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To obtain a large viewing angle and fast, smooth tracking of the target for a robot in an unknown environment, the vertebrate extraocular muscles are imitated in this paper, the configuration with a large field of view and high motion precision is studied, and then, a smallsized "inverted-tricept" parallel bionic eye mechanism is designed. For the "inverted-tricept" mechanism, the large inclination will make the mechanism more "slender", whereas the high precision requires the mechanism to be "stout". Thus, there is a contradiction between these two indexes. To ensure a large field of view and high eye movement accuracy simultaneously, the non-dominated sorting-based genetic algorithm-II (NSGA-II) is adopted under the constraints of the overall dimensions, driver stroke and hinge deflection to optimize the moving platform inclination and kinematic performance. Thus, the structural parameters are obtained. According to the optimization results, the dynamic simulation analysis of the system is conducted, and the rationality of the structural parameters is verified. In addition, the experimental prototype is built accordingly. It is shown from the actual measurement results that the designed parallel bionic eye has a distortion-free field of view of up to 200.95° and an eye movement accuracy of 0.01°, which lay a foundation for the wide range search and high-precision ranging of the subsequent binocular stereo vision system. [ABSTRACT FROM AUTHOR]

We aimed to create a facility layout design of semi-submersible drilling platform (DSDP) with performance constraints. The Boltzmann survival mechanism of simulated annealing algorithm was introduced into the replacement strategy of genetic algorithm to form an improved genetic algorithm called genetic and simulated annealing algorithm (GASA). The new algorithm alleviates the "combination explosion" and premature convergence of the traditional genetic algorithm. The layout problem of DSDP is efficiently solved by using this new algorithm. When the number of layout objects increases, GASA's performance is better than that of the genetic algorithm. [ABSTRACT FROM AUTHOR]

In order to select the effective features or feature subsets and realize an intelligent diagnosis of aero engine rolling bearing faults, this paper presents a sharing pattern feature selection method using multiple improved genetic algorithms. Based on the simple genetic algorithm, a multiple-population improved genetic algorithm was proposed, which improves the speed and effect of algorithm and overcomes the shortcomings of local optima that simple genetic algorithm is easy to fall into. Because all populations regularly share and exchange their selecting features, the proposed algorithms can quickly dig up the current effective feature patterns, and then analyze and deal with the strong correlation between the feature patterns. This will not only give clear directions for the descendant evolution, but also help to achieve high accuracy feature selection, for, the features are highly distinctive. This multiple-population improved genetic algorithm was applied to rolling bearing fault feature selection and comparisons with other methods are carried out, which demonstrates the validity of sharing pattern feature selection method proposed. [ABSTRACT FROM AUTHOR]

Electrical energy, which in the machining zone is transformed into heat, is of key importance in electrical discharge machining (EDM). Machining performance of EDM is determined by the characteristic of discharge energy. Therefore, an experimental-analytical approach of discharge energy efficiency was analyzed. The main input parameters for controlling the discharge energy are discharge current and discharge duration. The EDM process is monitored considering the two output machining performance, i.e., material removal rate and surface roughness, which are important for increasing productivity and quality. We modeled the energy efficiency of electrical discharge machining by the use of genetic algorithm. With this action an attempt was made to find even more precise dependence of discharge energy parameters with machining performance. Finally, this was followed by optimization of the discharge energy efficiency in EDM process using multi-objective approach. Evolutionary two-objective optimization is leading to the set of optimal solutions for the discharge energy considering the two machining parameters. Using this set of solutions, EDM discharge energy parameters can be selected to achieve high material removal rate with good surface roughness. [ABSTRACT FROM AUTHOR]

Selection of cutting parameters in micro-milling operations is essential for improving machining efficiency and quality, and prolonging the micro-milling tool life. The increase of material removal rate (MRR) always means the increase of cutting parameters, which may lead to poor surface quality and micro-milling tool failure, even cutter breakage. An optimization approach based on genetic algorithm is used to achieve the maximum MRR under the constraints of surface roughness and cutter breakage. A theoretical model for predicting micro-milling cutter breakage is presented and micro-milling experiments were conducted to establish statistical models of cutter breakage and surface roughness. The optimized results were achieved under the constraints of the specified surface roughness and compared under the different surface roughness limitation. We find that the optimized results improve the machining efficiency and quality in micro- milling and is affected by constraint conditions complicatedly. [ABSTRACT FROM AUTHOR]

In this study, we optimized thick airfoils for wind turbines using a genetic algorithm (GA) coupled with computational fluid dynamics (CFD) and geometric parameterization based on the Akima curve fitting method. Complex and separated flow fields around the airfoils of each design generation were obtained by performing Reynolds-averaged Navier-Stokes steady flow simulation based on the in-house code of an implicit high-resolution upwind relaxation scheme for finite volume formulation. Airfoils with 40 % and 35 % thickness values were selected as baseline airfoils. An airfoil becomes thicker toward the blade root area, thereby increasing blade stiffness and lowering its aerodynamic efficiency. We optimized the airfoils to simultaneously maximize aerodynamic efficiency and blade thickness. The design variables and objective function correspond to the airfoil coordinates and the lift-to-drag ratio at a high angle of attack with airfoil thickness constraints. We improved the lift-to-drag ratio by 30 %~40 % compared with the baseline airfoils by performing optimization using GA and CFD. The improved airfoils are expected to achieve a 5 %~11 % higher torque coefficient while minimizing the thrust coefficient near the blade root area. [ABSTRACT FROM AUTHOR]

Metal cladding is a process of depositing a filler material to enhance the surface properties of base material using a suitable welding process. In this work the clad specimens are produced by surfacing a layer of filler material using weld cladding process to minimize the heat loss across the walls of the pressure vessels. It is done by depositing a low thermal conductivity austenitic stainless steel grade of 316L on structural steel plates used for boiler construction using flux cored arc welding process. The experimental study is carried out as per design of experiments availed for five factors five levels central composite design using response surface methodology. The mathematical models are developed for the prediction of clad layer height, clad layer width and depth of penetration. These models are employed in formulating fitness functions for multi-objective optimization of clad layer dimensions using genetic algorithm (GA). The set of optimal solutions suggested by response surface optimizer and genetic algorithm are compared and discussed. Conformity tests are conducted to validate the prediction capability of developed models and optimum settings. Optimum clad layer dimensions have been arrived and optimized stainless steel clad specimen has been produced. The heat transfer analysis is planned to be conducted in the next phase. The findings can be used in energy efficient design of pressure vessels. [ABSTRACT FROM AUTHOR]

To predict potential problems and avoid an unexpected breakdown of rotating machinery, a rule-based classifier ensemble approach is presented. Feature reduction was first implemented on a fault decision table using discernibility matrices and the genetic algorithm. The generated rules of the reducts were used to build the candidate base classifiers. Then, several base classifiers were selected according to their diversity and scale. The weights of the selected base classifiers were also calculated based on the support rate measurements. A classifier ensemble was constructed through an integration of the base classifiers using an improved weighted voting technique. Finally, the proposed classifier ensemble was verified based on the vibration data of bearing types SKF6203 and NU205. The accuracy for the SKF6203 bearing type reached 88.75 %, which is at least 5 % higher than that of the three base classifiers for this type of bearing. In addition, the recognition rate for the latter bearing type was 90 %. The reasoning process was much easier to comprehend owing to the semantic descriptions of the rules. The results show that this is a promising and transparent approach for diagnosing typical faults of rotating machinery. [ABSTRACT FROM AUTHOR]

The high quality of welding in the automotive industry is achieved by proper positioning of the fixture elements. A new method, N-3-2-1 (N ≥ 1), is proposed for fixture layout optimization of sheet metals. The flexible nature of the sheet metals requires N+3 fixture elements to constrain it normal to the surface (primary plane), but 2-1 fixture elements for other two directions (secondary and tertiary). The objective function is to achieve high stiffness of the workpiece and is calculated in terms of strain energy. Finite element analysis (FEA) was combined with genetic algorithm. A method was also proposed to find the optimum fixturing position of the workpiece in multipoint respot welding operation. Two different kinds of case studies were solved and the performance of the proposed method was also tested in the industrial scenario by fixturing the workpiece and completing the respot welding operation with satisfactory results. [ABSTRACT FROM AUTHOR]

This paper presents an efficient bi-level optimization technique to obtain the optimal stacking sequence for symmetric composite structures. The proposed approach involves two levels of modeling and optimization. The first level of the optimization procedure is used to minimize the weight of the composite structure. At this level, lamination parameters and the number of plies of specified angles (0, ±45 and 90 degree) are design variables, buckling load factor is treated as a constraint, and the weight of the structure is to be minimized using continuous-discrete particle swarm optimization algorithm. Next, at the second level the location of each ply orientation through the thickness (i.e. the layup of the panel) is found. At the second level, optimum stacking sequence is sought to maximize the load bearing capacity of the structure with respect to the buckling. The proposed methodology is applied to two test cases. Results show that the approach improves the buckling load factor of the structure without any weight penalty. [ABSTRACT FROM AUTHOR]

A numerical study on the heat exchanger design process has been conducted in order to propose a more effective method for the preliminary design of highly efficient and compact heat exchanger. The ε-NTU based performance prediction program and the heat exchanger database having performance correlations of various fin-type heat exchanger were developed. Numerical characteristics of the genetic, pattern search, and simulated annealing algorithms for the heat exchanger sizing were compared in terms of the accuracy and the computational speed. The effect of margins in design requirements were examined through the size ranking and the response surface analysis. The usefulness of the entropy generation minimization was appraised by comparing to the case when the objective function was the volume of the heat exchanger. [ABSTRACT FROM AUTHOR]

The HRSG extensively affects all performance of CCPPs. The inlet duct geometry of an HRSG is the most essential part for determining heat exchange in the main body, in terms of flow uniformity. In the present study, numerical analysis of the HRSG flow characteristics and design optimization of inlet duct geometry for improving flow uniformity at the front section of the main body were performed to meet the trend requirements. A new inlet duct geometry, which has maximum flow uniformity, was proposed through design optimization procedures using a genetic algorithm. Specifically, the actual operating condition of the D-top model HRSG was applied and the pressure recovery coefficient and diffuser efficiency were considered. In the optimized design, a recirculation area was formed at the top internal wall of the second expansion stage. Results indicate that the forming of the recirculation area improves flow uniformity by rotating movement and spreading the high-speed flow. [ABSTRACT FROM AUTHOR]

An adaptive approach is developed for the tracking control of a pendulum-driven spherical robot. First, the dynamics model of the robot is derived using Newton-Euler methodology for a system of rigid bodies. The simplified model is marginally stable and nonminimum phase. Next, based on the theory of the model-reference adaptive system, a Model-reference adaptive controller (MRAC) is designed to track the desired command signals. The controller parameters are adjusted based on the error between the reference model and the process outputs and the command signals. Simulations illustrate that the convergence rate depends substantially on the adaptation gains of the MRAC. Hence, gains should be tuned properly by using fuzzy logic systems or a genetic algorithm. Results indicate that the proposed control strategy is highly promising for tracking different command signals even if the system is non-minimum phase and marginally stable. Moreover, this controller is more easily implemented in the real world compared with nonlinear controllers. [ABSTRACT FROM AUTHOR]

In this paper, a Dielectrophoretic (DEP) particle separation device is optimized with the purpose of maximizing a separation throughput. The DEP separation device is composed of a flow channel with electrodes where particles suspended in a flowing medium are separated using the DEP phenomenon; namely, the force exerted on a polarized particle under a non-uniform electric field. Here, the electrode configuration of the device is optimized to overcome low separation throughput that is a main weak point of the DEP device. More specifically, the length and location of positive and negative electrode pieces are optimized for two objectives: The maximization of medium outlet speed and minimization of applied voltage strength. The higher medium speed raises separation throughput, and the lower voltage strength alleviates particle damage. Particle trajectory is calculated by applying a particle dynamics simulation incorporating electric field and fluid analyses. The calculated outlet location of a particle is confined inside the desired region to ensure the success of particle separation. The formulated constrained multi-objective optimization problem is solved using the Non-dominated sorting genetic algorithm II (NSGA-II). The irregularly distributed electrode arrays are obtained as design results, and performances of benchmark and optimized designs are compared. The comparison result shows that 294 % higher throughput can be achieved by optimized irregular electrode array distribution. [ABSTRACT FROM AUTHOR]

This paper is concerned with the multi-objective optimization of the structure of TBR (Truck and bus radial) tire by making use of Genetic algorithm (GA) and Artificial neural network (ANN) in order to effectively enhance the tire durability. Four different types of continuous and discrete design variables are chosen by the carcass path, width and angle of tread belts and the rubber modulus of sidewall and base strip, while the objective functions are defined by the peak strain energy at the belt edge and the peak shear strain of carcass. The approximate models of two objective functions are approximated by neural network, and mathematical sensitivity analysis is substituted with the iterative genetic evolution to deal with the discontinuous discrete-type design variables. The weights of two objective functions are traded-off by adjusting the aspiration levels with respect to the ideal levels. The validity of proposed multi-objective optimization method is illustrated through the numerical experiment. [ABSTRACT FROM AUTHOR]

It is essential to prepare reliable material and radiative surface properties in predicting the accurate infrared signature of objects on a scene. Especially it is difficult to procure the radiative reflection property expressed by the Bidirectional reflectance distribution function (BRDF) due to the limited information in the literature. Recently, studies on inverse estimation of the radiative properties based on experimental data are conducted to obtain radiative reflective properties required for the infrared image generation. In this study we compare the two different inversely estimated radiative reflective properties obtained respectively from the daily measured radiance data with a fixed angle of measurement and the measured directional radiance data at a fixed time in a lab environment. The radiative properties in Mid-wavelength infrared (MWIR) region of the painted steel are estimated by finding the best fitted model parameters for the Sandford-Robertson BRDF model using the Repulsive particle swarm optimization (RPSO) algorithm by using those two different radiance data. The estimated radiative reflective properties obtained from the two different radiance data show fairly good agreements with the reference data but the measured directional radiance data at a fixed time show better results. The directional radiance data measured at a fixed time in a lab environment could be a better way of obtaining the radiative surface properties of newly developed paints when an appropriate measuring device for the directional radiance is in hand. [ABSTRACT FROM AUTHOR]

