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Any machining operation induces significant deformation and associated stress states within the component being machined. Once the component has been finished and is removed from the machining tool, a portion of these stresses remain within the finished component, and are termed residual stresses. These stresses have a significant effect upon the performance of the final component. However, despite their importance there is no accurate and cost effective method for measuring residual stresses. For this reason predicting these stresses without the need for measurement is highly desirable. The focus of this thesis is on advancing the development and implementation of finite element models aimed at predicting residual stresses induced by metal cutting operations. There are three main focus areas within this research, the first of which is concerned with predicting residual stresses when small feed rates are used. It is shown that in the existing cutting models residual stress prediction accuracy suffers when feed rates are small. A sequential cut module is developed, which greatly increases the accuracy of the predicted residual stress depth profiles. A second area of focus concerns the influence of friction models on predicted residual stresses. A detailed set of simulations is used to elucidate the effect of friction not only for sharp tools, but also for tools which have accrued wear. It is shown that whilst friction is not of critical importance for new tools, as tools continue to wear the choice of friction model becomes significantly more important. The third area of focus is on phase transformations, induced by the cutting process. A decoupled phase transformation module is developed in order to predict the depth, if any, of a phase transformed layer beneath the newly machined surface. Furthermore, the effect of this layer on the residual stress depth profile was also studied. All three focus areas present new and novel contributions to the field of metal cutting simulations, and serve to significantly increase the capabilities of predictive models for machining. / Thesis / Doctor of Philosophy (PhD)

A previsão e entendimento da formação de tensões residuais oriundas da têmpera plena ou superficial nos aços tem sido objeto de estudo por um número considerável de pesquisadores, devido ao seu grande interesse tecnológico. Neste trabalho o objetivo principal consistiu no desenvolvimento de um modelo numérico para a previsão das tensões residuais em peças cilíndricas temperadas por indução. A têmpera por indução executada num componente previamente \"beneficiado\", isto é, temperado e revenido, é capaz de produzir uma camada superficial de alta dureza e tensões compressivas num núcleo resistente e tenaz. É esperado, entretanto, que o aumento da camada endurecida possa gerar tensões trativas indesejáveis abaixo da superfície, comprometendo a integridade estrutural do componente e levando as falhas prematuras. Portanto, neste trabalho ênfase foi dada no estudo da influência da profundidade de camada induzida no perfil e magnitude das tensões residuais em corpos de prova cilíndricos (c.ps.) de aço. O método de elementos finitos foi adotado para a solução do problema térmico (distribuição de temperatura) e estrutural (cálculo das tensões) com o emprego do programa ANSYS 5.3. Os efeitos metalúrgicos da mudança de fase Austenita - Martensita, responsável pelas altas tensões compressivas residuais na superfície, como resultado da expansão volumétrica inerente a esta transformação, foi modelado através de uma rotina FORTRAN especialmente desenvolvida neste trabalho e acoplada ao programa ANSYS 5.3. A criação da geometria do modelo e passos da solução foram automatizados através do uso da linguagem paramétrica APDL (ANSYS Parametric Design Language) do programa ANSYS 5.3. Trata-se de um problema termo-elasto-plástico onde as propriedades termo-físicas e mecânicas necessárias para o cálculo foram consideradas dependente da temperatura. Verificação e calibração do modelo computacional foi efetuada através da medição das ) tensões residuais em c.ps. cilíndricos de aço com o emprego da técnica de difração de raios X. Finalmente, são discutidos aspectos de precisão e principais fontes de erro, como também proposta de melhorias e futuras aplicações deste modelo. / Large efforts have been made to predict and understand the residual stresses formation in through hardening and case hardening of steel. In the present work the objective was to develop a model to predict the residual stresses in induction hardening of cylindrical steel bars. Induction hardening, i.e. electromagnetic heating and subsequent quenching, is a surface treatment of great use in industry because it is suitable to improve locally mechanical properties of the high stressed regions of the part. A hard surface layer with high compressive residual stress is normally obtained, while retaining ductility and toughness in the core. Applied to components that undergo severe duty cycles, such as gears, shafts axles and bearings, it is also clean, fast and suitable for on-line applications. It is expected however, that increasing the hardening depth leads to undesirable high subsurface tensile stress, which can cause premature failure of the component. Emphasis has been on investigating the influence of case depth on the residual stress distribution of the cylindrical steel specimens. A finite element model was developed to compute the temperature history, phase transformation and residual stress for the induction hardening process. The Austenite to Martensite phase transformation during quenching, responsible for the high surface compressive stress, as result of volume expansion, was evaluated by a custom FORTRAN routine linked to the ANSYS5.3 FEA code. The model geometry and solution process were automated by the use of ANSYS Parametric Design Language (APDL). The thermoelastoplastic behavior of the material was studied, considering material properties temperature dependent. The results of the calculations have been compared to experimental measurements of the residual stresses at the surface, using X Ray diffraction technique. Finally, accuracy and main source of erros are discussed. Future improvements and applications of this model are proposed.

