Open-Source TIG-Based Metal 3D-Printing

Metal 3-D printing has been relegated to high-cost proprietary high-resolution systems and low-resolution low-cost metal inert gas (MIG) systems. In order to provide a path to high-resolution, low-cost, metal 3-D printing, this manuscript proposes a new open source metal 3-D printer design based around a low-cost tungsten inert gas (TIG) welder coupled to a commercial open source self replicating rapid prototyper. Optimal printing parameters for the machine are acquired using a novel computational intelligence software. TIG has many advantages over MIG, such as having a low heat input, clean beads, and the potential for both high-resolution prints as well as insitu alloying of complex geometries. The design can be adapted to most RepRap-class systems and has a basic yet powerful free and open source software (FOSS) package for the characterization of the 3-D printer. This system can be used for fabricating custom metal scientific components and tools, near net-shape structural metal component rapid prototyping, adapting and depositing on existing metal structures, and is deployable for in-field prototyping for appropriate technology applications

Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control

Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller\u27s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers

A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles

The evolution in micro-electro-mechanical systems technology (MEMS) has triggered the need for the development of wireless sensor network (WSN). These wireless sensor nodes has been used in many applications at many areas. One of the main issues in WSN is the energy availability, which is always a constraint. In a previous research, a relocating algorithm for mobile sensor network had been introduced and the goal was to save energy and prolong the lifetime of the sensor networks using Particle Swarm Optimization (PSO) where both of sensing radius and travelled distance had been optimized in order to save energy in long-term and shortterm. Yet, the previous research did not take into account obstacles’ existence in the field and this will cause the sensor nodes to consume more power if obstacles are exists in the sensing field. In this project, the same centralized relocating algorithm from the previous research has been used where 15 mobile sensors deployed randomly in a field of 100 meter by 100 meter where these sensors has been deployed one time in a field that obstacles does not exist (case 1) and another time in a field that obstacles existence has been taken into account (case 2), in which these obstacles has been pre-defined positions, where these two cases applied into two different algorithms, which are the original algorithm of a previous research and the modified algorithm of this thesis. Particle Swarm Optimization has been used in the proposed algorithm to minimize the fitness function. Voronoi diagram has also used in order to ensure that the mobile sensors cover the whole sensing field. In this project, the objectives will be mainly focus on the travelling distance, which is the mobility module, of the mobile sensors in the network because the distance that the sensor node travels, will consume too much power from this node and this will lead to shortening the lifetime of the sensor network. So, the travelling distance, power consumption and lifetime of the network will be calculated in both cases for original algorithm and modified algorithm, which is a modified deployment algorithm, and compared between them. Moreover, the maximum sensing range is calculated, which is 30 meter, by using the binary sensing model even though the sensing module does not consume too much power compared to the mobility module. Finally, the comparison of the results in the original method will show that this algorithm is not suitable for an environment where obstacle exist because sensors will consume too much power compared to the sensors that deployed in environment that free of obstacles. While the results of the modified algorithm of this research will be more suitable for both environments, that is environment where obstacles are not exist and environment where obstacles are exist, because sensors in this algorithm .will consume almost the same amount of power at both of these environments

Meta-model-based multi-objective optimization for robust color reproduction using hybrid diffraction gratings

International audienceWe propose an efficient and versatile optimization scheme, based on the combination of multi-objective genetic algorithms and neural-networks, to reproduce specific colors through the optimization of the geometrical parameters of metal-dielectric diffraction gratings. To illustrate and assess the performance of this approach, we tailor the chromatic response of a structure composed of three adjacent hybrid V-groove diffraction gratings. To be close to the experimental situation, we include the feasibility constraints imposed by the fabrication process. The strength of our approach lies in the possibility to simultaneously optimize different contradictory objectives, avoiding time-consuming electromagnetic calculations

Investigations of machining characteristics in upgraded MQL assisted turning of pure titanium alloy using evolutionary algorithms

Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque–Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%

Effect of optimization framework on rigid and non-rigid multimodal image registration

The process of transforming or aligning two images is known as image registration. In the present era, image registration is one of the most popular transformation tools in case of, for example, satellite as well as medical imaging analysis. Images captured by difference devices that can be processed under same registration model are called multimodal images. In this work, we present a multimodal image registration framework, upon which ant colony optimization (ACO) and flower pollination algorithms (FPA), which are two meta heuristics algorithms, are applied in order to improve the performance of a proposed rigid and non-rigid multimodal registration framework and decrease its processing time. The results of the ACO and FPA based framework were compared against particle swarm optimization and Genetic algorithm-based framework's results and seem to be promising