Remanufacturing is an important method to realize resource saving and circular economy. Disassembly is a vital step of remanufacturing and it is always finished by manual labor which is low efficiency. Although roboti...
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Remanufacturing is an important method to realize resource saving and circular economy. Disassembly is a vital step of remanufacturing and it is always finished by manual labor which is low efficiency. Although robotic disassembly covers the shortages of manual disassembly, it is still difficult for the robots to independently finish the disassembly process without the operator's assistance. The human-robot collaborative disassembly is being paid increasing attention and the human-robot collaborative disassembly line balancing is to assign suitable disassembly tasks to each human-robot collaborative disassembly workstation in a balanced manner. In humanrobot collaborative disassembly, the operator's safety should be considered when the robot collaboratively executes the disassembly tasks with the operator. In this paper, disassembly information model of human-robot collaborative disassembly considering the safe strategy is studied. After that, this paper solves the humanrobot collaborative disassembly line balancing problem using the improved discrete bees algorithm. To verify the proposed method, case study based on a bearing coupler is carried out. The results show the proposed improved discrete bees algorithm can generate the optimal solutions of human-robot collaborative disassembly line balancing problem considering the safe strategy and finds better solutions compared with the other optimization algorithms in terms of running time, average generational distance and Pareto fronts.
This paper presents an analytical approach on the nonlinear vibration of nanocomposite multilayer organic solar cell (NMOSC) subjected to the combination of wind load and uniform temperature change. The NMOSC comprise...
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This paper presents an analytical approach on the nonlinear vibration of nanocomposite multilayer organic solar cell (NMOSC) subjected to the combination of wind load and uniform temperature change. The NMOSC comprises of five layers, which are made of Al, P3HT:PCBM, PEDOT:PSS, Graphene and Glass. Compared to common models of the NMOSC, ITO is replaced by graphene to control the output colors of NMOSC. The nonlinear motion equations and the compatibility equation are deriving by taking into account the effect of viscous damping, von Karman nonlinearity terms, elastic foundations and initial imperfection. The formula of the wind load is improved for consideration in the general case that the direction of the wind load is not just perpendicular to the structure. Four edges of the NMOSC are assumed to be simply supported and immovable in the transverse plane. The Galerkin and Runge - Kutta methods are applied to obtain the dynamic response and the natural frequency of the NMOSC. Four optimization algorithms (bees algorithm, Basic differential evolution algorithm, enhanced colliding bodies optimization algorithm, social group optimization algorithm) are used to determine the maximum value of natural frequency of the NMOCS, which depends on nine variables including the geometrical parameters, elastic foundations stiffness and temperature increment. In the numerical results, the effects of elastic foundations, initial imperfection, viscous damping ratio, temperature increment, wind load and the length to width ratio on the dynamic response and natural frequency are considered in details. The maximum values of the natural frequency of the NMOSC with four algorithms are obtained and compared. The results show that the optimal value obtained by four algorithms is close to each other. However, the calculation time with enhanced colliding bodies optimization algorithm is the fastest and with bees algorithm is the longest of all.
The analytical investigation for the nonlinear thermal dynamic buckling of smart sandwich plate subjected to mechanical, thermal and electric loadings is presented. The sandwich plate is composed of a porous homogeneo...
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The analytical investigation for the nonlinear thermal dynamic buckling of smart sandwich plate subjected to mechanical, thermal and electric loadings is presented. The sandwich plate is composed of a porous homogeneous core, two carbon nanotube reinforced composite (CNTRC) layers and two piezoelectric face sheets. Basic equations are derived based on the Reddy's higher order shear deformation plate theory and Hamilton's principle in which the initial imperfection and Pasternak-type elastic foundations are included. The external pressure is assumed to be uniformly distributed on the surface of the sandwich plate and depend on time according to the linear functions. The nonlinear dynamic response, the frequency - amplitude relation are obtained by using the Galerkin and Runge - Kutta methods and the critical dynamic buckling load is determined by using Budiansky - Roth criterion. bees algorithm is used to determine the maximum value of natural frequency of smart sandwich plate and the corresponding optimum values of geometrical and material parameters. The effects of geometrical parameters, CNT volume fraction, elastic foundations, temperature increment, initial imperfection and porosity coefficient on the nonlinear vibration and dynamic buckling of the smart sandwich plate are considered specifically. The numerical results are also compared with existing results using different theories.
Facing to the globalization and increasing competition of manufacturing enterprise, how to integrate the existent manufacturing services in cloud manufacturing model to form the newly value-added services in order to ...
