Methods of measuring residual stresses in components

Residual stresses occur in many manufactured structures and components. Large number of investigations have been carried out to study this phenomenon and its effect on the mechanical characteristics of these components. Over the years, different methods have been developed to measure residual stress for different types of components in order to obtain reliable assessment. The various specific methods have evolved over several decades and their practical applications have greatly benefited from the development of complementary technologies, notably in material cutting, full-field deformation measurement techniques, numerical methods and computing power. These complementary technologies have stimulated advances not only in measurement accuracy and reliability, but also in range of application; much greater detail in residual stresses measurement is now available. This paper aims to classify the different residual stresses measurement methods and to provide an overview of some of the recent advances in this area to help researchers on selecting their techniques among destructive, semi destructive and non destructive techniques depends on their application and the availabilities of those techniques. For each method scope, physical limitation, advantages and disadvantages are summarized. In the end this paper indicates some promising directions for future developments

Design of ultraprecision machine tools with application to manufacturing of miniature and micro components

Currently the underlying necessities for predictability, producibility and productivity remain big issues in ultraprecision machining of miniature/microproducts. The demand on rapid and economic fabrication of miniature/microproducts with complex shapes has also made new challenges for ultraprecision machine tool design. In this paper the design for an ultraprecision machine tool is introduced by describing its key machine elements and machine tool design procedures. The focus is on the review and assessment of the state-of-the-art ultraprecision machining tools. It also illustrates the application promise of miniature/microproducts. The trends on machine tool development, tooling, workpiece material and machining processes are pointed out

Laser Wire Scanner Compton Scattering Techniques for the Measurement of the Transverse Beam Size of Particle Beams at Future Linear Colliders

This archive summarizes a working paper and conference proceedings related to laser wire scanner development for the Future Linear Collider (FLC) in the years 2001 to 2006. In particular the design, setup and data taking for the laser wire experiments at PETRA II and CT2 are described. The material is focused on the activities undertaken by Royal Holloway University of London (RHUL).Comment: 61 page

Laser beam characterisation for industrial applications

This thesis describes the theory, development and applications of laser beam characterisation for industrial laser materials processing systems. Descriptions are given of novel forms of beam diagnostic systems and their integration into highly automated industrial tools. Work is also presented that has contributed to the new ISO standard on beam characterisation. Particular emphasis is given to excimer laser applications and UV micromachining. [Continues.

Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components

This reprint is concerned with the microstructural characterization and the defect analysis of metallic additively manufactured (AM) materials and parts. Special attention is paid to the determination of residual stress in such parts and to online monitoring techniques devised to predict the appearance of defects. Finally, several non-destructive testing techniques are employed to assess the quality of AM materials and parts

An ultra-compact particle size analyser using a CMOS image sensor and machine learning

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125¿µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.This work is funded by the European Union’s Horizon 2020 research andinnovation programme under Grant Agreement No. 637232 (ProPAT project).R.H. and V.P. acknowledgefinancial support from the Spanish Ministry ofEconomy and Competitiveness through the‘Severo Ochoa’Programme forCentres of Excellence in R&D (SEV-2015-0522), from Fundació Privada Cellex,and from Generalitat de Catalunya through the CERCA programme, fromAGAUR 2017 SGR 1634. V.P. acknowledgesfinancial support from the SpanishMinistry of Economy and Competitiveness through the project OPTO-SCREEN(TEC2016-75080-R). This project has received funding from the EuropeanUnion’s Horizon 2020 research and innovation programme under the MarieSkłodowska-Curie grant agreement No 665884. The authors acknowledge theChemometrics group at the Universitat de Barcelona, especially Adrián GómezSánchez and Rodrigo Rocha de Oliveira, for their contribution in the helpfuldiscussions on measurement optimisation and background correction.Peer ReviewedPostprint (published version

An ultra-compact particle size analyser using a CMOS image sensor and machine learning

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring

Detection of particles, bacteria and viruses using consumer optoelectronic components

