Uncertainty and Interpretability Studies in Soft Computing with an Application to Complex Manufacturing Systems

In systems modelling and control theory, the benefits of applying neural networks have been extensively studied. Particularly in manufacturing processes, such as the prediction of mechanical properties of heat treated steels. However, modern industrial processes usually involve large amounts of data and a range of non-linear effects and interactions that might hinder their model interpretation. For example, in steel manufacturing the understanding of complex mechanisms that lead to the mechanical properties which are generated by the heat treatment process is vital. This knowledge is not available via numerical models, therefore an experienced metallurgist estimates the model parameters to obtain the required properties. This human knowledge and perception sometimes can be imprecise leading to a kind of cognitive uncertainty such as vagueness and ambiguity when making decisions. In system classification, this may be translated into a system deficiency - for example, small input changes in system attributes may result in a sudden and inappropriate change for class assignation. In order to address this issue, practitioners and researches have developed systems that are functional equivalent to fuzzy systems and neural networks. Such systems provide a morphology that mimics the human ability of reasoning via the qualitative aspects of fuzzy information rather by its quantitative analysis. Furthermore, these models are able to learn from data sets and to describe the associated interactions and non-linearities in the data. However, in a like-manner to neural networks, a neural fuzzy system may suffer from a lost of interpretability and transparency when making decisions. This is mainly due to the application of adaptive approaches for its parameter identification. Since the RBF-NN can be treated as a fuzzy inference engine, this thesis presents several methodologies that quantify different types of uncertainty and its influence on the model interpretability and transparency of the RBF-NN during its parameter identification. Particularly, three kind of uncertainty sources in relation to the RBF-NN are studied, namely: entropy, fuzziness and ambiguity. First, a methodology based on Granular Computing (GrC), neutrosophic sets and the RBF-NN is presented. The objective of this methodology is to quantify the hesitation produced during the granular compression at the low level of interpretability of the RBF-NN via the use of neutrosophic sets. This study also aims to enhance the disitnguishability and hence the transparency of the initial fuzzy partition. The effectiveness of the proposed methodology is tested against a real case study for the prediction of the properties of heat-treated steels. Secondly, a new Interval Type-2 Radial Basis Function Neural Network (IT2-RBF-NN) is introduced as a new modelling framework. The IT2-RBF-NN takes advantage of the functional equivalence between FLSs of type-1 and the RBF-NN so as to construct an Interval Type-2 Fuzzy Logic System (IT2-FLS) that is able to deal with linguistic uncertainty and perceptions in the RBF-NN rule base. This gave raise to different combinations when optimising the IT2-RBF-NN parameters. Finally, a twofold study for uncertainty assessment at the high-level of interpretability of the RBF-NN is provided. On the one hand, the first study proposes a new methodology to quantify the a) fuzziness and the b) ambiguity at each RU, and during the formation of the rule base via the use of neutrosophic sets theory. The aim of this methodology is to calculate the associated fuzziness of each rule and then the ambiguity related to each normalised consequence of the fuzzy rules that result from the overlapping and to the choice with one-to-many decisions respectively. On the other hand, a second study proposes a new methodology to quantify the entropy and the fuzziness that come out from the redundancy phenomenon during the parameter identification. To conclude this work, the experimental results obtained through the application of the proposed methodologies for modelling two well-known benchmark data sets and for the prediction of mechanical properties of heat-treated steels conducted to publication of three articles in two peer-reviewed journals and one international conference

