Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Information embedding and retrieval in 3D printed objects

Deep learning and convolutional neural networks have become the main tools of computer vision. These techniques are good at using supervised learning to learn complex representations from data. In particular, under limited settings, the image recognition model now performs better than the human baseline. However, computer vision science aims to build machines that can see. It requires the model to be able to extract more valuable information from images and videos than recognition. Generally, it is much more challenging to apply these deep learning models from recognition to other problems in computer vision. This thesis presents end-to-end deep learning architectures for a new computer vision field: watermark retrieval from 3D printed objects. As it is a new area, there is no state-of-the-art on many challenging benchmarks. Hence, we first define the problems and introduce the traditional approach, Local Binary Pattern method, to set our baseline for further study. Our neural networks seem useful but straightfor- ward, which outperform traditional approaches. What is more, these networks have good generalization. However, because our research field is new, the problems we face are not only various unpredictable parameters but also limited and low-quality training data. To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the image segmentation area, and (ii) we cannot know everything from data, our models should be aware what key features they should learn. This thesis explores these ideas and even explore more. We show how to use end-to-end deep learning models to learn to retrieve watermark bumps and tackle covariates from a few training images data. Secondly, we introduce ideas from synthetic image data and domain randomization to augment training data and understand various covariates that may affect retrieve real-world 3D watermark bumps. We also show how the illumination in synthetic images data to effect and even improve retrieval accuracy for real-world recognization applications

Lattice dynamics in perovskites for green energy applications: A theoretical perspective

Electrolyzers and fuel cells are used in green energy applications, electrolyzers split water to produce hydrogen, which can then be used in fuel cells to produce energy. Oxide perovskites have shown favorable properties for applications in this area, e.g., as electrolyte and cathode material in fuel cells and electrolyzers. The important property is the conductivity of protons, which depends sensitively on the hydrogen concentration and mobility. The concentration depends on the efficiency of the hydration reaction, which is the primary way to incorporate protons in perovskites. An example of an excellent proton conductor is acceptor doped BaZrO3. Hence, some of the most crucial material properties derive from defect properties. This thesis also explore the halide perovskites CsPbBr3, which have proven to be auspicious for photovoltaics. Insights into phase stability, phase transitions and the underlying dynamics in these materials are crucial. Thus, the understanding of microscopic properties is the cornerstone of this thesis.In the present thesis, density functional theory is utilized to obtain training data for construction of potentials. The potentials that have been used are either force constant potentials or neural network potentials. The potential are then used to run lattice dynamics. To vastly extend the total simulation time or simply decrease the computational time, graphical processing units are also employed. Furthermore, defect models are applied to understand reaction kinetics.More specifically, the vibrational defect thermodynamics of BaZrO3 was examined within the harmonic approximation. We also elaborate on the soft antiferrodistortive phonon mode found in this material using self-consistent phonons and molecular dynamics. This soft mode, should ultimately be the deciding factor for which structure \ch{BaZrO3} exhibit at low temperatures. Similar methods were also employed to investigate phonon dynamics in the very anharmonic perovskite, CsPbBr3. These type of insights can, e.g., further guide the development of new materials by fine-tuning of properties

Investigation of the design, manufacture and testing of additively manufactured coils for electric motor applications

Electric motor design has been relatively unchanged for nearly a century. However, there is a movement in our world to replace inefficient combustion technology with electricity. While current electric motor technology is being used in numerous areas, manufacturing has been limited to traditional techniques which result in inefficient and unreliable machines for achieving those electrification goals. As a result, there needs to be a shift in how these motors are manufactured and designed. Additive manufacturing (AM) can provide this shift, however, there has been both a lack of information on how to use AM to design for these applications and of electrical properties for AM materials processed by Laser Powder Bed Fusion (LPBF). This work helps to fill in some of this missing information. The first part of this work used DfAM and first principles to design coils which help achieve the goals of efficient, powerful and robust electric motors. It was demonstrated that AM can greatly increase the fill factor of a motor which increases its power density and efficiency. It can minimise the amount of support material required which aids in creating coils with AM. AM can also modify the end-turns of a coil to aid in thermal dissipation which further improves efficiency and reliability. Copper is a common material for electrical applications but has been very difficult to process with LPBF due to its high reflectivity and high thermal conductivity. Despite this, some have attempted to process copper but failed to provide any electrical properties such as resistivity. Despite a wide range of parameter optimisation, copper was not able to be processed in this work to a high density. Despite this, resistivity measurements with respect to initial build orientation and heat treatments were taken and found to be lower than fully dense AlSi10Mg. Artificial intelligence was also used to perform a secondary quality assessment of individual thin walls to aid in parameter optimisation. With the challenges of processing a high purity material to a high density, an aluminium alloy which can be processed to a high density was then studied. AlSi10Mg is an alloy commonly used by LPBF, however, there has been an incomplete body of knowledge surrounding its electrical properties. Previous research has neglected initial build orientations, variations that heat treatment can cause, and used techniques that assumed isotropic properties. In this work, experiments were performed to characterise these effects to electrical resistivity and microstructure. In addition, a geometric accuracy study was performed in order to understand the differences between model and as-built dimensions. The results from this work can be used as a guide to aid motor designers in using AM for electric motor manufacture. Through these improvements, electric motors can potentially become more powerful and reliable. They can then aid in global electrification and help reduce greenhouse gas emissions

Investigation of the design, manufacture and testing of additively manufactured coils for electric motor applications

Electric motor design has been relatively unchanged for nearly a century. However, there is a movement in our world to replace inefficient combustion technology with electricity. While current electric motor technology is being used in numerous areas, manufacturing has been limited to traditional techniques which result in inefficient and unreliable machines for achieving those electrification goals. As a result, there needs to be a shift in how these motors are manufactured and designed. Additive manufacturing (AM) can provide this shift, however, there has been both a lack of information on how to use AM to design for these applications and of electrical properties for AM materials processed by Laser Powder Bed Fusion (LPBF). This work helps to fill in some of this missing information. The first part of this work used DfAM and first principles to design coils which help achieve the goals of efficient, powerful and robust electric motors. It was demonstrated that AM can greatly increase the fill factor of a motor which increases its power density and efficiency. It can minimise the amount of support material required which aids in creating coils with AM. AM can also modify the end-turns of a coil to aid in thermal dissipation which further improves efficiency and reliability. Copper is a common material for electrical applications but has been very difficult to process with LPBF due to its high reflectivity and high thermal conductivity. Despite this, some have attempted to process copper but failed to provide any electrical properties such as resistivity. Despite a wide range of parameter optimisation, copper was not able to be processed in this work to a high density. Despite this, resistivity measurements with respect to initial build orientation and heat treatments were taken and found to be lower than fully dense AlSi10Mg. Artificial intelligence was also used to perform a secondary quality assessment of individual thin walls to aid in parameter optimisation. With the challenges of processing a high purity material to a high density, an aluminium alloy which can be processed to a high density was then studied. AlSi10Mg is an alloy commonly used by LPBF, however, there has been an incomplete body of knowledge surrounding its electrical properties. Previous research has neglected initial build orientations, variations that heat treatment can cause, and used techniques that assumed isotropic properties. In this work, experiments were performed to characterise these effects to electrical resistivity and microstructure. In addition, a geometric accuracy study was performed in order to understand the differences between model and as-built dimensions. The results from this work can be used as a guide to aid motor designers in using AM for electric motor manufacture. Through these improvements, electric motors can potentially become more powerful and reliable. They can then aid in global electrification and help reduce greenhouse gas emissions

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Annual Report 2018-2019

It contains the statement of R&D works undertaken, achievement made and the expenditure by the laboratory during the financial year 2018-2019

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute