RPU 70, in multiple ratios were then created and used to print tensile and hardness test specimens. Data collected from tensile and hardness tests show that EPU 40 and RPU 70 can be combined in various ratios to obtain material properties that lie between the two individual components. In addition to developing these new materials, the effect of printing orientation on mechanical properties was also studied inmore »this paper.« less
5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less