The role of end-use energy conversion efficiency as a climate mitigation tool

Historically, conversion efficiency improvements have revolutionised the energy system, yet to reach climate targets, the scientific community agrees that even higher levels of energy efficiency improvement are required. When focusing on technical options there are two classes of technologies: conversion devices and passive systems. This thesis explores the role that the former can have in reducing energy demand with the aim of providing advice on the prioritisation and differentiation of policy action among these devices. The analysis is divided into three main chapters. First, issues with data quality were identified a cause for the marginalisation of end-use efficiency measures compared to supply-side ones. For the first time, the uncertainty of end-use statistics is quantified by drawing from methods developed in the field of Material Flow Analysis using the United Kingdom as a case study. The majority (85%) of the Useful energy balance uncertainties are below an acceptable (±25%) threshold. Therefore, end-use statistics are deemed sufficiently reliable for the development of policy-relevant indicators. Second, the technical efficiency limits for six widely used conversion devices are determined stochastically based on a combination of engineering models and review of the technical literature. The resulting limits are used to calculate the energy saving potential of each conversion device, and each design parameter for the United Kingdom. It is shown that 25% of the UK’s Final energy demand could be avoided if all conversion devices reached their technical limit. On the other hand, 15% savings could be achieved by applying available technology. Nonetheless, improvement margins vary substantially among devices meaning that strategies involving different balances of R&D and technology adoption incentives are required for each technology. Third, the International Energy Agency’s Energy Technology Perspective’s modelling results are used to assess the saving potential of seven conversion devices in three emission scenarios. Between 3.2% and 4.2% of cumulative energy demand between 2014 and 2060 can be saved thanks to improvements in conversion efficiency. Most savings come from improved internal combustion engines in all scenarios. Carbon emission savings from conversion efficiency are highest in the baseline scenario and lowest in the most ambitious climate scenario due to negative emissions in electricity generation nullifying the effect of improvements in electricity-using devices. No technology was found to breach the technical efficiency limit in the IEA’s assessment meaning that expected efficiency improvements technically realistic. Current innovation activity in energy conversion devices is quantified by means of patent counts and it’s compared to the distribution of saving potentials. It is found that innovation in air coolers and heat pumps is low when compared to the expected efficiency savings from these technologies. The thesis results are useful for directing policy and investment priorities for conversion devices as function of the ambition of the climate scenario. The analysis of technical efficiency limits for conversion devices, help improve energy system models. The novel uncertainty method provides a powerful tool for supporting energy planning and decision making

Manufacturing of aluminium composite materials : a review

Abstract: Aluminium composite materials are becoming very popular as a result of their physical and mechanical characteristics, which are making them relevant to various applications. The addition of reinforcement materials with unique characteristics into aluminium produces aluminium composites with superior quality. Wear resistance, stiffness, strength and hardness are some of the improved properties obtained when reinforcement materials were added to the primary aluminium. This chapter presents some of the manufacturing processes of aluminium, its alloys and composites. The effects of reinforcements on aluminium composites from existing work and research direction on the fabrication of aluminium composite materials were discussed in this chapter

Materials & Machines: Simplifying the Mosaic of Modern Manufacturing

Manufacturing in modern society has taken on a different role than in previous generations. Today’s manufacturing processes involve many different physical phenomenon working in concert to produce the best possible material properties. It is the role of the materials engineer to evaluate, develop, and optimize applications for the successful commercialization of any potential materials. Laser-assisted cold spray (LACS) is a solid state manufacturing process relying on the impact of supersonic particles onto a laser heated surface to create coatings and near net structures. A process such as this that involves thermodynamics, fluid dynamics, heat transfer, diffusion, localized melting, deformation, and recrystallization is the perfect target for developing a data science framework for enabling rapid application development with the purpose of commercializing such a complex technology in a much shorter timescale than was previously possible. A general framework for such an approach will be discussed, followed by the execution of the framework for LACS. Results from the development of such a materials engineering model will be discussed as they relate to the methods used, the effectiveness of the final fitted model, and the application of such a model to solving modern materials engineering challenges

Investigating the Physical Properties of Planetary Surfaces using the Huygens Penetrometer

The Huygens probe landed on the unknown surface of Titan on 14 January 2005. Onboard, a small, protruding hemispherical-tipped penetrometer, ACC-E, part of the Surface Science Packagem impacted the surface returning a short ~20 ms force signature of the mechanical resistance of the ground. The purpose of this thesis is to examine the response of an identical penetrometer to various terrestrial target materials and provide an insight into the penetrometer's performance and assess its capability. Signatures collected at the Huygens impact speed of both natural and artificial material targets in the laboratory are examined using various descriptive statistical measures. Structural and textural properties are identified and the penetrometer's response to granular targets, both wet and dry, is examined. Mitigation of the effects of target boundaries and velocity dependence of the signatures are both considered. Terrestrial fieldwork penetrometry is presented that demonstrates that even for short penetration depths the sensor is capable of distinguishing several types of geological surface features. A physical model extended for layered targets is presented and applied to laboratory data. Using the results from the laboratory and fieldwork, together with more specific experiments, a possible interpretation of the signature returned from Titan is presented and discussed within the context of recent findings from other investigations

Detector Technologies for CLIC

The Compact Linear Collider (CLIC) is a high-energy high-luminosity linear electron-positron collider under development. It is foreseen to be built and operated in three stages, at centre-of-mass energies of 380 GeV, 1.5 TeV and 3 TeV, respectively. It offers a rich physics program including direct searches as well as the probing of new physics through a broad set of precision measurements of Standard Model processes, particularly in the Higgs-boson and top-quark sectors. The precision required for such measurements and the specific conditions imposed by the beam dimensions and time structure put strict requirements on the detector design and technology. This includes low-mass vertexing and tracking systems with small cells, highly granular imaging calorimeters, as well as a precise hit-time resolution and power-pulsed operation for all subsystems. A conceptual design for the CLIC detector system was published in 2012. Since then, ambitious R&D programmes for silicon vertex and tracking detectors, as well as for calorimeters have been pursued within the CLICdp, CALICE and FCAL collaborations, addressing the challenging detector requirements with innovative technologies. This report introduces the experimental environment and detector requirements at CLIC and reviews the current status and future plans for detector technology R&D.Comment: 152 pages, 116 figures; published as CERN Yellow Report Monograph Vol. 1/2019; corresponding editors: Dominik Dannheim, Katja Kr\"uger, Aharon Levy, Andreas N\"urnberg, Eva Sickin

Process Modeling in Pyrometallurgical Engineering

The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions

A multi-physics visco-plasticity theory for porous sedimentary rocks

In this thesis a physics-based constitutive theory for sedimentary porous rocks is proposed combining the results of laboratory tests, theoretical analysis, and numerical validation. The motivation for this framework stems from triaxial experiments on calcarenite performed up to 50% axial strain inside an X-Ray CT-scan. These tests revealed that: 1) calcarenite plastified at the first increment of displacement; 2) with increasing axial strain, the material underwent a phase change where all the inter-granular bonds broke, the pore space collapsed and the material behaved as sand; 3) in repeating loading-unloading cycles and relaxation tests, the deformation of the rock became increasingly more rate-dependent with strain, as a result of the aforementioned phase change and reorganization of released grains. Motivated by these experiments, a visco-plastic flow law is proposed. The viscosity of the material is assumed to be a function of the temperature, pore-pressure and energy required to alter the inter-granular interfaces. Thus, stress equilibrium and flow law are fully coupled to the energy and mass conservation laws, constituting a closed system of equations. In order to solve this system, the theoretical framework is implemented into the tightly coupled Finite Element code REDBACK, and its qualitative behaviour is analysed in monotonous and cyclic isotropic compression as well as in direct shear for different loading rates. A series of numerical calibration tests against different types of rocks (sandstone, mudstone, calcarenite), saturating conditions (dry, wet) and stress paths (triaxial, isotropic) is then performed, concluding that the mechanical response of sedimentary porous rocks in strains usually achieved in laboratory testing is determined by the strength of the cementitious material bonding the grains. The latter is shown to be stress path dependent under the hypotheses made in this thesis and the interfaces are shown to obey a Kelvin-like law at the microscopic level. Finally, the proposed framework is applied at geophysical scale problem and is qualitatively linked with theoretical studies of landslide and faults in the literature. A reinterpretation of the brittle to ductile transition is then attempted linking the two cases (brittle and ductile) to the types of instabilities that the model theoretically predicts