Designing a Robotic Platform for Investigating Swarm Robotics

This paper documents the design and subsequent construction of a low-cost, flexible robotic platform for swarm robotics research, and the selection of appropriate swarm algorithms for the implementation of a swarm focused predominantly on target location. The design described herein is intended to allow for the construction of robots large enough to meaningfully interact with their environment while maintaining a low per-robot cost of materials and a low assembly time. The design process is separated into three stages: mechanical design, electrical design, and software design. All major design components are described in detail under the appropriate design section. The BOM for a single robot is also included, along with relevant testing information

Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips

This paper describes a method combining Bayesian optimization (BO) and a lamped-capacitance thermal circuit network model that is effective for speeding up the thermal design optimization of an electronic circuit board layout with transient heating chips. As electronic devices have become smaller and more complex, the importance of thermal design optimization to ensure heat dissipation performance has increased. However, such thermal design optimization is difficult because it is necessary to consider various trade-offs associated with packaging and transient temperature changes of heat-generating components. This study aims to improve the performance of thermal design optimization by artificial intelligence. BO using a Gaussian process was combined with the lamped-capacitance thermal circuit network model, and its performance was verified by case studies. As a result, BO successfully found the ideal circuit board layout as well as particle swarm optimization (PSO) and genetic algorithm (GA) could. The CPU time for BO was 1/5 and 1/4 of that for PSO and GA, respectively. In addition, BO found a non-intuitive optimal solution in approximately 7 minutes from 10 million layout patterns. It was estimated that this was 1/1000 of the CPU time required for analyzing all layout patterns.Comment: 13 pages, 7 figure

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

Optimal Thermal Distribution by using Inverse Genetic Algorithm Optimization Technique

Optimal arrangement of components on printed circuit board (PCB) has become a basic necessity so as to have effective management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve this objective. This paper proposes IGA search engine to optimize the thermal profile of components based on thermal resistance network and to minimize the area of PCB. Comparison between the proposed IGA and the conventional GA (FGA) performances are extensively analyzed. Unlike the conventional FGA, the IGA approach allows the user to set the desired fitness, so that the GA process will try to approach these set values. A reduction in the overall computational time and the freedom of choosing a desired fitness are the major advantages of IGA over FGA. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The computational time has also been minimized by 15.56%

The Threat of Plant Toxins and Bioterrorism: A Review

The intentional use of highly pathogenic microorganisms, such as bacteria, viruses or their toxins, to spread mass-scale diseases that destabilize populations (with motivations of religious or ideological belief, monetary implications, or political decisions) is defined as bioterrorism. Although the success of a bioterrorism attack is not very realistic due to technical constraints, it is not unlikely and the threat of such an attack is higher than ever before. It is now a fact that the capability to create panic has allured terrorists for the use of biological agents (BAs) to cause terror attacks. In the era of biotechnology and nanotechnology, accessibility in terms of price and availability has spread fast, with new sophisticated BAs often being produced and used. Moreover, there are some BAs that are becoming increasingly important, such as toxins produced by bacteria (e.g., Botulinum toxin, BTX), or Enterotoxyn type B, also known as Staphylococcal Enterotoxin B (SEB)) and extractions from plants. The most increasing records are with regards to the extraction / production of ricin, abrin, modeccin, viscumin and volkensin, which are the most lethal plant toxins known to humans, even in low amounts. Moreover, ricin was also developed as an aerosol biological warfare agent (BWA) by the US and its allies during World War II, but was never used. Nowadays, there are increasing records that show how easy it can be to extract plant toxins and transform them into biological weapon agents (BWAs), regardless of the scale of the group of individuals

Improvement of electronic components arrangement on printed circuit board based on thermal distribution profile using comsol multiphysics

The increasing demand for high speed, high performance, robust and smaller packaging and high-power density leads to the need for an optimal electronic system design. These features will increase the thermal distribution of electronics components, which directly affect the lifespan of the electronic product, performance, and the reliability. This project presents the 3D simulation of the optimal thermal profile of component placement on Printed Circuit Board (PCB) using COMSOL Multiphysics software package. The simulation was carried out for various model of component arrangement on (PCB). The objectives are to find an optimum component arrangement with minimal heat dissipation and smaller area of PCB. Nelder-Mead Optimization tools have been used to solve the multi-objective problems. The result shows that with the proper arrangement, the area of PCB able reduces up to 26% while the temperature of components able to reduce up to 40%. Therefore, this research will significantly benefit for the case of thermal performance improvement onto the electronic product and package size

Optimization of multi-holes drilling path using particle swarm optimization

Multi-hole drilling is a manufacturing process that is commonly used in industries. In this process, the tool movement and switching, on average, take 70% of the total machining time. There are many applications of multi-hole drilling, such as in mould, die-making and printed circuit board (PCB). One way to improve the multi-hole drilling is by optimising the tool path in the process. This research aims to model and optimise multi-hole drilling problems using Particle Swarm Optimisation (PSO) algorithm. The study begins by modelling the multi-hole drilling problems using the Travelling Salesman Problem (TSP) concept. The objective function was set to minimise the total tool path distance. Then, the PSO was formulated to minimise total length in multi-hole drilling. The main issue in this stage was to convert the continuous encoding in PSO to permutation problems as in multi-hole drilling. For this purpose, a topological sorting procedure based on the most prominent particle rule was implemented. The algorithm was tested on 15 test problems where between 10 to 150 holes were randomly generated. The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). Then, a validation experiment was conducted by implementing the PSO generated tool path against the commercial CAD-CAM path. In this stage, the machining time was measured. The results from the computational experiment indicated that the proposed PSO algorithm came out with the best solution in 10 out of the 15 test problems. In the meantime, the validation experiment result proved that the PSO generated tool path provides faster machining time compared with the commercial CAD-CAM path by 5% on average. The results clearly showed that PSO has a great potential to be applied in the multi-hole drilling process. The findings from this research could benefit the manufacturing industry to improve their productivity using existing resources

Thermal and area optimization for component placement on PCB design using inverse genetic algorithm

Considering the current trend of compact designs which are mostly multiobjective in nature, proper arrangement of components has become a basic necessity so as to have optimal management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve optimal placement of components on printed circuit board (PCB). The objective functions are the PCB area and temperature of each component while the constraint parameters are; to avoid the overlapping of components, the maximum allowable PCB area is 2(120193.4)mm2 , thermal connections were internally set, and the manufacturer allowable temperature for the ICs must be more than the components optimal temperature. In the conventional Forward Genetic Algorithm (FGA) optimization, the individual fitness of components are generated through the GA process. The IGA approach on the other hand, allows the user to set the desired fitness, so that the GA process will try to approach these set values. Hence, the IGA has two major advantages over FGA; the first being a reduction in the overall computational time and the other is the freedom of choosing the desired fitness (i.e. ability to manipulate the GA output). The objectives of this work includes; development of an IGA search Engine, minimization of the thermal profile of components based on thermal resistance network and the area of PCB, and comparison of the proposed IGA and FGA performances. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The CPU-time has also been minimised by 15.56%

Electronics System Thermal Management Optimization Using Finite Element And Nelder-Mead Method

The demand for high-performance, smaller-sized, and multi-functional electronics component poses a great challenge to the thermal management issues in a printed circuit board (PCB) design. Moreover, this thermal problem can affect the lifespan, performance, and the reliability of the electronic system. This project presents the simulation of an optimal thermal distribution for various samples of electronics components arrangement on PCB. The objectives are to find the optimum components arrangement with minimal heat dissipation and cover small PCB area. Nelder-Mead Optimization (NMO) with Finite Element method has been used to solve these multi-objective problems. The results show that with the proper arrangement of electronics components, the area of PCB has been reduced by 26% while the temperature of components is able to reduce up to 40%. Therefore, this study significantly benefits for the case of thermal management and performance improvement onto the electronic product and system