Crushing Behaviour of Woven Roving Glass Fibre/Epoxy Laminated Composite Rectangular Tubes Subjected to 'Quasi-Static Compressive Load

The automotive industry is exploring to adapting more fibre reinforced composite materials due to their stiffness to weight ratio. The amount of energy that a vehicle absorbs during a collision is a matter of concern to ensure safer and more reliable vehicle. The efficient use of composite material in the field of crash worthiness depends on the understanding of how a composite member absorbs and dissipates energy during the event of an impact. An experimental and finite element investigation of the woven roving glass fibre/epoxy laminated composite rectangular tubes subjected to compressive loading were carried out under compressive loading. Through out this investigation, rectangular tubes with different cross-sectional aspect ratio varying (alb) from 1 to 2 with 0.25 increment were investigated under axial and lateral loading conditions applied independently. The effects of increasing the cross-sectional aspect ratio on the load carrying capacity and the energy absorption capability were also presented and discussed. Finite element models to predict the load carrying capacity, failure mechanism and stress contours at pre-crush stage of the rectangular tubes under axial and lateral loading conditions have been developed. Experimental results show that the cross-sectional aspect ratio significantly affects the load carrying capacity and the energy absorption capability of the tubes. The axially loaded rectangular tubes have better load carrying capacity and energy absorption capability compared to the laterally loaded rectangular tubes. The buckling failure mode has been identified for the rectangular tubes under the different loading conditions. The developed finite element models approximately predict the initial failure load and the deformed shapes. The discrepancy between the finite element prediction and the experimental results is due to the assumption made in the finite element models and not considering the imperfection of the real tubes in the finite element models. From the experimental and finite element results 'knockdown' factors have been proposed to be used in the design phase of energy absorption elements to predict the initial failure load

Second Order Sliding Mode Control of the Coupled Tanks System

Four classes of second order sliding mode controllers (2-SMC) have been successfully applied to regulate the liquid level in the second tank of a coupled tanks system. The robustness of these classes of 2-SMC is investigated and their performances are compared with a first order controller to show the merits of these controllers. The effectiveness of these controllers is verified through computer simulations. Comparison between the controllers is based on the time domain performance measures such as rise time, settling time, and the integral absolute error. Results showed that controllers are able to regulate the liquid level with small differences in their performance

Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending

Bending has significant importance in the sheet metal product industry. Moreover, the spring back of sheet metal should be taken into consideration in order to produce bent sheet metal parts within acceptable tolerance limits and to solve geometrical variation for the control of manufacturing process. Nowadays, the importance of this problem increases because of the use of sheet-metal parts with high mechanical characteristics. This research proposes a novel approach to predict springback in the air bending process. In this approach the finite element method is combined with metamodeling techniques to accurately predict the springback. Two metamodeling techniques namely the neural network and the response surface methodology are used and compared to approximate two multidimensional functions. The first function predicts the springback amount for a given material, geometrical parameters, and the bend angle before springback. The second function predicts the punch displacement for a given material, geometrical parameters, and the bend angle after springback. The training data required to train the two-metamodeling techniques were generated using a verified nonlinear finite element algorithm developed in the current research. The algorithm is based on the updated Lagrangian formulation, which takes into consideration geometrical, material nonlinearity, and contact. To validate the finite element model physical experiments were conducted. A neural network algorithm based on the backpropagation algorithm has been developed. This research utilizes computer generated D-optimal designs to select training examples for both metamodeling techniques so that a comparison between the two techniques can be considered as fair. Results from this research showed that finite element prediction of springback is in good agreement with the experimental results. The standard deviation is 1.213 degree. It has been found that the neural network metamodels give more accurate results than the response surface metamodels. The standard deviation between the finite element method and the neural network metamodels for the two functions are 0.635 degree and 0.985 mm respectively. The standard deviation between the finite element method and the response surface methodology are 1.758 degree and 1.878 mm for both functions, respectively

Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances

Hybrid Integrated System for Air Bending Optimal Design

Genetic algorithm (GA) is widely accepted method for handling optimization problems. GA can find optimal solutions for large and irregular search spaces. However, finding optimal solutions using GA is associated with high computational time when coupled with finite element (FE) code, since FE analysis should be applied to each individual of GA populations. A neural network metamodel (NNM) is introduced to reduce the computational time.GA utilizes the NNMas an approximation tool instead of FE. Application examples results show that the metamodelcan be used efficiently to obtainthe optimal process parameters of metal forming operations with large saving in time

Tracking Control of Chaotic Systems via Optimized Active Disturbance Rejection Control

For tracking control of chaotic systems, we develop an active disturbance rejection (ADR) control method. Using the first state of the system as the only available state, a time-varying bandwidth extended state observer reconstructs the remaining states and the total disturbance. A time-varying bandwidth feedback controller forces all the states of the system to follow exactly the reference signal and its derivative. The parameters of the ADR controller are optimized using a genetic algorithm. As the objective function, we chose the weighted sum of the integral of the absolute error and the integral of the absolute control signal. Two chaotic systems—the Duffing system and the Genesio–Tesi system—are considered in computer simulation tests. Results of these simulations are presented to demonstrate the effectiveness of the ADRC method in controlling chaotic systems

On multistage approach for flexible routing in flexible manufacturing systems

Optimizing flexible routing in flexible manufacturing systems is one of the aspects that increase the efficiency of flexible manufacturing systems especially in dynamic environment systems. This article presents a multistage approach to solve flexible routing problem in flexible manufacturing systems. Multistage approach includes three stages; the first stage is a production simulation system to find the fitness of the flexible manufacturing systems corresponding to different products’ routes’ groups. The second stage proposes an artificial neural network approach to predict the products’ routes’ group in flexible manufacturing systems. The last stage combines genetic algorithms and artificial neural network to optimize proper routes for all product types in flexible manufacturing systems. Multistage approach proposed in this study aims to reduce the computational time required to obtain and optimize the flexible routes in flexible manufacturing systems. The results of this study show that the artificial neural network can be used efficiently to predict the flexible routes in flexible manufacturing systems and it reduces the computational time for routes’ optimization required with production simulation system. This characteristic improves the flexibility of flexible manufacturing systems since it can be adapted frequently against any change in production ratios

Layout design optimization of dynamic environment flexible manufacturing systems

The proper positioning of machine tools in flexible manufacturing system is one of the factors that lead to increase in production efficiency. Choosing the optimum position of machine tools curtails the total part handling cost between machine tools within the flexible manufacturing system. In this article, a two-stage approach is presented to investigate the best locations of the machine tools in flexible manufacturing system. The location of each machine tool is selected from the available specific and fixed locations in such a way that it will result in best throughput of the flexible manufacturing system. In the first stage of the two-stage approach, the throughput of randomly selected locations of the machine tool in flexible manufacturing system is computed by proposing a production simulation system. The production simulation system utilizes genetic algorithms to find the locations of the machine tools in flexible manufacturing system that achieve the maximum throughput of the flexible manufacturing system. In the second stage, the generated locations are fed into artificial neural network to find a relation between a machine tool’s location and the throughput that can be used to predict the throughput for any other set of locations. Artificial neural network will result in mitigating the computational time