Additive Manufacturing of Continuous Fiber-Reinforced Polymer Composites via Fused Deposition Modelling: A Comprehensive Review.

Additive manufacturing (AM) has arisen as a transformative technology for manufacturing complex geometries with enhanced mechanical properties, particularly in the realm of continuous fiber-reinforced polymer composites (CFRPCs). Among various AM techniques, fused deposition modeling (FDM) stands out as a promising method for the fabrication of CFRPCs due to its versatility, ease of use, flexibility, and cost-effectiveness. Several research papers on the AM of CFRPs via FDM were summarized and therefore this review paper provides a critical examination of the process-printing parameters influencing the AM process, with a focus on their impact on mechanical properties. This review covers details of factors such as fiber orientation, layer thickness, nozzle diameter, fiber volume fraction, printing temperature, and infill design, extracted from the existing literature. Through a visual representation of the process parameters (printing and material) and properties (mechanical, physical, and thermal), this paper aims to separate out the optimal processing parameters that have been inferred from various research studies. Furthermore, this analysis critically evaluates the current state-of-the-art research, highlighting advancements, applications, filament production methods, challenges, and opportunities for further development in this field. In comparison to short fibers, continuous fiber filaments can render better strength; however, delamination issues persist. Various parameters affect the printing process differently, resulting in several limitations that need to be addressed. Signifying the relationship between printing parameters and mechanical properties is vital for optimizing CFRPC fabrication via FDM, enabling the realization of lightweight, high-strength components for various industrial applications.

Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario.

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject's forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer-Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.

Adaptive filtering of physiological noises in fNIRS data.

The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.

Classification of somatosensory cortex activities using fNIRS.

The ability of the somatosensory cortex in differentiating various tactile sensations is very important for a person to perceive the surrounding environment. In this study, we utilize a lab-made multi-channel functional near-infrared spectroscopy (fNIRS) to discriminate the hemodynamic responses (HRs) of four different tactile stimulations (handshake, ball grasp, poking, and cold temperature) applied to the right hand of eight healthy male subjects. The activated brain areas per stimulation are identified with the t-values between the measured data and the desired hemodynamic response function. Linear discriminant analysis is utilized to classify the acquired data into four classes based on three features (mean, peak value, and skewness) of the associated oxy-hemoglobin (HbO) signals. The HRs evoked by the handshake and poking stimulations showed higher peak values in HbO than the ball grasp and cold temperature stimulations. For comparison purposes, additional two-class classifications of poking vs. temperature and handshake vs. ball grasp were performed. The attained classification accuracies were higher than the corresponding chance levels. Our results indicate that fNIRS can be used as an objective measure discriminating different tactile stimulations from the somatosensory cortex of human brain.

Single-trial lie detection using a combined fNIRS-polygraph system.

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into "true" and "lie" classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph.

Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water.

Given that approximately 80% of blood is water, we develop a wireless functional near-infrared spectroscopy system that detects not only the concentration changes of oxy- and deoxy-hemoglobin (HbO and HbR) during mental activity but also that of water (H2O). Additionally, it implements a water-absorption correction algorithm that improves the HbO and HbR signal strengths during an arithmetic task. The system comprises a microcontroller, an optical probe, tri-wavelength light emitting diodes, photodiodes, a WiFi communication module, and a battery. System functionality was tested by means of arithmetic-task experiments performed by healthy male subjects.