The parameter space approach based robust PI control design methodology for DC motor speed control is proposed in this paper. The multi-objective design requirements like D-stability, phase margin and mixed sensitivity (frequency domain) bounds are mapped into the controller parameter space to determine PI controller coefficients which satisfies the desired user-defined specifications. Besides robust PI controller, an add-on disturbance observer is utilized to enhance the tracking performance and disturbance rejection of the control system. The proposed control scheme is validated by simulations and experiments. The results prove that the effectiveness of the proposed control system against uncertainties in the modeling and disturbances on the system response. [ABSTRACT FROM AUTHOR]

Adaptive Resistance spot welding (RSW) technologies have been developed and applied within the automotive industry to improve the weldability of automotive steels, including conventional and high strength steels. Almost all adaptive RSW systems require an inverter DC RSW system as a platform because such a system can generate various types of current waveforms effectively with higher control frequency than an AC RSW system. In this work, a fuzzy controller for an inverter DC RSW system that can control constant current and more complex current waveforms was developed as a platform for adaptive control of RSW. This study used System identification (SI) to model the inverter DC welding system effectively. To optimize the fuzzy controller for the SI model, fuzzy scaling factors were optimized using a genetic algorithm. To apply the scaling factor of the fuzzy controller with the SI model to a real system, the scaling factors were adjusted using response surface methodology. [ABSTRACT FROM AUTHOR]

High-speed electromagnetic actuator (HEA) is the key component of Electronic fuel injection system (EFIS) of diesel engine, its dynamic performances have a significant impact on the control accuracy and flexible control law of fuel injection. In order to obtain the higher dynamic response speed, lower power consumption, higher safety and reliability, a novel HEA with permanent magnet (NPMHEA) was proposed based on the principle of parallel magnetic circuit in this paper. Then the simulation models of HEA coupling of magnetic field, electric circuit and mechanical motion were developed by the method of time-stepping finite element, and their precisions were verified by experiment. By the comparative analysis, influence rules of key parameters on the dynamic performances of HEA have been established. In addition, the multi-objective optimization of NPMHEA based on response surface methodology and Genetic algorithm (GA) was carried out and the Pareto optimal solution set was obtained. It is concluded that there are contradictions between objectives for the Pareto optimal solutions; considering the whole performances, the optimal solution is determined for which the peak current, the peak pulse and the hold current reduce by 20.5 %, 7.8 % and 43.9 %, respectively, and the actuation response speed quickens by 11.9 %, significantly reducing the power consumption and coil loss and improving the dynamic response speed of HEA. It decisively provides certain theoretical guidance for the design and optimization of HEA. [ABSTRACT FROM AUTHOR]

A steering mechanism is one of the main components of a vehicle that is responsible of controlling the directional wheels. The optimal design of a steering mechanism is not a trivial problem because the equations involved in the modeling are highly nonlinear. Therefore, this paper proposed a method for global optimization using a genetic algorithm for a multi-link steering mechanism, which can also be applied to other problems of optimal synthesis of mechanisms. [ABSTRACT FROM AUTHOR]

The Electric power steering (EPS) system, a typical non-linear system, is easy to be influenced by parameters perturbation and disturbance of the road. Traditional linear control method based on a simplified linear model such as PID control cannot reach good dynamic performance. To reduce the influence of parameters perturbation and disturbance of the road and enhance the robustness of the system, an Adaptive fuzzy sliding mode control (AFSMC) method is proposed in this paper. First, fuzzy sliding mode control is employed to enhance the dynamic performance of the system. Then, to improve the precision of the controller, genetic algorithm is used to optimize the control rules which are essential to fuzzy control. The experimental results on non-linear EPS model demonstrate that AFSMC is more stable than Sliding mode control (SMC) method and more efficient to the non-linear system than SFPID control method. They can also prove that AFSMC can provide a stable driving in the presence of parameters perturbation and disturbance of the road. [ABSTRACT FROM AUTHOR]

Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application. [ABSTRACT FROM AUTHOR]

Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named 'Quasi-oppositional based Jaya algorithm' (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO). [ABSTRACT FROM AUTHOR]