A previsão e entendimento da formação de tensões residuais oriundas da têmpera plena ou superficial nos aços tem sido objeto de estudo por um número considerável de pesquisadores, devido ao seu grande interesse tecnológico. Neste trabalho o objetivo principal consistiu no desenvolvimento de um modelo numérico para a previsão das tensões residuais em peças cilíndricas temperadas por indução. A têmpera por indução executada num componente previamente \"beneficiado\", isto é, temperado e revenido, é capaz de produzir uma camada superficial de alta dureza e tensões compressivas num núcleo resistente e tenaz. É esperado, entretanto, que o aumento da camada endurecida possa gerar tensões trativas indesejáveis abaixo da superfície, comprometendo a integridade estrutural do componente e levando as falhas prematuras. Portanto, neste trabalho ênfase foi dada no estudo da influência da profundidade de camada induzida no perfil e magnitude das tensões residuais em corpos de prova cilíndricos (c.ps.) de aço. O método de elementos finitos foi adotado para a solução do problema térmico (distribuição de temperatura) e estrutural (cálculo das tensões) com o emprego do programa ANSYS 5.3. Os efeitos metalúrgicos da mudança de fase Austenita - Martensita, responsável pelas altas tensões compressivas residuais na superfície, como resultado da expansão volumétrica inerente a esta transformação, foi modelado através de uma rotina FORTRAN especialmente desenvolvida neste trabalho e acoplada ao programa ANSYS 5.3. A criação da geometria do modelo e passos da solução foram automatizados através do uso da linguagem paramétrica APDL (ANSYS Parametric Design Language) do programa ANSYS 5.3. Trata-se de um problema termo-elasto-plástico onde as propriedades termo-físicas e mecânicas necessárias para o cálculo foram consideradas dependente da temperatura. Verificação e calibração do modelo computacional foi efetuada através da medição das ) tensões residuais em c.ps. cilíndricos de aço com o emprego da técnica de difração de raios X. Finalmente, são discutidos aspectos de precisão e principais fontes de erro, como também proposta de melhorias e futuras aplicações deste modelo. / Large efforts have been made to predict and understand the residual stresses formation in through hardening and case hardening of steel. In the present work the objective was to develop a model to predict the residual stresses in induction hardening of cylindrical steel bars. Induction hardening, i.e. electromagnetic heating and subsequent quenching, is a surface treatment of great use in industry because it is suitable to improve locally mechanical properties of the high stressed regions of the part. A hard surface layer with high compressive residual stress is normally obtained, while retaining ductility and toughness in the core. Applied to components that undergo severe duty cycles, such as gears, shafts axles and bearings, it is also clean, fast and suitable for on-line applications. It is expected however, that increasing the hardening depth leads to undesirable high subsurface tensile stress, which can cause premature failure of the component. Emphasis has been on investigating the influence of case depth on the residual stress distribution of the cylindrical steel specimens. A finite element model was developed to compute the temperature history, phase transformation and residual stress for the induction hardening process. The Austenite to Martensite phase transformation during quenching, responsible for the high surface compressive stress, as result of volume expansion, was evaluated by a custom FORTRAN routine linked to the ANSYS5.3 FEA code. The model geometry and solution process were automated by the use of ANSYS Parametric Design Language (APDL). The thermoelastoplastic behavior of the material was studied, considering material properties temperature dependent. The results of the calculations have been compared to experimental measurements of the residual stresses at the surface, using X Ray diffraction technique. Finally, accuracy and main source of erros are discussed. Future improvements and applications of this model are proposed.

The dissertation focuses on estimating the uncertainty associated with stress/strain prediction procedures from dynamic test data used in turbine blade analysis. An accurate prediction of the maximum response levels for physical components during in-field operating conditions is essential for evaluating their performance and life characteristics, as well as for investigating how their behavior critically impacts system design and reliability assessment. Currently, stress/strain inference for a dynamic system is based on the combination of experimental data and results from the analytical/numerical model of the component under consideration. Both modeling challenges and testing limitations, however, contribute to the introduction of various sources of uncertainty within the given estimation procedure, and lead ultimately to diminished accuracy and reduced confidence in the predicted response. The objective of this work is to characterize the uncertainties present in the current response estimation process and provide a means to assess them quantitatively. More specifically, proposed in this research is a statistical methodology based on a Bayesian-network representation of the modeling process which allows for a statistically rigorous synthesis of modeling assumptions and information from experimental data. Such a framework addresses the problem of multi-directional uncertainty propagation, where standard techniques for unidirectional propagation from inputs' uncertainty to outputs' variability are not suited. Furthermore, it allows for the inclusion within the analysis of newly available test data that can provide indirect evidence on the parameters of the structure's analytical model, as well as lead to a reduction of the residual uncertainty in the estimated quantities. As part of this work, key uncertainty sources (i.e., material and geometric properties, sensor measurement and placement, as well as noise due data processing limitations) are investigated, and their impact upon the system response estimates is assessed through sensitivity studies. The results are utilized for the identification of the most significant contributors to uncertainty to be modeled within the developed Bayesian inference scheme. Simulated experimentation, statistically equivalent to specified real tests, is also constructed to generate the data necessary to build the appropriate Bayesian network, which is then infused with actual experimental information for the purpose of explaining the uncertainty embedded in the response predictions and quantifying their inherent accuracy.

[pt] A ocorrência de eventos de perfuração em poços de óleo e gás é frequente, porém indesejada, uma vez que acarreta aumento no tempo e custo das operações. Sua ocorrência pode levar à perda de circulação do fluido, desmoronamento da parede do poço, restrição da sua funcionalidade, danos à plataforma, problemas ambientais e mesmo a perda de vidas humanas. Desta forma, o conhecimento das tensões é essencial para a estimativa da estabilidade do poço e aumento da segurança da perfuração. Existem diversos métodos para prever a tensão horizontal mínima, entretanto, para a máxima, ainda há dificuldades na sua determinação. No caso de poços inclinados, este cálculo se torna ainda mais complexo, uma vez que as tensões regionais não são alinhadas com o eixo do poço. Identificada essa necessidade, e partindo do princípio de que ao menos um poço já tenha sido perfurado em um campo e que este apresentou instabilidades do tipo colapso ou fraturamento induzido, esta dissertação trabalha na implementação de um algoritmo capaz de obter a magnitude da tensão horizontal máxima que levou ao surgimento dos mesmos. As fraturas Induzidas são falhas de tração, enquanto os Breakouts são gerados pelo colapso da parede do poço por falhas de compressão. Ambos devem ser devidamente identificados a partir de perfis de imagem. Os algoritmos implementados envolvem solução analítica, calculada através de métodos implícito e explícito. O procedimento para a rotação das tensões para poços inclinados é baseado em formulação presente na literatura, e os cálculos das tensões ao redor da parede do poço, nas equações de Kirsch. O fraturamento do material é avaliado de acordo com a tensão de cut-off, e o de ruptura, a partir do critério de Mohr-Coulomb ou Lade-Ewy. Os métodos desenvolvidos são comparados entre si, relatando suas vantagens e desvantagens. Aplicando uma vasta variação de inclinação e azimute do poço, os resultados alcançados apresentam uma diferença pequena com relação aos obtidos por meio de pacotes comerciais, sendo considerados satisfatórios. / [en] The occurrence of oil and gas well drilling events is common, but undesirable, once it increases time and cost of operations. Its occurence can lead to loss of fluid circulation, wellbore wall collapse, restriction of its functionality, platform damage, environmental issues and even the lost of human lives. stresses knowledge is essential to wellbore stability estimation and to the improvement of drilling safety. In fact, there are some methods and tests to compute the minimum horizontal stress, but there still exist difficulties on maximum horizontal stress determination. In the case of deviated wells, the calculations are even more complexes due to the lack of alignment between the regional stresses and well axis. Identified that problem, and considering that at least one wellbore has been drilled in a field and presented compressive or tensile instabilities, this dissertation works on the implementation of algorithms capable of obtaining the maximum horizontal stress magnitude that lead the appearance of breakout or induced fracture. The instability problems are identified and previously interpreted through image logs. The implemented algorithms involve an analytical solution, which is calculated by an explicit method and an implicit one, to the stated problem. The procedure to the stresses rotation on deviated wells used at this work is based on the formulation presented on technical literature, and the calculation of the stresses around the wellbore wall, on Kirsch s equation. The failure of the material is evaluated using a tensile cutoff for induced fractures and Mohr-Coulomb or Lade-Ewy criteria for breakouts. The developed methods are compared between each other, presenting the advantages and disadvantages of then. The results, applying a large variation on the wellbore inclination and azimuth, show that the values accomplished a low difference in relation to the obtained by using a commercial software, being considered as satisfactory.

Additive manufacturing (AM) technology is a key emerging field transforming how customized products with complex shapes are manufactured. AM is the process of layering materials to produce objects from three-dimensional (3D) models. AM technology can be used to print objects with complicated geometries and a broad range of material properties. However, the issue of ensuring the quality of printed products during the process remains an obstacle to industry-level adoption. Furthermore, the characteristics of AM processes typically involve complex process dynamics and interactions between machine parameters and desired qualities. The issues associated with quality assurance in AM processes underscore the need for research into smart quality assurance systems. To study the complex physics behind process interaction challenges in AM processes, this dissertation proposes the development of a data-driven smart quality assurance framework that incorporates in-process sensing and machine learning-based modeling by correlating the relationships among parameters, signatures, and quality. High-fidelity AM simulation data and the increasing use of sensors in AM processes help simulate and monitor the occurrence of defects during a process and open doors for data-driven approaches such as machine learning to make inferences about quality and predict possible failure consequences. To address the research gaps associated with quality assurance for AM processes, this dissertation proposes several data-driven approaches based on the design of experiments (DoE), forward prediction modeling, and an inverse design methodology. The proposed approaches were validated for AM processes such as fused filament fabrication (FFF) using polymer and hydrogel materials and laser powder bed fusion (LPBF) using common metal materials. The following three novel smart quality assurance systems based on a parameter–signature–quality (PSQ) framework are proposed: 1. A customized in-process sensing platform with a DOE-based process optimization approach was proposed to learn and optimize the relationships among process parameters, process signatures, and parts quality during bioprinting processes. This approach was applied to layer porosity quantification and quality assurance for polymer and hydrogel scaffold printing using an FFF process. 2. A data-driven surrogate model that can be informed using high-fidelity physical-based modeling was proposed to develop a parameter–signature–quality framework for the forward prediction problem of estimating the quality of metal additive-printed parts. The framework was applied to residual stress prediction for metal parts based on process parameters and thermal history with reheating effects simulated for the LPBF process. 3. Deep-ensemble-based neural networks with active learning for predicting and recommending a set of optimal process parameter values were developed to optimize optimal process parameter values for achieving the inverse design of desired mechanical responses of final built parts in metal AM processes with fewer training samples. The methodology was applied to metal AM process simulation in which the optimal process parameter values of multiple desired mechanical responses are recommended based on a smaller number of simulation samples. / Doctor of Philosophy / Additive manufacturing (AM) is the process of layering materials to produce objects from three-dimensional (3D) models. AM technology can be used to print objects with complicated geometries and a broad range of material properties. However, the issue of ensuring the quality of printed products during the process remains a challenge to industry-level adoption. Furthermore, the characteristics of AM processes typically involve complex process dynamics and interactions between machine parameters and the desired quality. The issues associated with quality assurance in AM processes underscore the need for research into smart quality assurance systems. To study the complex physics behind process interaction challenges in AM processes, this dissertation proposes a data-driven smart quality assurance framework that incorporates in-process sensing and machine-learning-based modeling by correlating the relationships among process parameters, sensor signatures, and parts quality. Several data-driven approaches based on the design of experiments (DoE), forward prediction modeling, and an inverse design methodology are proposed to address the research gaps associated with implementing a smart quality assurance system for AM processes. The proposed parameter–signature–quality (PSQ) framework was validated using bioprinting and metal AM processes for printing with polymer, hydrogel, and metal materials.