The focus of this thesis is on the design, development and validation of two novel photonic sensors for the detection and characterisation of industrial and biological samples. The first one is a PSA in a collimated beam configuration using an innovative angular spatial filter, and a consumer electronic camera similar to that used in a smartphone. The small form factor angular spatial filter allows for the collection of diffused light from particles up to predefined discrete angles. By using angularly resolved scattering images acquired by the camera, a machine learning (ML) algorithm predicts the volume median diameter of the particles. Our system has achieved a mean absolute percentage error of only 0.72% for spherical particles in solution with sizes greater than 10 µm and at concentrations up to 40 mg mL-1. Compared to traditional laser diffraction systems, the proposed PSA is an order of magnitude smaller in size, weight and cost, and offers a promising approach to online industrial process monitoring. As light scattering is influenced by factors other than particle size, including shape, refractive index contrast and suspension concentration, the PSA can also be employed in biological applications. To this end, the second part of the thesis aims to optimise the PSA for the measurement of small (< 10 µm) particles such as microorganisms. The results demonstrate that the modified PSA in combination with ML is able to accurately classify different types of bacteria (Escherichia coli and Enterococcus sp.) and distinguish them from silica beads of comparable sizes, with an accuracy of 89%. Moreover, it can detect the concentration of bacteria in water with a limit of detection (LOD) of approximately 105 cells mL-1. The final part of the thesis is dedicated to the development of a low-cost, portable optical biosensor for the specific detection of particles smaller than bacteria, such as viruses (< 1 µm). The proposed system, which we have called flow virometry reader (FVR), is a modification of a flow cytometer and relies on measuring light emissions from fluorescent antibodies that bind to specific viral particles. An LOD of 3,834 copies mL-1 for SARS-CoV-2 in saliva can be achieved with the device. The FVR is clinically validated using 54 saliva samples in a blind test, with high sensitivity and specificity of 91.2% and 90%, respectively. These findings suggest that the FVR has the potential to be a highly viable alternative to current diagnostic methods for pandemic events, as it is faster (< 30 min) and less expensive than PCR tests, while being more sensitive than today’s COVID-19 rapid antigen tests. The photonic sensing technologies developed in the thesis show significant potential for use in a wide range of applications, including: • particulate air pollution, causing cardiovascular and respiratory problems • particulate water pollution, which affects the ecosystems of rivers, lakes and oceans • total bacterial count in environmental or bathing water • viral pandemics The technologies are particularly appealing in countries with limited resources due to their simple design, portability, short time-to-result and affordability, as well as the fact that they do not require a specialised laboratory or trained personnel to operate them.El objetivo de esta tesis es el diseño, desarrollo y validación de dos nuevos sensores fotónicos para la detección y caracterización de muestras industriales y biológicas. El primero es un PSA en configuración de haz colimado que usa un innovador filtro espacial angular y una cámara electrónica similar a la usada en móviles. El pequeño factor de tamaño del filtro angular espacial permite la detección de la luz difusa de las partículas hasta ángulos discretos predefinidos. A partir del uso de imágenes difusas angularmente resueltas obtenidas por la cámara, un algoritmo de aprendizaje automático, machine learning (ML) en inglés, puede predecir la mediana del diámetro del volumen de las partículas. Nuestro sistema ha conseguido un error absoluto medio porcentual de solamente un 0.72% para partículas esféricas en disoluciones con tamaños superiores a 10 µm y concentraciones de hasta 40 mg mL-1. En comparación a sistemas tradicionales de difracción láser, el propuesto PSA es un orden de magnitud más pequeño en tamaño, peso y coste, y ofrece un enfoque prometedor para la supervisión online de procesos industriales. Dado que la difusión de luz depende de más factores aparte del tamaño de la partícula, incluyendo la forma, el contraste del índice de refracción y la suspensión de la concentración, el PSA también puede ser empleado en aplicaciones biológicas. Con este objetivo, la segunda parte de la tesis busca optimizar el PSA para la medida de partículas pequeñas (< 10 µm) como microorganismos. Los resultados demuestran que el PSA modificado en combinación con ML es capaz de clasificar con exactitud diferentes tipos de bacterias (Escherichia coli y Enterococcus sp.) y diferéncialas de partículas de silicio con tamaños similares, con una precisión del 89%. Además, puede detectar una concentración de bacterias en agua con un límite de detección (LOD en inglés) de aproximadamente 105 células mL-1. La parte final de tesis está dedicada al desarrollo de un biosensor óptico de bajo coste y portátil para la detección especifica de partículas más pequeñas que bacterias, como virus (< 1 µm). El sistema propuesto, el cual hemos llamado flow virometry reader (FVR), es una modificación de un citómetro de flujo y se basa en la medida de emisiones de luz provenientes de anticuerpos fluorescentes que son unidos a partículas virales específicas. Con este dispositivo se puede conseguir un LOD de 3,834 copias mL-1 para el SARS-CoV-2 en saliva. El FVR ha sido validado clínicamente usando 54 muestras de saliva en un test a ciegas, con una sensibilidad y especificidad del 91.2% y 90%, respectivamente. Estos hallazgos sugieren que el FVR tiene el potencial de ser una alternativa viable a los métodos de diagnóstico actuales en escenarios de pandemias, pues es rápido (< 30 min) y menos costoso que los test por PCR, mientras que es más sensible que los actuales test de antígenos para COVID-19. Las tecnologías de detección fotónicas desarrolladas en esta tesis muestran un potencial significativo para su uso en un amplio rango de aplicaciones, incluyendo: -contaminación de aire por partículas, causantes de problemas cardiovasculares y respiratorios -contaminación de agua por partículas, el cual afecta a ecosistemas como ríos, lagos y océanos -recuento total de bacterias en aguas de baño o ambientales -pandemias víricas. Estas tecnologías son particularmente atractivas en países con recursos limitados, dado sus simples diseños, portabilidad, el poco tiempo de espera para obtener resultados y asequibilidad, así como el hecho de que estos no requieren un laboratorio especializado o un personal cualificado para operar con ellas.Postprint (published version