Friction welding has vital industrial role in fabricating automobiles, aerospace, ship building, heat exchangers using similar, dissimilar and bi-metal of ferrous and non-ferrous metals at mass production level. In this study, admiralty brass C44300 tube and aluminium alloy AA7075 -T651, 6 mm thick tube plate were identified as base metals. Different joint surface area profile of with and without thread of different pitch values was chosen to study the mechanical properties and micro structures of these two base metals. 0.1 mm clearance was maintained between the AA7075-T651 tube plate and C44300 tube outer diameter to make friction welding. Taguchi's L16 orthogonal array techniques were adopted for identifying the most significant ranking process parameters. Analysis of variance (ANOVA) has been used to analyze the input parameter contribution in terms of percentage. Genetic algorithm (GA) was used to access the suitable input parameter value to obtain effective joint strength in terms of hardness, compressive strength and microstructure formation in the interface of the joint. A Compression test (CT) was conducted to evaluate the level of compressive strength of the joint. Threaded profile pair with higher pitch value proved high compressive strength over unthreaded pair. Micro structure for base metal C44300 tube and AA7075-T651 tube plate, Heat affected zone (HAZ) and Weld zone (WZ) of the joint has been studied. Hardness of base metals, HAZ and WZ was measured by micro Vickers hardness tester and the observation shows that hardness at joint interface has been found to be higher in all pairs. [ABSTRACT FROM AUTHOR]

Structural optimization of a typical locomotive carbody subjected to various operational loads is investigated using a simultaneous topology and size optimization approach. The proposed approach builds on a string representation of the structure in which the presence or absence of a structural element between specific nodes, as well as its cross-section, are represented by integers. Strings representing various structure topologies are then evolved according to a modified Genetic algorithm with improved genetic operators. The efficiency of the proposed model is verified through applying it to optimize the carbody structure of ER24PC locomotive. Operational load cases and performance criteria are adopted from the European standard EN12663. It is shown that the results based on the present optimal design have higher safety factors compared to the original design without a significant increase in weight. Besides, the computational cost of the optimization process is shown to be considerably less than that of the classical binary Genetic algorithm. [ABSTRACT FROM AUTHOR]

Our aim was to improve the performance of tracked ambulance nonlinear vibration reduction system by structure optimization design. The structure optimization focuses on the stretcher base. Two structure optimization schemes are proposed based on the mechanism of Dynamic vibration absorber (DVA): Linear and nonlinear structure. The linear structure optimization scheme is finally adopted by comparison of the two schemes under random vibration, and the performance of linear scheme is also verified under shock vibration. Then the global sensitivity analysis method is applied to calculate the parameter sensitivity of nonlinear vibration reduction system. Finally, the Nondominated sorting genetic algorithm II (NSGA-II) is used to optimize the performance of the nonlinear vibration reduction system. It shows that the vibration energy of the supine human body on a stretcher is reduced after above studies, which proves the validity and feasibility of the structure optimization schemes proposed and the optimization studies applied in this paper. [ABSTRACT FROM AUTHOR]

In this study, estimation of some physical properties of a laminated composite plate was conducted via the inverse vibration problem. Laminated composite plate was modelled and simulated to obtain vibration responses for different length-to-thickness ratio in ANSYS. Furthermore, a numerical finite element model was developed for the laminated composite utilizing the Kirchhoff plate theory and programmed in MATLAB for simulations. Optimizing the difference between these two vibration responses, inverse vibration problem was solved to obtain some of the physical properties of the laminated composite using genetic algorithms. The estimated parameters are compared with the theoretical results, and a very good correspondence was observed. [ABSTRACT FROM AUTHOR]

A wide operating band is important for a pump to safely perform at maximum efficiency while saving energy. To widen the operating range, a multi-point optimization process based on numerical simulations in order to improve impeller performance of a centrifugal pump used in nuclear plant applications is proposed by this research. The Reynolds average Navier Stokes equations are utilized to perform the calculations. The meridional shape of the impeller was optimized based on the following four parameters; shroud arc radius, hub arc radius, shroud angle, and hub angle as the design variables. Efficiencies calculated under 0.6 Q , 1.0 Q and 1.62 Q were selected as the three optimized objectives. The Design of experiment method was applied to generate various impellers while 35 impellers were generated by the Latin hypercube sampling method. A Response surface function based on a second order function was applied to construct a mathematical relationship between the objectives and design variables. A multi-objective genetic algorithm was utilized to solve the response surface function to obtain the best optimized objectives as well as the best combination of design parameters. The results indicated that the pump performance predicted by numerical simulation was in agreement with the experimental performance. The optimized efficiencies based on the three operating conditions were increased by 3.9 %, 6.1 % and 2.6 %, respectively. In addition, the velocity distribution, pressure distribution, streamline and turbulence kinetic energy distribution of the optimized and reference impeller were compared and analyzed to illustrate the performance improvement. [ABSTRACT FROM AUTHOR]

This study presents the construction process of a novel spherical rolling robot and control strategies that are used to improve robot locomotion. The proposed robot drive mechanism is constructed based on a combination of the pendulum and wheel drive mechanisms. The control model of the proposed robot is developed, and the state space model is calculated based on the obtained control model. Two control strategies are defined to improve the synchronization performance of the proposed robot motors. The proportional-derivative and proportional-integral-derivative controllers are designed based on the pole placement method. The proportional-integral-derivative controller leads to a better step response than the proportional-derivative controller. The controller parameters are tuned with genetic and differential evaluation algorithms. The proportional-integral-derivative controller which is tuned based on the differential evaluation algorithm leads to a better step response than the proportional-integral-derivative controller that is tuned based on genetic algorithm. Fuzzy logics are used to reduce the robot drive mechanism motors synchronizing process time to the end of achieving a high-performance controller. The experimental implementation results of fuzzy-proportional-integral-derivative on the proposed spherical rolling robot resulted in a desirable synchronizing performance in a short time. [ABSTRACT FROM AUTHOR]

In the traditional Stress-strength interference (SSI) model, stress and strength must satisfy the basic assumption of mutual independence. However, a complex dependence between stress and strength exists in practical engineering. To evaluate structural reliability under the case that stress and strength are dependent, a mixed copula function is introduced to a new dependent SSI model. This model can fully characterize the dependence between stress and strength. The residual square sum method and genetic algorithm are also used to estimate the unknown parameters of the model. Finally, the validity of the proposed model is demonstrated via a practical case. Results show that traditional SSI model ignoring the dependence between stress and strength more easily overestimates product reliability than the new dependent SSI model. [ABSTRACT FROM AUTHOR]

The success of an implant depends upon surface characteristics like roughness, topography, chemistry and hardness. The fabrication of a hard surface in combination with micron-, submicron- and nano-scale surface roughness is a great challenge for biomanufacturing industries. The surface microhardness (MH) needs to be maximized while controlling the Surface roughness (SR). The present research is the first study in which the application of Non-dominated sorting genetic algorithm (NSGA)-II coupled with Taguchi based Response surface methodology (RSM) is used to predict the optimal conditions of Powder mixed electric discharge machining (PMEDM) parameters to fabricate the biocompatible surface on β-phase Ti alloy. Batch vial tests were first carried out in accordance with the L orthogonal array. ANOVA analysis gave the significant influencing factors and then mathematical models were developed between input parameters and output responses like SR and MH using Taguchi based RSM technique. These models were then optimized using NSGA-II to obtain a set of Pareto-optimal solutions. From the series of multiple solutions, the best optimal condition to achieve required low SR and high MH was determined, which are 13 A peak current, 5 μs pulse duration, 8% duty cycle (longer pulse-interval) and 8 g/l silicon powder concentration for achieving a required low SR and high MH. The MH considerably increased about 184% compared to the base material, and about 1.02 μm SR can be achieved in combination with micron-, submicron- and nano-scale surface features. [ABSTRACT FROM AUTHOR]

This paper presents an optimal kinetostatic design method for a general 3PRS (Prismatic-revolute-spherical) spatial parallel manipulator by formulating a multi-objective optimization problem considering the performance indices are as the objective functions. Three performance criteria-Global conditioning index (GCI), Global stiffness index (GSI) and workspace volume-were formulated and the effect of actuator layout angle on the performance indices was studied. A multi-objective evolutionary algorithm based on the Control elitist non-dominated sorting genetic algorithm (CENSGA) was adopted to find the final approximation set. The optimal geometric parameters that yield minimal compliance with larger workspace volume and improved dexterity are suggested for a general 3PRS parallel manipulator. For the optimal design, it is shown that global isotropy and global stiffness of the platform is improved at the cost of workspace reduction. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show the design and optimization of a novel electromagnetic servo brake incorporating an Antilock brake system (ABS) function by Multi-objective genetic algorithms. To consider different design requirements, three types of Axisymmetric Finite element (FE) models were initially formulated parametrically to determine the braking force and position of the pusher at each instant during operation of the proposed device. Using a combination of the FE models and Weight-based multi-objective genetic algorithms (WBMOGA), the optimal geometry and dimensions of the proposed FE models were determined while maximizing the braking force of the device and minimizing both the current supplied by the battery and the weight of the assembly. Once an optimal configuration for each type of servo brake designed had been achieved, three prototypes were built and validated experimentally on a conventional test bench. Finally, the prototype that performed best of the three prototypes was mounted and tested on a hybrid test bench with a realistic ABS device. The good agreement between the results obtained from the simulations and those measured experimentally, suggests that the combination of FE models and WBMOGA may be used successfully to design and optimize any complex electromechanical device. [ABSTRACT FROM AUTHOR]

