UVicSpace : Browsing by Supervisor "Ahmadi, Keivan"

Browsing by Supervisor "Ahmadi, Keivan"

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3D finite element model for predicting cutting forces in machining unidirectional carbon fiber reinforced polymer (CFRP) composites
(2019-01-04) Salehi, Amir Salar; Jun, Martin Byung-Guk; Ahmadi, Keivan
Excellent properties of Carbon Fiber Reinforced Polymer (CFRP) composites are usually obtained in the direction at which carbon fibers are embedded in the polymeric matrix material. The outstanding properties of this material such as high strength to weight ratio, high stiffness and high resistance to corrosion can be tailored to meet specific design applications. Despite their excellent mechanical properties, application of CFRPs has been limited to more lucrative sectors such as aerospace and automotive industries. This is mainly due to the high costs involved in manufacturing of this material. Machining, milling and drilling, is a critical part of finishing stage of manufacturing process. Milling and drilling of CFRP is complicated due to the inhomogeneous nature of the material and extreme abrasiveness of carbon fibers. This is why CFRP parts are usually made near net shape. However, no matter how close they are produced to the final shape, there still is an inevitable need for some post machining to obtain dimensional accuracies and tolerances. Problems such as fiber-matrix debonding, subsurface damage, rapid tool wear, matrix cracking, fiber pull-out, and delamination are usually expected to occur in machining CFRPs. These problems can affect the dimensional accuracy and performance of the CFRP part in its future application. To improve the efficiency of the machining processes, i.e. to reduce the costs and increase the surface quality, researchers began studying machining Fiber Reinforced Polymer (FRP) composites. Studies into FRPs can be divided in three realms; analytical, experimental and numerical. Analytical models are only good for a limited range [0° – 75°] of Fiber Orientations , to be found from now on as “FO” in this thesis. Experimental studies are expensive and time consuming. Also, a wide variety of controlling parameters exist in an experimental machining study; including cutting parameters such as depth of cut, cutting speed, FO, spindle speed, feed rate as well as tool geometry parameters such as rake angle, clearance angle, and tool edge/nose radius. Furthermore, the powdery dust created during machining is known to cause serious health hazards for the operator. Numerical models, on the other hand, offer the unique capability of studying the complex interaction between the tool and workpiece as well as chip formation mechanisms during the cut. Large number of contributing parameters can be included in the numerical model without wasting material. Three main objectives of numerical models are to predict principal cutting force, thrust force and post-machining subsurface damage. Knowing these, one can work on optimization of machining process by tool geometry and path design. Previous numerical studies mainly focus on the orthogonal cutting of FRP composites. Thus, the existing models in the literature are two-dimensional (2D) for the most part. The 2D finite element models assume plain stress or strain condition. Accordingly, the reported results cannot be reliable and extendable to real cutting situations such as drilling and milling, where oblique cutting of the material occurs. Most of the numerical studies to date claim to predict the principle cutting forces fairly acceptable, yet not for the whole range of fiber orientations. Predicted thrust forces, on the other hand, are generally not in good agreement with experimental results at all. Subsurface damage is reported by some experimental studies and again only for a limited FO range. To address the lack of reliable force and subsurface damage prediction model for the whole FO range, this thesis aims to develop a 3D finite element model, in hope of capturing out-of-plane displacements during stress formation in different material phases (Fiber, Matrix and the Interface bonding). ABAQUS software was chosen as the most commonly used finite element simulation tool in the literature. In present work a user-defined material subroutine (VUMAT) is developed to simulate behavior of carbon fibers during the cut. Carbon fibers are assumed to behave transversely isotropic with brittle (perfectly elastic) fracture. Epoxy matrix is simulated with elasto-plastic behavior. Ductile and shear damage models are also incorporated for the matrix. Surface-based cohesive zone technique in ABAQUS is used to simulate the behavior of the zero-thickness bonding layer. The tool is modeled as a rigid body. Mechanical properties were extracted from the literature. The obtained numerical results are compared to the experimental and numerical data in literature. The model is capable of capturing principal forces very well. Cutting force increases with FO from zero to 45° and then decreases up to 135°. The simulated thrust forces are still underestimated mainly due to the fiber elastic recovery effect. Also, the developed 3D model is shown to capture the subsurface damage generally by means of a predefined dimensionless state variable called, Contact Damage (CSDMG). This variable varies between zero to one. It is stored at each time step and can be called out at the end of the analysis. It was shown that depth of fiber-matrix debonding increases with increase in FO.
Chatter vibrations in robotic milling considering structural nonlinearity
(2022-09-08) Mohammadi, Yaser; Ahmadi, Keivan
The application of robotic manipulators in machining systems has gained a great interest in manufacturing because of their lower prices, higher kinematic flexibility and larger workspace compared to conventional CNC machine tools. However, their performance is limited due to the much lower structural rigidity which makes them more susceptible to excessive and unstable vibrations, known as chatter, during the machining process. Highly effective chatter modeling and avoidance methods that have been developed for CNC machining in the past decades are now being used by the industry to design high-performance chatter-free machining operations. The available methods, however, face major difficulties when applied to robotic machining, mainly due to the high flexibility and pose-dependency of the vibration response in robots. High flexibility leads to high-amplitude vibrations which affect the process dynamics and excite structural nonlinearities. The existing approaches to modeling machining vibrations assume linearity of the structural dynamics of the robotic manipulator. This assumption, considering the inherent nonlinearities in the robot’s revolute joints, may cause considerable inaccuracies in predicting the stability of vibrations during the process. This thesis studies the high flexibility and nonlinearity of the robot’s structural dynamics and their effects on chatter vibrations. The research starts with investigating the effects of high flexibility of robot's structure in the process dyamics by considering the modulation of cutting forces by axial vibrations, which is normally ignored in CNC milling due to high rigidity of the machine in this direction. The results of chatter prediction considering this effect are shown and discussed. The rest of the thesis focuses on the structural nonlinearity. Firstly, an experimental study is presented to investigate the extent of nonlinearity in structural dynamics of the robot. The results confirm that structural nonlinearities in robotic machining systems can be effectively excited in the presence of high-amplitude vibrations due to milling forces, such that they cause remarkable differences in chatter prediction. The following step is modeling the structural nonlinearities. For this purpose, the variation of restoring forces with the dynamic response (displacement and velocity) are observed when the robot is subjected to harmonic excitation. Based on the experimental observations, the nonlinear effects are modeled by cubic stiffness and damping characteristics. Parameters of the nonlinear model are then identified using Higher-order Frequency Response Functions (HFRF) extracted from measurements. The identified model can predict the vibration behavior of the robotic machining system when subjected to periodic loads such as milling forces. The developed model of nonlinear structural dynamics is then coupled with the chatter model. Consequently, the system is described by nonlinear Delay Differential Equations (DDE) with periodic coefficients. Bifurcation diagrams for the forced vibrations in the described system are developed using the numerical continuation method. The effects of cutting parameters such as feedrate as well as the nonlinear parameters are studied. The thesis is concluded by proposing the use of in-process FRF in the linear model of chatter stability for quick prediction of stability limits. In this approach, the exact characteristics of the nonlinear mechanisms are not studied; instead, the measured FRF during the milling process are used, which are assumed to represent the nonlinear structural dynamics that are linearized about the applied operational conditions. Two methods of measuring in-process FRF are proposed and employed in the robotic milling system. The measured FRF are then used in the linear chatter model to develop the Stability Lobes Diagram (SLD) which shows the combination of cutting parameters that lead to stable or unstable vibrations. Experimental chatter tests show that better agreement with predictions can be achieved by using in-process FRF instead of FRF measured at the idle state of the system. The results of this thesis contribute to better characterization of vibrations in robotic machining with high-amplitude forces and selecting suitable strategies to enhance productivity of the operation.
Estimating machining forces from vibration measurements
(2019-12-11) Joddar, Manish Kumar; Ahmadi, Keivan; Nadler, Ben
The topic of force reconstruction has been studied quite extensively but most of the existing research work that has been done are in the domain of structural and civil engineering construction like bridges and beams. Considerable work in force reconstruction has also being done in fabrication of machines and structures like aircrafts, gear boxes etc. The topic of force reconstruction of the cutting forces during a machining process like turning or milling machines is a recent line of research to suffice the requirement of proactive monitoring of forces generated during the operation of the machine tool. The forces causing vibrations while machining if detected and monitored can enhance system productivity and efficiency of the process. The objective of this study was to investigate the algorithms available in literature for inverse force reconstruction and apply for reconstruction of cutting forces while machining on a computer numerically controlled (CNC) machine. This study has applied inverse force reconstruction technique algorithms 1) Deconvolution method, 2) Kalman filter recursive least square and 3) augmented Kalman filter for inverse reconstruction of forces for multi degree of freedom systems. Results from experiments conducted as part of this thesis work shows the effectiveness of the methods of force reconstruction to monitor the forces generated during the machining process on machine tools in real time without employing dynamometers which are expensive and complex to set-up. This study for developing a cost-effective method of force reconstruction will be instrumental in applications for improving machining efficiency and proactive preventive maintenance.
Estimation of Cutting Forces in Vibration Assisted Drilling System Using Augmented Kalman Filter
(2022-05-04) Nadeem, Kashif; Ahmadi, Keivan
Vibration assisted drilling (VAD) is a type of machining process in which high-frequency vibrations with a small amplitude are induced in the cutting tool to improve the cutting process of hard and brittle materials. These vibrations create an unsteady repetitive processing effect which eventually reduce the cutting forces. It is also crucial to measure these forces in some way because their knowledge directly aids in determining the best machining parameters. Direct and indirect methods can be used to measure these forces, but due to serious limitations of direct measurement methods, an indirect measurement method is required which is capable of online monitoring of high-frequency cutting forces. In this thesis, an indirect method is proposed to estimate thrust force and torque from the voltage signal generated by piezoelectric sensor and torsional deflection signal measured through piezoelectric accelerometer. The estimation of two input signals requires a multi-input multi-output (MIMO) model of VAD system which is developed using Receptance Coupling and Substructure Analysis (RCSA) method. Experimental and numerical methods are used to validate the constituent single-input single-output (SISO) transfer functions of the MIMO model. As the estimated forces are distorted by the dynamics of VAD structure, a Kalman Filter is employed to compensate the dynamics. The accuracy and similarity of results is determined by comparing the estimated cutting force values with the force measured from a load cell in time and frequency domain. The reported experimental results confirm the possibility of using Kalman Filter in estimating high-frequency forces generated in VAD process.
Experimental model for predicting cutting forces in machining carbon fiber reinforced polymer composites
(2019-05-15) Ahmadian, Amirali; Ahmadi, Keivan
The demand for materials with high mechanical performances such as Carbon Fiber Reinforced Plastics (CFRP) is increasing. However, there are major challenges in machining CFRP as it involves delamination, fiber pullouts, and extreme cutting tool wear. Analysis of chip formation mechanisms and prediction of associated cutting forces in CFRP machining enables one to address these challenges. This study proposes a mechanistic cutting force model for milling operations of the CFRP workpiece, considering its non-homogeneity and anisotropy, by taking into account variations of fiber cutting angle during machining. A mechanistic model of cutting force constants is obtained from a number of experimentally measured unidirectional CFRP milling forces. The obtained mechanistic force model predictions are verified against experimentally measured milling forces with arbitrary tool path indicating the accuracy of the proposed mechanistic model in predicting cutting forces. The proposed mechanistic cutting force model is capable of being integrated into the manufacturing process to allow optimized machining of quality certified CFRP work-pieces.
Measurement of High-Frequency Milling Forces with Dynamic Compensation
(2022-04-28) Jullien-Corrigan, Alan; Ahmadi, Keivan
Piezoelectric dynamometers are widely used to measure cutting forces during milling operations for diagnostic, process monitoring, and research and development purposes. However, the bandwidth of tooth passing frequencies that can be measured has an upper limit due to the electromechanical dynamics of the measurement device. As a result, high-frequency forces cannot be accurately measured. Even if an effort is made to match the cutting conditions to the specifications of the dynamometer, the higher harmonics of the tooth-passing frequency are still affected so that the resulting measurements are distorted. In this work, two new (for milling applications) methods are presented to reconstruct the machining forces from the distorted measurement signal and compared to an existing method, the Augmented Kalman Filter (AKF). The first method implements a Sliding Mode Observer (SMO) to estimate the machining forces at each time-step from the measured signal. The second method, referred to as Regularized Deconvolution (RD), considers the convolution sum of the input machining force and the impulse response of the system, and then reconstructs the machining force signal by regularizing a related inverse problem. All three methods are implemented in a simulation study that imitates the cutting conditions used in a latter experimental cutting test in which the above methods are again used to recover the true machining forces and their relative performance evaluated and compared. A transfer function model of the electomechanical dynamics of a Kistler dynamometer is identified and incorporated into the simulation study and the experiment. The results of this work find that, while all three methods reconstruct the true machining forces reasonably well, SMO has clear advantages for processes carried out over time in which the system dynamics changes. AKF also performs better than RD, but is not robust against variations in system dynamics. Despite its drawbacks, RD does have the advantage of being the method that only requires one parameter to be tuned, whereas the other methods require the tuning of two or more parameters.
Model Parameter Identification for Feed Drive Dynamics using Kernel-Based Methods
(2024) McPherson, J.D.; Ahmadi, Keivan
This thesis presents an application of kernel-based methods for identification of the feed drive's dynamics model parameters and prediction of the disturbances affecting feed drives during operation from the cutting forces. To this purpose, the Partially Linear - Least Squares Support Vector Machine (PL-LSSVM) and Kernel Recursive Least Squares - Tracker (KRLS-T) algorithms were utilised for batch identification and online prediction of disturbances. Experimental case studies were performed with two ball screw feed drives under simulated and real cutting conditions to verify the identification and in-operation prediction of cutting forces.
Modelling forces in milling screw rotors
(2022-09-13) Wang, Xi; Ahmadi, Keivan
The deflections of screw rotors under machining forces cause mismatch between the male and female rotors and, consequently, accelerated wear and suboptimal efficiency in their performance. Optimizing the machining process to minimize the generated forces and accounting for the resulting mismatch in the design of the rotor profile requires accurately computing the machining forces in computer simulations. Virtual machining systems combine graphics-based computation of the Cutter-Workpiece Engagement (CWE) with the physics-based models of machining mechanics to simulate the forces during complex machining processes. However, because of the high computational load of graphical simulations, virtual machining is not suitable for the repetitive force simulations that are required for optimizing the design and manufacturing of rotors. In this work, we present a new method that simulates screw milling forces based on the process kinematics instead of graphical simulations. Utilizing mathematical equations that describe the process kinematics, the theoretical rotor profile is determined for feasible combinations of cutting tool profile, setup angle, and centre distance. Subsequently, to find the milling forces, the cutting edge is discretized into multiple small edge segments and a mechanistic cutting force model is used to determine the local cutting forces at each segment. After geometric and kinematic transformations of these local forces, the screw milling forces are obtained for each roughing and finishing pass. Instead of graphics-based methods, the engagement conditions between the cutter and workpiece are determined by the ensemble of 2D rotor and tool profiles; as a result, the computational efficiency is increased substantially. The semi-analytical nature of the presented method allows for computing the forces with arbitrary resolution within a reasonable time. The accuracy and efficiency of the presented method is verified by comparing the simulated forces against a dexel-based virtual machining system.
Modelling the dynamics of vibration assisted drilling systems using substructure analysis
(2020-06-28) Ostad Ali Akbari, Vahid; Ahmadi, Keivan
Vibration Assisted Machining (VAM) refers to a non-conventional machining process where high-frequency micro-scale vibrations are deliberately superimposed on the motion of the cutting tool during the machining process. The periodic separation of the tool and workpiece material, as a result of the added vibrations, leads to numerous advantages such as reduced machining forces, reduction of damages to the material, extended tool life, and enabling the machining of brittle materials. Vibration Assisted Drilling (VAD) is the application of VAM in drilling processes. The added vibrations in the VAD process are usually generated by incorporating piezoelectric transducers in the structure of the toolholder. In order to increase the benefits of the added vibrations on the machining quality, the structural dynamics of the VAD toolholder and its coupling with the dynamics of the piezoelectric transducer must be optimized to maximize the portion of the electrical energy that is converted to mechanical vibrations at the cutting edge of the drilling tool. The overall dynamic performance of the VAD system depends of the dynamics of its individual components including the drill bit, concentrator, piezoelectric transducer, and back mass. In this thesis, a substructure coupling analysis platform is developed to study the structural dynamics of the VAD system when adjustments are made to its individual components. In addition, the stiffness and damping in the joints between the components of the VAD toolholder are modelled and their parameters are identified experimentally. The developed substructure coupling analysis method is used for structural modification of the VAD system after it is manufactured. The proposed structural modification approach can be used to fine-tune the dynamics of the VAD system to maximize its dynamic performance under various operational conditions. The accuracy of the presented substructure coupling method in modeling the dynamics of the VAD system and the effectiveness of the proposed structural modification method are verified using numerical and experimental case studies.
Numerical modelling and metallurgical characterization of Cr-Mo steels processed by directed energy deposition
(2021-07-09) Cooke, Shaun; Ahmadi, Keivan; Herring, Rodney A.
Additive manufacturing (AM) provides unique opportunities to push the boundaries of material properties and free form fabrication. However with this novel manufacturing technique a number of defects not commonly found in conventional processes such as machining or casting can arise. Both experimental and numerical studies can help better understand the printed material on a more fundamental level in order to optimize the process and mitigate these defects. Electron microscopy can provide essential information about the as-built microstructure and characteristic defects while numerical modelling can help determine a correlation between process parameters and the resulting properties. First, an initial investigation of directed energy deposition (DED) processed 4140 steel was conducted using various microscopy methods to better understand the defects and microstructure of the printed alloy. A martensite dominate microstructure within a bainitic matrix with increasing degrees of tempering further down the build was revealed. Additional sample preparation was conducted with a focused ion beam and analyzed with the transmission electron microscope to investigate features such as grain boundaries, mechanical twins and interplanar spacing. This interplanar spacing was measured for a number of different diffraction images and compared with the theoretical values. The deviation between the measured and theoretical values can be attributed to defects such as residual stress which causes lattice strain and consequently a smaller or larger spacing between atomic planes. Lastly, diffraction images were characterized and compared with the literature to determine the Miller indices and the specific zone axis orientations. A thermo-mechanical-metallurgical finite element model for 42CrMo4 steel was then developed in ABAQUS to identify the correlation between processing parameters and resulting properties by predicting the temperature history, and resulting residual stresses and metallurgical phase fractions for the DED process. A pre-processing framework was implemented in order to allow the modelling of complex geometries and laser trajectories while experiments were conducted to validate the fidelity of the model. Four separate cases were fabricated with varying processing parameters and geometries. In addition to in-situ temperature measurements, post-build residual stress and substrate distortion data was also collected. Furthermore, metallurgical analysis was performed for each case and compared with the simulated phase fractions. The accuracy of the distortion profile increased with increasing dwell time while the accuracy in predicting the metallurgical phase fractions and residual stresses demonstrated the opposite trend.
Operational modal analysis for chatter prediction in milling
(2024) Zorlu, Ayberk; Ahmadi, Keivan
Unstable vibrations during machining can harm both the tool and the workpiece,requiring careful selection of process parameters to avoid them. These parameters are usually set based on vibration models of the machining process. However, due to unmodeled dynamics or process variations, chatter can still occur, highlighting the need for online chatter monitoring systems. Existing methods often detect chatter only after it occurs, so there is a need for monitoring systems that can predict chatter before it occurs to ensure high-quality machining. This thesis presents a new method to identify the dynamics of regenerative chatter from the measured process vibrations in milling. This method combines the synchronous onceper- revolution sampling of stable process vibrations with Operational Modal Analysis to estimate the Floquet multipliers of the delayed linear time-periodic dynamics in milling, all from the natural process vibrations without external excitation. The identified multipliers quantify vibration stability, enabling chatter prediction before it occurs. Additionally, they can be used to calibrate physics-based chatter models based on vibration measurements solely within the stable region. The method’s accuracy in identifying Floquet multipliers is validated through extensive numerical simulations and two experimental case studies. The results show that chatter due to both Hopf and period-doubling bifurcations can be predicted from the process vibrations during stable cuts. Moreover, the experimental case studies demonstrate a vibration measurement system for implementing the presented method in standard milling operations and confirm its effectiveness in practice.
Parameter identification of mechanistic milling force model for carbon fiber-reinforced polymer composite parts
(2022-09-09) Farhadmanesh, Mehran; Ahmadi, Keivan
The application of Carbon Fiber-Reinforced Polymers (CFRP) has been recently increasing in a wide range of industries such as automotive and aerospace due to their excellent mechanical properties and high strength-to-weight ratio. Machining of CFRPs is required at the finishing stage of the manufacturing process before assembly. The occurrence of failure types including fiber pull-out, fiber breakage, and delamination causes rejection of the high-value-added composite part. This work is motivated by the need to better understand the fundamental machining processes for CFRP parts. Since delamination is correlated with the level of cutting forces, it is required to develop mechanistic force models for the prediction of milling forces under different machining parameters to avoid damage. In isotropic (e.g. metallic) materials, parameters of the mechanistic model are treated as constants that are identified using well-established experimental procedures. However, the mechanics of chip formation in milling CFRP varies continuously depending on the fiber cutting angle. To address this variation, a mechanistic model with parameters depending on the fiber cutting angle is proposed, and a new experimental method is presented to identify the parameters of the proposed model. The model parameters, also known as specific force coefficients (SFC), are assumed to be periodic functions of the fiber cutting angle, where the Fourier coefficients of the periodic function are identified from the milling forces measured during a set of milling operations at various feedrate and fiber orientations. The experimental analysis confirms the accuracy of this approach to predict cutting forces in CFRP milling. Unexpected changes in cutting conditions and tool wear during the cutting process add to the uncertainty of the conventional offline calibration approaches, causing the need for online identification methods to adaptively recalibrate the model parameters. In this work, two identification methods based on recursive least squares (RLS) and Kalman filter (KF) algorithms are presented. In the RLS method, the model parameters are identified by the recursive regression of the forces measured at discrete time steps. Runout parameters are measured accurately and modeled properly in the RLS algorithm in order to improve the performance of RLS. The initial immersion angle of the tool is also estimated in the first stage of the identification process before the algorithm starts recursively updating the unknown parameters of the force model in the second stage. In the KF approach, a state-space model and observer with constant stochastic dynamics are constructed. In addition to SFC, the runout forces could be also identified as harmonic functions in the state variables vector without prior knowledge of their values, which is one advantage of KF over RLS. The initial immersion angle is considered as an additional state variable in an extended Kalman filter (EKF) by linearization of the observation matrix in a one-stage identification process. Numerical simulations and experimental studies on milling UD-CFRP and MD-CFRP validate the performance of the presented methods. As a result, the identified model accurately predicts the machining forces, and therefore, can be used for process monitoring and optimization in the machining of metals and composite materials.
Posture dependent dynamics in robotic machining
(2019-05-15) Assadi, Hamed; Ahmadi, Keivan
Compared to conventional machine tools, industrial robots offer great advantages such as multitasking, larger workspace, and lower price. However, these advantages of robots are undermined by their high structural flexibility leading to excessive deflections, severe vibrations, and ultimately violating dimensional tolerances and poor surface finish. Modeling the dynamics of robots under machining (e.g. milling and drilling) forces is essential for reducing deflections and vibrations during the process. Although modeling the dynamics of traditional machining systems is a well-studied subject, the existing modeling approaches are not applicable to robotic manipulators because of the posture-dependent dynamics of industrial robots. Within this context, the presented thesis aims to predict the stability of vibrations during robotic machining operations through prediction of posture dependent dynamic behavior of robots. A rigid-body modeling approach is used to identify the dynamic parameters of the robotic manipulator based on least squares estimation method. Next, by adopting a rigid link flexible joint model and employing experimental modal analysis to identify the joint stiffness and damping parameters, posture dependent dynamic response prediction of the robot is achieved. Finally, the posture-dependent milling stability is presented as a function of the predicted tool center point transfer function, spindle speed, and axial depth of cut. A Staubli TX200 robot and a Kuka KR90 robot are used as experimental case studies.
Predicting regenerative chatter in turning using operational modal analysis
(2019-04-23) Kim, Sooyong; Ahmadi, Keivan; Jun, Martin Byung-Guk
Chatter, unstable vibration during machining, damages the tool and workpiece. A proper selection of spindle speed and depth of cut are required to prevent chatter during machining. Such proper cutting conditions are usually determined using vibration models of the machining process. Nonetheless, uncertainties in modeling or changes in dynamics during the machining operations can lead to unstable machining vibrations, and chatter may arise even when stable cutting conditions are used in the process planning stage. As a result, online chatter monitoring systems are key to ensuring chatter-free machining operations. Although various chatter monitoring systems are described in the literature, most of the existing methods are suitable for detecting chatter after vibrations become unstable. In order to prevent poor surface finish resulting from chatter marks during the finishing stages of machining, a new monitoring system that is capable of predicting the occurrence of chatter while vibrations are still stable is required. In this thesis, a new approach for predicting the loss of stability during stable turning operations is developed. The new method is based on the identification of the dynamics of self-excited vibrations during turning operations using Operational Modal Analysis (OMA). The numerical simulations and experimental results presented in this thesis confirm the possibility of using Operational Modal Analysis as an online chatter prediction method during stable machining operations.
Prediction of Orthogonal Cutting Forces Based on Multi-fidelity Modeling
(2023-04-26) Ahmadi, Keivan
Simplified analytical models of chip formation mechanics (e.g. the well-known Merchant’s model) are widely used to compute the machining forces in orthogonal cutting operations. The accuracy of analytical models, however, diminishes when the cutting edge has a rounded shape, known as edge (or hone) radius, which is common for most cutting tools. Finite element (FE) simulation can be used to obtain more accurate predictions of the forces in the presence of edge radius, but FE is computationally expensive because it should numerically solve a thermo-mechanical contact problem with nonlinear material properties to model the plastic deformation and damage of the workpiece. The high computational cost of FE simulations indeed becomes crucial when the force model is used for process optimization or for online simulations in the digital twin of the machining process. In this research, we present a computationally efficient data-driven model with acceptable accuracy when compared to the FE simulation. The presented model combines the predictions of FE simulations (i.e., high-fidelity dataset) and the predictions of the analytical model (i.e., low-fidelity dataset) and generates a new regression multi-fidelity model. The high-fidelity dataset is generated by an FE simulation in Abaqus and using Johnson-Cook constitutive equation to model the plastic deformation and damage of an aluminum workpiece during chip formation. The low-fidelity dataset is generated by Merchant’s analytical model. In both datasets, the inputs are the tool rake angle and uncut chip thickness, and the outputs are the cutting and feed forces. In total, 440 data points (40 high-fidelity points and 400 low-fidelity points) are generated. Based on this dataset, a multi-fidelity model is trained and tested through the emulator-embedded neural network (E2NN) method. The Root Mean Squared Error (RMSE) is then computed between the predictions of the trained model and the predictions from the FE simulation to quantify the performance of the presented multi-fidelity model. The results show a close agreement between the predictions of the high-fidelity and the multi-fidelity models. The computed RMSE was less than 8.5%. Yet, the accuracy would gradually improve by increasing the high-fidelity samples. Moreover, note that the computational time of a FE simulation is typically a few (~5) minutes while it is less than a second for the presented multi-fidelity model. The presented modelling approach therefore can efficiently replace high-cost FE simulations in process optimization or online simulations.
Synthesis and characterization of Fe-doped TiO2 on fiberglass cloth for the wastewater treatment reactor
(2020-05-04) Ahmed, Faysal; Ahmadi, Keivan; Herring, Rodney A.
The photocatalytic wastewater treatment facility presented in this thesis is a promising economic green technology that can degrade wastewater’s organic and ammonia pollutants, which produce environmentally sensitive products like CO2, H2O, Nitrates, etc. that can be captured and used in many biological and engineering ways. Previous advances used for this research was determining the importance of cleaning the photocatalytic nanocrystals, Fe-TiO2, as one of the revolutionary improvements that expose and maximizes the active surface of the photocatalytic nanocrystals to the pollutants enabling the strong oxidants produced by the absorption of a photon, excitation of an electron and positive hole to produce oxidants on the surface of the nanocrystals. The oxidants indiscriminately produce CO2 and H2O from living and non-living organic matter to obtain near ~100% clean water. This research focused on taking the next steps in the development of a wastewater cleaning facility tested in our laboratory. An important step involved coating Fe-TiO2 crystals onto flexible, strong, fiber-glass cloth using a sol-gel processing method. Success was found in this research by applying the coated fiberglass cloth into a photoreactor aimed to clean a large amount of water rather than the laboratory scale.

Browsing by Supervisor "Ahmadi, Keivan"

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