Genetic algorithm-based pore network extraction from micro-computed tomography images

A genetic-based pore network extraction method from micro-computed tomography (micro-CT) images is proposed in this paper. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two approaches to generate the pore network structure are presented. Unlike previous algorithms, the presented approaches are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the network. In the first approach, only the difference between the real micro-CT images of the porous medium and the sliced images from the generated network is selected as the objective function which is minimized via a genetic algorithm (GA). In order to further improve the structure and behavior of the generated network, making it more representative of the real porous medium, a second optimization has been used in which the contrast between the experimental and the predicted values of the network permeability is minimized via GA. We present two case studies for two different complex geological porous media, Clashach sandstone and Indiana limestone. We compare porosity and permeability predicted by the GA generated networks with experimental values and find an excellent match

Full-field pulsed magneto-photoelasticity – Experimental Implementation

This paper contains a description of the experimental procedure employed when using a pulsed-magneto-polariscope (PMP) and some initial full-field through-thickness measurements of the stress distribution present in samples containing 3D stresses. The instrument uses the theory of magneto-photoelasticity (MPE), which is an experimental stress analysis technique that involves the application of a magnetic field to a birefringent model within a polariscope. MPE was developed for through-thickness stress measurement where the integrated through-thickness birefringent measurement disguises the actual stress distribution. MPE is mainly used in toughened glass where the through-thickness distribution can reduce its overall strength and so its determination is important. To date MPE has been a single-point 2D through-thickness measurement and the analysis time is prohibitive for the investigation of an area which may contain high localised stresses. The pulsed-magneto-polariscope (PMP) has been designed to enable the application of full-field 3D MPE [ ]. Using a proof-of concept PMP several experimental measurements were made, these were promising and demonstrate the potential of the new instrument. Further development of this technique presents several exciting possibilities including a tool for the measurement of the distribution of principal stress difference seen in a general 3D model

Automated Design and Optimization of Metallic Alloys

The design and optimization of metallic alloys poses a significant engineering challenge. The search space of all possible alloys is sufficiently large that it is impossible to fully explore by traditional methods. In order to address this challenge, physics based computational frameworks linked to advanced machine learning algorithms can serve to automate this process with computational efficiency such that the state of the industry may be rapidly advanced. The work herein presents a suite of computational frameworks leveraged to automate the design and optimization process of advanced alloys. An ab initio alloy thermodynamics system, Molecular Dynamics simulations, a Convolutional-Neural Network system, and a coupled Neural Network and Multi-objective Genetic Algorithm. These algorithms are validated over the set of binary nanocrystalline Al-X alloys, and multi-component High Entropy Alloys (HEA)

Vibrational Spectroscopy and Chemometrics Applied to the Forensic Analysis of Automotive Paints and Edible Oils

Profiling of complex materials (e.g., automotive paint and cooking oil) with infrared and Raman spectroscopy is an active area of research with a large and growing literature. The object of profile analysis is to correlate a characteristic fingerprint pattern in a spectrum with the properties of the sample. Objective analysis of these profiles depends upon the use of multivariate curve statistical methods. In this regard, pattern recognition techniques have been found to be of enormous utility. In this dissertation, several projects were undertaken to demonstrate the advantages of chemical fingerprinting using spectroscopic techniques to solve problems in the areas of food chemistry and forensic science. In one study, Raman spectra of 15 varieties of edible oils obtained from 53 samples purchased over a 3 year period representing different production years and vendors were analyzed by pattern recognition methods using a hierarchical classification procedure. Supplier to supplier variability and seasonal variability within a supplier were the major sources of variation with the Raman spectral data. Edible oils assigned to one group could be readily differentiated from those assigned to other groups, whereas Raman spectra within the same group more closely resemble each other and therefore were more difficult to classify by type. In another study, IR microscopic imaging and a prototype pattern recognition library search system were applied to the forensic examination of automotive paint using a new methodology for cross sectioning paint samples and decatenating infrared spectral images. Successful methods developed in test experiments such as the studies described in this dissertation will become part of the routine analytical practices of chemists in the very near future

Finite Element Analysis and Design Optimization of Deep Cold Rolling of Titanium Alloy at Room and Elevated Temperatures