The prediction and optimization of weld quality characteristics in small scale resistance spot welding of TC2 titanium alloy were investigated. Grey relational analysis, neural network and genetic algorithm were applied separately. Quality characteristics were selected as nugget diameter, failure load, failure displacement and failure energy. Welding parameters to be optimized were set as electrode force, welding current and welding time. Grey relational analysis was conducted for a rough estimation of the optimum welding parameters. Results showed that welding current played a key role in weld quality improvement. Different back propagation neural network architectures were then arranged to predict multiple quality characteristics. Interaction effects of welding parameters were analyzed with the proposed neural network. Failure load was found more sensitive to the change of welding parameters than nugget diameter. Optimum welding parameters were determined by genetic algorithm. The predicted responses showed good agreement with confirmation experiments. [ABSTRACT FROM AUTHOR]

Metallic foams are a class of porous materials widely used in the industry because of their advantages. In recent years, extensive studies on the behavior of these materials have been conducted. Several constitutive equations have also been presented and applied. This study proposes a new constitutive equation that predicts metallic foam behavior using the stress-strain curve in uniaxial compression. The proposed model offers a new functionality for work hardening and is evaluated for both isotropic and combined hardening. The constitutive equations are implemented in MATLAB and integrated using return mapping algorithm. The material parameters are identified using genetic algorithm and through a comparison of the experimental and numerical results. The aluminum foams discussed in this paper are the commercially available types, Foaminal and Alporas. The comparison of numerical and experimental results indicate that this new constitutive equation predicts foam behavior in a reasonable manner. Moreover, a good agreement is observed between the experimental and computational curves. [ABSTRACT FROM AUTHOR]

The present study investigates the use of HVAC and solar heating systems inside residential zones using a combination of direct numerical modeling and genetic optimization method in different climates of Iran. The direct numerical modeling is capable of calculating temperature field inside buildings. Then, direct modelling algorithm is used to determine energy consumption rate of the building in the different climates of Iran. The results of direct numerical modelling are checked against those of carrier and energy-plus software. The results show good accordance with those of numerical simulations presented in the open literature. [ABSTRACT FROM AUTHOR]

An optimization process for impellers was carried out based on numerical simulation, Latin hypercube sampling (LHS), surrogate model and Genetic algorithm (GA) to improve the efficiency of residual heat removal pump. The commercial software ANSYS CFX 14.5 was utilized to solve the Reynolds-averaged Navier-Stokes equations by using the Shear stress transport turbulence model. The impeller blade parameters, which contain the blade inlet incidence angle Δ β, blade wrap angle φ, and blade outlet angle β, were designed by random sample points according to the LHS method. The efficiency predicted under the design flow rate was selected as the objective function. The best combination of parameters was obtained by calculating the surrogate model with the GA. Meanwhile, the prediction accuracies of three surrogate models, namely, Response surface model (RSM), Kriging model, and Radial basis neural network (RBNN), were compared. Results showed that the calculated findings agree with the experimental performance results of the original pump. The RSF model predicted the highest efficiency, while the RBNN had the highest prediction accuracy. Compared with the simulated efficiency of the original pump, the optimization increased efficiency by 8.34% under the design point. Finally, the internal flow fields were analyzed to understand the mechanism of efficiency improvement. The optimization process, including the comparison of the surrogate models, can provide reference for the optimization design of other pumps. [ABSTRACT FROM AUTHOR]

Abundant improvements have occurred in materials handling, especially in metal joining. Pulsed current gas tungsten arc welding (PCGTAW) is one of the consequential fusion techniques. In this work, PCGTAW of AISI 4135 steel engendered through powder metallurgy (P/M) has been executed, and the process parameters have been highlighted applying Taguchi's L9 orthogonal array. The results show that the peak current (Ip), gas flow rate (GFR), welding speed (WS) and base current (Ib) are the critical constraints in strong determinant of the Tensile strength (TS) as well as percentage of elongation (% Elong) of the joint. The practical impact of applying Genetic algorithm (GA) and Simulated annealing (SA) to PCGTAW process has been authenticated by means of calculating the deviation between predicted and experimental welding process parameters. [ABSTRACT FROM AUTHOR]

This paper explains the effect of turning parameters such as cutting speed, feed rate, depth of cut and cutting tool nose radius on surface roughness of hybrid metal matrix (Al-SiC-Fly ash) composite. Experiments have been conducted based on the orthogonal array L16(4) and surface roughness was tested on the composites turned by an high speed CNC centre lathe. Analysis of variance (ANOVA) was performed to predict the significant parameters and their contribution towards surface finish of the composite. A mathematical model was developed using non-linear regression analysis. Taguchi method and Genetic algorithm have been employed to optimize the turning parameters for optimum surface roughness of the composite. The optimum turning parametric conditions have been checked with the confirmation experiments. It has been noted that the optimum condition of genetic algorithm exhibited better results than the experimental results based on the orthogonal array and the optimum condition of Taguchi method. [ABSTRACT FROM AUTHOR]

