5,300 research outputs found

    Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

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    In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD–TLBO) was developed to optimize the boring process, which promotes surface roughness as the output. At the same time, the speed, feed, and depth of cut are taken as the inputs. The case examines experimental data from the literature on the boring of IS 2062 E250 steel plates. The proposed method draws from a recent idea on the Taguchi-Pareto-Box Behnken design method that argues for a possible relationship between the Taguchi-Pareto method and the Box Behnken design method. This idea was used as a basis for the further argument that teaching learning-based optimization has a role in the further optimization of the established TPBBD method. The optimal solutions were investigated when the objective function was generated using the Box Behnken design in a case. It was replaced with the regression method in the other case, and the python programming codes were used to execute the computations. Then the optimal solutions concerning the parameters of speed, feed rate, depth of cut, and nose radius were evaluated. With the Box Behnken as the objective function for the TLBO method, convergence was reached at 50 iterations with a class population of 5. The optimal parametric solutions are 800 rpm of speed, 0.06 min/min of feed rate, 1 min for depth of cut, and 0 min for nose radius. On the use of the regression method for the objective function, while the TLBO method was deployed, convergence was experienced after 50 iterations with a class population of 200 students. The optimal parametric solution is 1135rpm of speed, 0.06 min/min of feed rate, 1024 min of the depth of cut, and 0.61 min of nose radius. The speed, depth of cut, and nose radius showed higher values, indicating the use of more energy resources to accomplish the optimal goals using the regression method-based objective function. Therefore, the proposed method constitutes a promising route to optimize further the results of the Taguchi-Pareto-Box Behnken design for boring operation improvement

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

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    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%

    Metaheuristic design of feedforward neural networks: a review of two decades of research

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    Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

    Topological optimization of structures produced through 3D printing of fiber reinforced cementitious materials

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    Dissertação de mestrado integrado em Engenharia CivilTopology optimization can play an important role in the Architecture, Engineering and Construction (AEC) sector. This technology along with digital manufacturing can be a game changer in the future of civil construction, allowing to build, in a short time period, lighter constructions with very geometry complexity but keeping the same of even better structural functioning. These optimized structures when coupled with a material with high capacity efforts redistribution, e.g. fibre reinforced cementitious material (FRC), can partially or totally substitute the conventional reinforcement, consequently less raw material is use, contributing for a better sustainable development. Following this idea, this dissertation will focus on study topology optimization processes along with the use of FRC materials. Initially a comparison between some topology optimization software’s will be carried out, in order to proper evaluate to most suitable for the realization of the present work. In a second stage, considering only the linear behavior of the material, different topology optimization analyses will be done. These analyses will be based on the geometry and the intended structural application (support and load conditions), in addition to the optimization goal (design variable and constraint). This part aims to assess the influence of height / length ratio (H/L ratio) of the beam, in the optimization outcome. After that, a study of the influence of reinforcement amount in the optimization will be done. Afterwards, some finite element analysis (FEA) for one of the optimized structures will be performed and assessed using distinct approaches for obtaining the tensile stress – strain relationship, namely by adopting the ultimate limit state (USL) and service limit state (SLS) tensile diagrams according to the recommendations presented in FIB Model Code 2010. These simulations will serve to evaluate the nonlinear behavior of the FRC structure. For this study six FRC with different strength classes were considered. Finally, an optimized structural element obtained through the FEA was sliced for 3D printing and the influence of the nozzle dimensions, i.e. printing resolution was checked.A otimização da topologia pode desempenhar um papel importante no setor de Arquitetura, Engenharia e Construção (AEC). Esta tecnologia aliada à manufatura digital pode completamente revolucionar o futuro da construção civil, permitindo construir, num curto espaço de tempo, construções mais leves, mas mantendo o mesmo ou ainda melhor funcionamento estrutural. Estas estruturas otimizadas quando conjugadas a um material com alta capacidade de redistribuição de esforços, por ex. materiais cimentícios reforçado com fibras (FRC), pode substituir parcial ou totalmente o reforço convencional, onde consequentemente menos matéria-prima será utilizada, contribuindo-se assim, para um melhor desenvolvimento sustentável. Seguindo essa ideia, esta dissertação terá como foco estudar processos de otimização de topológica juntamente com o uso de materiais FRC. Inicialmente será realizada uma comparação entre alguns softwares de otimização de topológica, a fim de avaliar adequadamente o mais adequado para a realização do presente trabalho. Em uma segunda etapa, considerando apenas o comportamento linear do material, serão realizados diferentes processos de otimização topológica. Essas otimizações serão baseadas na geometria e na aplicação estrutural pretendida e no objetivo da otimização. Esta parte visa avaliar a influencia da relação altura/comprimento da viga (relação H/L), no resultado da otimização. Posteriormente, algumas análises de elementos finitos (FEM) para uma das estruturas otimizadas serão realizadas e avaliadas usando duas abordagens distintas para a obtenção da relação tensão de tração – deformação, uma para estado limite último (ELU) e estado limite de serviço (ELS), seguindo as recomendações presentes no FIB Model Code 2010. Estas simulações servirão para avaliar o comportamento não linear da estrutura de FRC. Para este estudo foram considerados seis FRC com diferentes classes de força. Finalmente, para um elemento estrutural otimizado anteriormente, foi realizada uma simulação de impressão 3D, de modo a estudar a influencia do tamanho do bico de impressão, ou seja, a resolução de impressão foi verificada

    Scalable Co-Optimization of Morphology and Control in Embodied Machines

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    Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training -- while simultaneously providing a testbed to investigate the theory of embodied cognition

    Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes

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    This paper presents a new systematic approach to the optimization of both design and manufacturing variables across a multi-step production process. The approach assumes a generic manufacturing process in which an initial Near Net Shape (NNS) process is followed by a limited number of finishing operations. In this context the optimisation problem becomes a multi-variable problem in which the aim is to optimize by minimizing cost (or time) and improving technological performances (e.g. turning force). To enable such computation a methodology, named Conditional Design Optimization (CoDeO) is proposed which allows the modelling and simultaneous optimization of process parameters and product design (geometric variables), using single or multi-criteria optimization strategies. After investigation of CoDeO’s requirements, evolutionary algorithms, in particular Genetic Algorithms, are identified as the most suitable for overall NNS manufacturing chain optimization The CoDeO methodology is tested using an industrial case study that details a process chain composed of casting and machining processes. For the specific case study presented the optimized process resulted in cost savings of 22% (corresponding to equivalent machining time savings) and a 10% component weight reduction

    Particle swarm optimization algorithms with selective differential evolution for AUV path planning

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    Particle swarm optimization (PSO)-based algorithms are suitable for path planning of the Autonomous Underwater Vehicle (AUV) due to their high computational efficiency. However, such algorithms may produce sub-optimal paths or require higher computational load to produce an optimal path. This paper proposed a new approach that improves the ability of PSO-based algorithms to search for the optimal path while maintaining a low computational requirement. By hybridizing with differential evolution (DE), the proposed algorithms carry out the DE operator selectively to improve the search ability. The algorithms were applied in an offline AUV path planner to generate a near-optimal path that safely guides the AUV through an environment with a priori known obstacles and time-invariant non-uniform currents. The algorithm performances were benchmarked against other algorithms in an offline path planner because if the proposed algorithms can provide better computational efficiency to demonstrate the minimum capability of a path planner, then they will outperform the tested algorithms in a realistic scenario. Through Monte Carlo simulations and Kruskal-Wallis test, SDEAPSO (selective DE-hybridized PSO with adaptive factor) and SDEQPSO (selective DE-hybridized Quantum-behaved PSO) were found to be capable of generating feasible AUV path with higher efficiency than other algorithms tested, as indicated by their lower computational requirement and excellent path quality
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