High strength-to-density ratio, high corrosion resistance and superior biocompatibility are the main advantages of Ti-6Al-4V (Ti64), making it a long been favored titanium alloy for aerospace and biomedical applications. Designing titanium components to last longer and refurbishing of aged ones using surface treatments have become a desirable endeavor considering high environmental damage, difficulty in casting, scarcity and high cost associated with this metal. Among mechanical surface treatments, Deep Cold Rolling (DCR) has been shown to be a very promising process to improve fatigue life by introducing a deep compressive residual stress and work-hardening in the surface layer of components. This process has shown to be superior compared with other surface treatment methods as it yields a better surface quality and induces a deeper residual stress profile which can effectively be controlled through the process parameters (i.e. ball diameter, rolling pressure and feed). However, residual stresses induced through this process at room temperature are generally relaxed upon exposure of the components to elevated operating temperatures. In this work, high-fidelity Finite Element (FE) models have been developed to simulate the DCR process in order to predict the induced residual stresses at room temperature and their subsequent relaxation following exposure to temperature increase. Accuracy of the developed models has been validated using experimental measurements available in the literature. A design optimization strategy has also been proposed to identify the optimal process parameters to maximize the induced beneficial compressive residual stress on and under the surface layer and thus prolong the fatigue life. Conducting optimization directly on the developed high-fidelity FE model is not practical due to high computational cost associated with nonlinear dynamic models. Moreover, responses from the FE models are typically noisy and thus cannot be utilized in gradient based optimization algorithms. In this research study, well-established machine learning principles are employed to develop and validate surrogate analytical models based on the response variables obtained from FE simulations. The developed analytical functions are smooth and can efficiently approximate the residual stress profiles with respect to the process parameters. Moreover the developed surrogate models can be effectively and efficiently utilized as explicit functions for the optimization process. Using the developed surrogate models, conventional (one-sided) DCR process is optimized for a thin Ti64 plate considering the material fatigue properties, operating temperature and external load. It is shown that the DCR process can lead to a tensile balancing residual stress on the untreated side of the component which can have a detrimental effect on the fatigue life. Additionally, application of conventional DCR on thin geometries such as compressor blades can cause manufacturing defects due to unilateral application of the rolling force and can also lead to thermal distortion of the part due to asymmetric profile of the induced residual stresses. Double-sided deep rolling has been shown as a viable alternative to address those issues since both sides of the component are treated simultaneously. The process induces a symmetric residual stress which can be further optimized to achieve a compressive residual stress on both sides of the component. For this case, a design optimization problem is formulated to improve fatigue life in high stress locations on a generic compressor blade. All the optimization problems are formulated for multi-objective functions to achieve most optimal residual stress profiles both at room temperature as well as elevated temperature of 450℃. A hybrid optimization algorithm based on combination of sequential quadratic programming (SQP) technique with stochastic based genetic algorithm (GA) has been developed to accurately catch the global optimum solutions. It has been shown that the optimal solution depends on the stress distribution in the component due to the external load as well as the operating temperature

A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas

In utility based service industries with a large mobile workforce, there is a need to optimize the process of allocating engineers to tasks (i.e. fixing faults, installing new services, such as internet connections, gas or electricity etc.). Part of the process of optimizing the resource allocation to tasks involves finding the optimum area for an engineer to operate within, which we term as work area optimization. Work area optimization in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. However when attempting to optimize the workforce in real world scenarios, mostly single objective optimization algorithms are used while employing crisp logic. Nevertheless, there are many objectives that need to be satisfied and hence multi-objective based optimization will be more suitable. Even where multi-objective optimization is employed, the involved systems fail to recognize that these real world problems are full of uncertainties. Type-2 fuzzy logic systems can handle the high level of uncertainties associated with the dynamic and changing environments, such as those presented with real world scheduling problems. This paper presents a novel multi-objective genetic type-2 Fuzzy Logic based System for the optimal allocation of mobile workforces to their working areas. The method has been applied in a real world service industry workforce environment. The results show strong improvements when the proposed multi-objective type-2 fuzzy genetic based optimization system was applied to the work area optimization problem as compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in improving the utilization of the workforce

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing