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.

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.