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The final product of additive manufacturing (AM) or 3D printing critically depends on the surface quality. An experimental study on the 3D printed intake manifold flange using acrylonitrile butadiene styrene (ABS) material was executed by varying the four process parameters. A fused deposition modeling (FDM) based 3D printer was used to fabricate the flanges. The association between the parameters and the surface roughness of printed ABS flanges was investigated. A feed forward neural network (FFNN) model trained on particle swarm optimization (PSO) optimized with a genetic algorithm (GA) was used to estimate the surface roughness. A Box-Behnken design (BBD) with printing parameters at three levels was used, and 25 parts were fabricated. The suggested model demonstrated a coefficient of determination (R2) of 0.9865 on test values, mean of root-mean-square-error (RMSE) of 0.1231 after 500 times training for generalization. And also mean of overfitting factor is 0.7110. This means that the suggested system could generalize. Comparing the results from the suggested model and ANN, the suggested hybrid model outperformed ANN in predicting the surface roughness values with no overfitting. This suggests that GA optimized PSO based FFNN may be a more suitable method for estimating product quality in terms of surface roughness. [ABSTRACT FROM AUTHOR]

Objective: This project aims to develop a wearable device to suppress both the essential and resting tremor and investigate its effectiveness. Materials and Methods: This study details the development and assessment of a wearable device for upper extremity tremors. The wearable device underwent a comprehensive design and a prototype was produced with a 3D-printer. To refine the functionality of the prototype, a motor that mimics tremor was attached to a 3D-printed prototype. Then, the printed prototype was applied to the hand model, and tested its effectiveness for tremor suppressing. The wearable device was further investigated on patients with essential tremor and Parkinson's disease seeking treatment at Neurology Clinics. We recorded the tremor data and processed and visualized the recorded data by using the MatLab (version R2021a, MathWorks Inc., USA) software. Results: The wearable device effectively decreased the tremors both during the simulation phase and the patient testing phase. The data from the wearable device revealed a notable decrease in the amplitude of the tremor. This decrease signifies an achievement of tremor suppression. Conclusion: The prototype of the wearable device signifies a remarkable efficacy in tremor supression. It holds promise for being a potential solution to alleviate the tremor symptoms of essential tremor and Parkinson's disease patients. [ABSTRACT FROM AUTHOR]

We present the design and control of a pneumatic ankle-foot orthosis (P-AFO) device powered via bi-directional pneumatic rotary actuator and a pneumatic artificial muscle for rehabilitation assistance and treatment of neuromuscular disorders. The rotary actuator and the pneumatic muscle assist with dorsiflexion and plantar flexion, respectively. The prototype is also equipped with simple sensor system for gait pattern analysis. The P-AFO has the capability of 20 degrees dorsiflexion from the plantar flexion and 12 degrees dorsiflexion from the neutral position of an ankle joint. The data-driven predictive control (DDPC) algorithm has been designed for P-AFO to follow desired gait cycle trajectories while rectifying the nonlinearity and uncertainties of the pneumatic actuators. The design of DDPC is realized from the subspace identification matrices acquired by the input-output values obtained as a result of an open-loop operation. The control structure is completely data-based without certain use of a model in the control implementation. In order to control the developed P-AFO prototype device, the suggested controller was implemented in a real-time operating system. Experimental studies are performed to compare the proposed controller with a three-term controller (PID) in trajectory tracking of the P-AFO. [ABSTRACT FROM AUTHOR]

An adaptive robotic system has been developed to be used for hand rehabilitation. Previously developed exoskeletons are either very complex in terms of mechanism, hardware and software, or simple but have limited functionality only for a specific rehabilitation task. Some of these studies use simple position controllers considering only to improve the trajectory tracking performance of the exoskeleton which is inadequate in terms of safety and health of the patient. Some of them focus only on either passive or active rehabilitation, but not both together. Some others use EMG signals to assist the patient, but this time active rehabilitation is impossible unless different designs and control strategies are not developed. The proposed mechanical structure is extremely simple. The middle and the proximal phalanxes are used as a link of consecutively connected two 4-bar mechanisms, respectively. The PIP and MCP joints are actuated by a single electro mechanical cylinder to produce complex flexion and extension movements. It is simpler than similar ones from aspect with the mechanical structure and the biodynamic fit of the hand, making it practicable in terms of production and personal usage. Simple design lets to implement adaptive compliance controller for all active and passive rehabilitation tasks, instead of developing complex and different strategies for different rehabilitation tasks. Furthermore, using the Luenberger observer for unmeasured velocity state variable, an on-line estimation method is used to estimate the dynamic parameters of the system. This makes possible to estimate the force exerted by the patient as well, without a force sensor. [ABSTRACT FROM AUTHOR]