The present study aims to implement a new selection method and a novel crossover operation in a real-coded genetic algorithm. The proposed selection method facilitates the establishment of a successively evolved population by combining several subpopulations: an elitist subpopulation, an off-spring subpopulation and a mutated subpopulation. A probabilistic crossover is performed based on the measure of probabilistic distance between the individuals. The concept of 'allowance' is suggested to describe the level of variance in the crossover operation. A number of nonlinear/non-convex functions and engineering optimization problems are explored to verify the capacities of the proposed strategies. The results are compared with those obtained from other genetic and nature-inspired algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slidercrank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

To maximize buckling loads of composite laminates, optimization is carried out using a Genetic algorithm (GA) in conjunction with finite element analysis. A perforated laminated composite plate is used for buckling analysis. The geometry is discretized into solid threedimensional twenty node isoparametric layered composite elements developed for this study. Fiber orientations of each element and individual plies are controlled independently by the genetic algorithm, which is especially advantageous for complex problems including many design variables. This approach for composite buckling produces more enhanced results than conventional methods, such as changing the stacking sequence of various rectilinear orthotropic plies with different fiber orientations, different ply thicknesses or different local fiber densities. Additionally, it can be used in diverse areas from sensitive local buckling to global stability of structures. The genetic algorithm, finite element analysis and eigen buckling analysis are numerically combined into a composite optimization code, COMBO20. The successful performance of the proposed approach is demonstrated with an example. [ABSTRACT FROM AUTHOR]

The position of vibration sensors influences the modal identification quality of flexible structures for a given number of sensors, and the quality of modal identification is usually estimated in terms of correlation between the natural modes using the modal assurance criterion (MAC). The sensor placement optimization is characterized by the fact that the design variables are not continuous but discrete, implying that the conventional sensitivity-driven optimization methods are not applicable. In this context, this paper presents the application of genetic algorithm to the sensor placement optimization for improving the modal identification quality of flexible structures. A discrete-type optimization problem using genetic algorithm is formulated by defining the sensor positions and the MAC as the design variables and the objective function, respectively. The proposed GA-based evolutionary optimization method is validated through the numerical experiment with a rectangular plate, and its excellence is verified from the comparison with the cases using different modal correlation measures. [ABSTRACT FROM AUTHOR]

A system analysis program for the preliminary design of a liquid rocket engine is developed to analyze performance according to the input variables and the requirements needed. It contains sub-system analysis program modules that include the main thrust chamber, gasgenerator, turbo pumps, and turbine. The hydraulic system, which includes pipes, valves, and elbows, is considered to measure mass and pressure loss. The analysis process is developed to satisfy energy balance, mass-flow balance, and pressure balance. It also determines the design parameters with a higher specific impulse and thrust-to-weight ratio based on the genetic algorithm. The results of the program analysis are consistent with the reference of existing liquid rocket engines. The developed program is acceptable as a preliminary design process for a liquid rocket engine. [ABSTRACT FROM AUTHOR]

An electrostatic forming membrane reflector with AstroMesh structural support is a new concept for space-borne deployable antennas. A membrane shape-control optimization model is proposed in this study for electrode design to achieve high reflector precision. In this model, the electrode number, channel number, and voltages are considered design variables, the membrane reflector precision is an objective function, and the positivity and uniformity of membrane stress are constraints. A technology that combines a gradient method and genetic algorithm is proposed to solve the optimization model. This technology includes continuous and discrete design variables with different values and dimensions. The validity and effectiveness of the optimization model are demonstrated via the numerical example of a 5 m membrane reflector and a 0.55 m experimental prototype. [ABSTRACT FROM AUTHOR]

A preliminary design method and genetic algorithm were used to design a methane-fuel rocket combustor that can be beneficial for next-generation rocket engines. The O/F ratio, aspect ratio of the cooling channel, number of cooling channels, and mass flux of coolant were considered as design variables. A Monte Carlo simulation was conducted and the correlation tendency between the design variables and objective parameters were identified using random variables. Two methods for variable application and two types of objective functions were compared for optimization, namely, addition type and multiplication type functions with all variables and restricted variables. The multiplication type of the objective function with restricted variables presented the best optimization results. [ABSTRACT FROM AUTHOR]

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems. [ABSTRACT FROM AUTHOR]

Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we address an inspection policy problem for a one-shot system with two types of units, namely, Type 1 units that fail at random times and Type 2 units that degrade with time. Interval availability and life cycle cost are used as optimization criteria and estimated by simulation. We determine inspection intervals, preventive replacement ages of Type 1 units, and preventive maintenance thresholds of Type 2 units that have minimal life cycle cost and satisfy the target interval availability during inspection periods. A simulation-based optimization procedure using a hybrid genetic algorithm is proposed to find near-optimal solutions. Numerical examples are studied to investigate the effects of model parameters on optimal solutions and compare the hybrid genetic algorithm with the general genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link's inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature. [ABSTRACT FROM AUTHOR]