The Sustainability Of Neural Network Applications Within Finite Element Analysis In Sheet Metal Forming: A Review

The prediction of springback in sheet metal is vital to ensure economical metal forming. The latest nonlinear recovery in finite element analysis is used to achieve accurate results, but this method has become more complicated and requires complex computational programming to develop a constitutive model. Having the potential to assist the complexity, computational intelligence approach is often regarded as a statistical method that does not contribute to the development of a constitutive model. To provide a reference for researchers who are studying the potential application of computational intelligence in springback research, a review of studies into the development of sheet metal forming and the application of neural network to predict springback is presented in this research paper. It can be summarized as: (1) Springback is influenced by various factors that are involved in the sheet metal forming process. (2) The main complexity in FE analysis is the development of a constitutive model of a material that has the potential to be solved by using the computational intelligence approach. (3) The existing neural network approach for solving springback predictions is unable to represent all the factors that affect the results ofthe analysi

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Metamodel-based uncertainty quantification for the mechanical behavior of braided composites

The main design requirement for any high-performance structure is minimal dead weight. Producing lighter structures for aerospace and automotive industry directly leads to fuel efficiency and, hence, cost reduction. For wind energy, lighter wings allow larger rotor blades and, consequently, better performance. Prosthetic implants for missing body parts and athletic equipment such as rackets and sticks should also be lightweight for augmented functionality. Additional demands depending on the application, can very often be improved fatigue strength and damage tolerance, crashworthiness, temperature and corrosion resistance etc. Fiber-reinforced composite materials lie within the intersection of all the above requirements since they offer competing stiffness and ultimate strength levels at much lower weight than metals, and also high optimization and design potential due to their versatility. Braided composites are a special category with continuous fiber bundles interlaced around a preform. The automated braiding manufacturing process allows simultaneous material-structure assembly, and therefore, high-rate production with minimal material waste. The multi-step material processes and the intrinsic heterogeneity are the basic origins of the observed variability during mechanical characterization and operation of composite end-products. Conservative safety factors are applied during the design process accounting for uncertainties, even though stochastic modeling approaches lead to more rational estimations of structural safety and reliability. Such approaches require statistical modeling of the uncertain parameters which is quite expensive to be performed experimentally. A robust virtual uncertainty quantification framework is presented, able to integrate material and geometric uncertainties of different nature and statistically assess the response variability of braided composites in terms of effective properties. Information-passing multiscale algorithms are employed for high-fidelity predictions of stiffness and strength. In order to bypass the numerical cost of the repeated multiscale model evaluations required for the probabilistic approach, smart and efficient solutions should be applied. Surrogate models are, thus, trained to map manifolds at different scales and eventually substitute the finite element models. The use of machine learning is viable for uncertainty quantification, optimization and reliability applications of textile materials, but not straightforward for failure responses with complex response surfaces. Novel techniques based on variable-fidelity data and hybrid surrogate models are also integrated. Uncertain parameters are classified according to their significance to the corresponding response via variance-based global sensitivity analysis procedures. Quantification of the random properties in terms of mean and variance can be achieved by inverse approaches based on Bayesian inference. All stochastic and machine learning methods included in the framework are non-intrusive and data-driven, to ensure direct extensions towards more load cases and different materials. Moreover, experimental validation of the adopted multiscale models is presented and an application of stochastic recreation of random textile yarn distortions based on computed tomography data is demonstrated

Contributing Towards Improved Communication Systems for Future Cellular Networks

The rapid growth of wireless communications and upcoming requirements of 5G networks are driving interest in the areas from wireless transceivers to sensor nodes. One of the most vital components of the wireless transmitter is the radio frequency power amplifier. A large-signal device model of the transistor is an essential part of the power amplifier design process. Despite the significant developments in large-signal modelling, the models for commercially available devices from the manufacturers are still under continuous development and often lack accuracy. One of the main objectives of this thesis is the validation and extension of an analytic approach as an alternative to conventional large-signal modelling for power amplifier designing. The first contribution is the derivation of new analytical expressions based on the equivalent circuit model, including the extrinsic parasitic elements introduced by the package, to calculate the optimum source and load impedances and to predict the performance of a radio frequency power amplifier. These expressions allow to evaluate the effects of a package on the optimum impedance values and performance. The second contribution is establishing the accuracy of the analytic approach. Harmonic balance simulation is used as the first benchmark to evaluate the method at various bias points and frequencies. The validity of the analytic approach is demonstrated at a frequency of 3.25 GHz for gallium nitride based high power devices with measurement of prototype radio frequency power amplifier designed for the impedance values obtained from the analytic expressions. The third contribution is extending the analytic approach to determine the optimum impedance values for different criteria of maximum gain, linearity and efficiency. The analytic expressions are utilized to gain an understanding of the relationship among the device performance, the elements of devices and package models and I-V characteristics. The wireless sensor networks are essential elements for the realization of the Internet of Things. Sensor nodes, which are the fundamental building blocks of these networks, have to be energy efficient and able to produce energy to reduce the maintenance cost and to prolong their lifetime. The second main aim of the thesis is designing and implementing an ultra-low power autonomous wireless sensor node that harvests the indoor light energy. The forth contribution of this thesis includes a comprehensive comparison of six different solar cell technologies under a controlled light intensity, carried out to determine the best option for indoor light energy harvesting. The power consumption of the node is reduced by selecting the appropriate hardware and implementing a wake-up receiver to reduce the active and idle mode currents. The low power consumption coupled with light energy harvesting significantly prolong the operating lifetime of the node