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ISBN:
(纸本)9783642412783;9783642412776
Facing to the globalization and increasing competition of manufacturing enterprise, how to integrate the existent manufacturing services in cloud manufacturing model to form the newly value-added services in order to fulfill the user requirements has become a significant issue in manufacturing area. In this context, a discrete hybrid bees algorithm (DHBA) is proposed to solve service optimal the selection in resource service aggregation. The problem of service aggregation with QoS global optimal is transformed into a multi-objective services aggregation optimization with QoS constraints, and DHBA is utilized to produce a near-optimal solution. A case study together with a set of simulation experiment is presented and the results demonstrate the effectiveness and feasibility of the proposed method.
The advanced manufacturing and machining techniques are adopting a population-based metaheuristic algorithm for production, predicting and decision-making. Using the same approach, this paper deals with the applicatio...
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The advanced manufacturing and machining techniques are adopting a population-based metaheuristic algorithm for production, predicting and decision-making. Using the same approach, this paper deals with the application of bees algorithm and differential evolution to forecast the optimal parametric values aiming to obtain maximum material removal rate during electrochemical discharge machining of silicon carbide particle/glass fiber-reinforced polymer matrix composite. The bees algorithm follows swarm-based approach, while differential evolution works on a population-based approach. The experimental design was prepared on the basis of Taguchi's methodology using an L-16 orthogonal array. For the experimental analysis, the main variables in the process, that is, electrolyte concentration (g/L), inter-electrode gap (mm), duty factor and voltage (volts), were selected as main input parameters, and material removal rate (mg/min) was adjudged as output quality characteristic. A comparative investigation reveals that the maximum material removal rate was obtained by the parametric value proposed by differential evolution that follows the bees algorithm and Taguchi's methodology. Furthermore, the results prove that the differential evolution algorithm has better collective assessment capability with a rapid converging rate.
The bees algorithm (BA) is a swarm- based metaheuristic algorithm inspired by the foraging behavior of honeybees. This algorithm is very efficient, simple and natural algorithm. In this paper, two natural aspects, nam...
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The present study outlines the application of smartphone and model updating in structural health monitoring by using vibration response data. The application of the approach has been shown for simply supported beams w...
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The present study outlines the application of smartphone and model updating in structural health monitoring by using vibration response data. The application of the approach has been shown for simply supported beams with uncracked and different crack configurations. Crack has been artificially generated at the time of casting using thin steel plate of 1.5 mm thickness. Finite element analyses have been carried out for uncracked and cracked beams. Laboratory tests were conducted using smartphone to obtain the natural frequencies of both damaged and undamaged beams with the help of accelerometer application software. Acceleration in the vertical direction of the cracked and uncracked beams was measured using this mobile application and thereafter, frequency response function curves have been obtained from this. At last, the model has been updated using bees algorithm to get more accurate results.
The brown rat lives with man in a wide variety of environmental contexts and adversely affects public health by transmission of diseases, bites, and allergies. Understanding behavioral and spatial correlation aspects ...
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The brown rat lives with man in a wide variety of environmental contexts and adversely affects public health by transmission of diseases, bites, and allergies. Understanding behavioral and spatial correlation aspects of pest species can contribute to their effective management and control. Rat sightings can be described by spatial coordinates in a particular region of interest defining a spatial point pattern. In this paper, we investigate the spatial structure of rat sightings in the Latina district of Madrid (Spain) and its relation to a number of distance-based covariates that relate to the proliferation of rats. Given a number of locations, biologically considered as attractor points, the spatial dependence is modeled by distance-based covariates and angular orientations through copula functions. We build a particular spatial trivariate distribution using univariate margins coming from the covariate information and provide predictive distributions for such distances and angular orientations.
This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem t...
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This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem that deals with machine assignment, job sequencing, operator allocation, and task sequencing. Setup and unloading must be scheduled to operators, and they are allowed to leave machines while processing jobs. Earlier research on MTSSDRC developed a permutation-based genetic algorithm (PGA) with a specific decoding scheme, namely DSE, to solve the problem. Many previous studies succeed in solving scheduling problems by modifying well-known metaheuristic techniques. Therefore, we are inspired by this to explore further modifications to particular metaheuristics. The first contribution of the present study lies in the development of new decoding schemes that can perform better than the existing option. Five new decoding schemes are considered. Two of those schemes, namely DS2 and DS4, perform significantly better than DSE, reaching 6% relative deviation. DS4 is superior in terms of solution quality, but DS2 can run eight times faster. Another contribution is the development of six modified metaheuristics that are implemented for the MTSSDRC problem: tabu search, simulated annealing, particle swarm optimization, bees algorithm (BA), artificial bee colony, and grey wolf optimization. The performance of these metaheuristics is compared with that of the PGA. The results show that the PGA and BA are consistently superior for medium- and large-sized problems. The BA is more promising in terms of solution quality, but the PGA is faster.
With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors an...
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With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the bees algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.
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