This study presents the design and fabrication results of an electrothermal micro-electro-mechanical system (MEMS) actuator. Unlike traditional one-directional U-shaped actuators, this bi-directional electrothermal (BET) micro-actuator can produce displacements in two directions as a single device. The BET micro-actuator was fabricated using two-photon polymerization (2PP) and digital light processing (DLP) methods, which are 3D printing techniques. These methods have been compared to see the success of BET micro-actuator fabrication. The compound of these methods and the essential coefficients through the 3D printing operation were applied. Evaluation experiments have demonstrated that in both methods, the 3D printer can print materials smaller than 95.7 μm size features. Though the same design was used for the 2PP and DLP methods, the supporting structures were not produced with the 2PP. The BET micro-actuator was manufactured by removing the supports from the original design in the 2PP. The number of supports, the diameter, and height on the arms of the micro-actuator is 18, 4 μm, and 6 μm, respectively. Although 4 μm diameter supports could be produced with the DLP, it was not possible to produce them with 3D printing device based on 2PP. Besides, the DLP was found to be better than the 2PP for the manufacturing of asymmetrical support structures. The fabrication process has been carried out successfully by two methods. When the fabrication success is compared, the surface quality and fabrication speed of the micro-actuator fabricated with DLP is better than the 2PP. Presented results show the efficiency of the 3D printing technology and the simplicity of fabrication of the micro-actuator via 2PP and DLP. An experimental study was carried out to characterize the relationship between displacement and input voltage for the micro-actuator. Experimental results show that the displacement range of the micro-actuator is 8 μm with DLP, while 6 μm with 2PP.

Electromyography (EMG) signals are an important technique in the control applications of prostatic hand. These signals, which are measured from the skin surface, are used to perform movements such as wrist flexion / extension, forearm supination / pronation and hand opening / closing of prosthetic devices. In this study, root mean square, waveform length and kurtosis methods were applied to extracted EMG signals from flexor carpi radialis and extensor carpi radialis muscles by using two channel surface electrodes. A fuzzy logic based classification method has been applied to classify the extracted signal features. With this method, classification for different gripping movements has been successfully accomplished.

It is the aim to develop optimization techniques to separate platelets from Red Blood Cells (RBCs) after designing and analyzing a microfluidic chip in this study.

This study aims to perform optimizatione to achieve the best diffusion control between the channels by designing and analysing a microfluidic-based micromixer. The design and analysis of the micromixer were made with the COMSOL Multiphysics program. Some input and output parameters must be defined for diffusion control of the micromixer. Among these parameters, inputs are the diffusion coefficient and inlet flow rate, while outputs are velocity, pressure, and concentration. Each input parameter in the microfluidic chip affects the output of the system. To make the diffusion control in the most optimum way, the data were obtained by making much analysis. The data obtained from this program was also provided with the Fuzzy Logic method to optimize the microfluidic chip. The diffusion coefficient value (5E-11 m2/s) should be given to the channels to achieve the optimum diffusion between the micromixer channels, if the inlet flow rate value (15E-15 m3/s) is the output value of the system, the velocity is 0.09 mm/s. The pressure is 2 Pa, and the concentration is 0.45 mol/m3. These values are the optimum values obtained from the analysis without damaging the liquid’s microfluidic channels supplied to the micromixer’s inlet.

The selection of parameters affects the surface roughness in the additive manufacturing process. This study aims to determine the optimal combination of input parameters for predicting and minimising the surface roughness of samples produced by Fused Deposition Modelling on a 3D printer using a cascade-forward neural network (CFNN) and genetic algorithm. Box-Behnken Design with four independent printing parameters at three levels is used, and 25 parts are fabricated with a 3D printer. Roughness tests are performed on the fabricated parts. Models generated by the hybrid algorithm achieve the best results for predicting and optimising surface roughness in 3D-printed parts. The surface roughness prediction accuracy of the trained CFNN with optimised parameters is more accurate compared to previous random test results.

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For the past few years, different types of control techniques are being used in various fields of industry. Fuzzy logic based control system is one of them. Fuzzy logic uses statements instead of mathematical model for solving a given problem. In this paper, a normal household washing machine, which is used very often, is modeled with the help of Fuzzy logic. Both the simulation and the control of the aforementioned device have been done by using MATLAB's fuzzy logic toolbox.

Additive manufacturing (AM) or 3D printing technology is one of the preferred methods to ensure sustainability in fabrication. In addition to providing continuity in sustainability, fabrication, and diversity, it aims to improve people’s quality of life, develop the economy, and protect the environment and resources for future generations. In this study, the life cycle assessment (LCA) method was used to determine whether a product fabricated by the AM provides tangible benefits compared to traditional fabrication methodologies. LCA is an evaluation method that provides information on resource efficiency and waste generation, where the environmental impacts of a process can be calculated, measured, and reported throughout the entire life cycle, starting from the acquisition of raw materials, processing, fabrication, use, end of life, and disposal, according to ISO 14040/44 standards. This study examines the environmental impacts of the three most preferred filaments and resin materials in the AM for a 3D-printed product from the start, which consists of three stages. These stages are raw material extraction, manufacturing, and recycling. Filament material types are Acrylonitrile Butadiene Styrene (ABS), Polylactic Acid (PLA), Polyethylene Terephthalate (PETG), and Ultraviolet (UV) Resin. The fabrication process was carried out with Fused Deposition Modeling (FDM) and Stereolithography (SLA) techniques using a 3D printer. Environmental impacts for all identified steps were estimated over the life cycle using the energy consumption model. As a result of the LCA, it was seen that UV Resin was the most environmentally friendly material in the mid-point and end-point indicators. It has been determined that the ABS material also exhibits bad results on many indicators and is the least environmentally friendly. The results support those working with AM in comparing different materials’ environmental impacts and choosing an environmentally friendly material.