Yusuf Altintas

Shell face milling cutters are widely used in the industry for various machining applications for their efficiency in achieving a high material removal rate and superior surface finish. Particularly, larger diameter shell face mills with a high number of teeth can remove excess material from workpiece surfaces in a single pass. In this thesis, shell face milling cutters are mathematically modeled and design modifications are done during the cutter design phase to improve their chatter stability and productivity.The generalized mechanics and dynamics of shell mills are modeled for any given cutting condition, insert geometry, cutting coefficients, cutter workpiece engagement, and runout. The cutting force coefficients of AlSi12 work material are experimentally identified in both rake face (UV) reference frame and Radial-Tangential-Axial (RTA) coordinate frames and used in the process mechanics and dynamics models. The generalized model allows the prediction of cutting forces, torque, and power. The Frequency Response Function (FRF) of the cutter is obtained from Finite Element analysis of the digital model of the cutter and used to predict chatter stability and vibrations. The structural dynamics, cutting force prediction and chatter stability models are experimentally validated. The dynamic stiffness of the cutter is improved by design modifications which allowed the reduction of the mass and improved stiffness of the structural modes that affect the chatter stability.The thesis presents a systematic design, modification, and machining performance analysis of shell face mills in a digital environment to avoid costly physical trials.

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This thesis presents a physics-guided neural network model to predict the frequency response function (FRF) at the tool tip and a Bayesian learning method to reach the optimum cutting conditions with the highest material removal rate by a few cutting tests.The spindle assembly is one of the weakest parts in the machine tool and contributes to the chatter. The undesired vibrations lead to a poor surface finish and can damage the tool, tool holder, and spindle. The FRF of the spindle up to the flange is obtained experimentally by three impact modal tests and the inverse receptance coupling method. The Timoshenko beam based finite elements model is used to calculate the free-free FRF of arbitrary tools and tool holders. Then, the spindle, tool, and tool holder are coupled using the receptance coupling method to obtain the tool tip FRF. The modal parameters are extracted from the FRF to build a dataset of different tools and tool holders geometry with the corresponding modal parameters. A deep neural network (DNN) is then trained using the simulated dataset and fine-tuned using the experimental impact modal tests to overcome the inaccuracies and uncertainties in the model and measurements. The model’s predictions are verified with experimental impact modal tests and showed computationally efficient predictions. This thesis also presents a hybrid method to find the stable combination of cutting conditions with the highest material removal rate (MRR). The FRF is used to calculate the analytical stability lobes using the zero-order stability solution. Then, the cutting conditions with the highest analytical MRR are found, and the Bayesian algorithm is applied to search for the optimum cutting conditions with few cutting tests. The method is verified on a milling machining center.

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The prediction of cutting forces is essential to plan machining operations without unstable vibrations, tool breakage, excessive tool deflections, and spindle overloads. Tool geometry, tool-workpiece engagement area, and material-dependent cutting force coefficients are required to model the cutting forces. This thesis presents a generalized mechanistic model to identify the friction and normal force coefficients between the chip and the rake face of the tool. The cutting force coefficients depend on the material’s shear yield stress, the friction coefficient between the workpiece and tool materials, the shear angle, local rake and oblique angles, cutting speed and chip thickness when machining isotropic metal alloys. They also depend on the flank wear, the direction and density of fibres, and the fibre-matrix layout for Carbon Fibre Reinforced Polymer (CFRP) composite materials. A generalized method that allows estimating the cutting force coefficients from milling and drilling tests is proposed in the thesis. The cutting force coefficients are estimated by modelling forces with differential elements as a function of the fibre angle, tool geometry, and process parameters using least squares. The distributed forces are superposed in the measured directions. In addition to tooth passing frequency, the cutting forces are considered periodic due to fibre direction and density, which is handled by modelling the cutting force coefficients by a mean and fundamental component governed by the fibre-cutting angle relative to the cutting speed. The effect of the tool wear is considered for CFRP drilling operations. The model has been extended to include the dependency of force coefficients on the normal rake angle when machining isotropic metal alloys. The proposed general mechanistic model is experimentally validated in the milling and drilling of unidirectional CFRP composite and Aluminium Al7050-T7451 materials. It is shown that the identified cutting force coefficients can be used to predict forces in any machining operation, as demonstrated in ball end milling and drilling tests. The model allows rapid but sufficiently accurate identification of cutting force coefficients for isotropic and anisotropic materials.

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No abstract available.

Monitoring and control systems for machine tools are essential for increasing productivity. A robust monitoring system, coupled with the ability to use machining state signals predicted by the digital model is key to the implementation of such systems in production environment. This thesis presents the use of machining simulations to CNC-inherent or accessible data collected from sound, vibration, and force sensors. Through the combination of simulations and on-line measurements, a digital twin is created to detect chatter, tool breakage, and tool wear.First, the machining process states such as force, torque, power, and cumulative chip removal are simulated along the tool path. The actual and virtual positions of the tool along the tool path are synchronized during actual machining so that measured and simulated states can be compared. A new tool wear monitoring algorithm is proposed. The cutter – workpiece engagement area and cumulative chip removed by the cutting edge are computed using the Virtual Machining Software developed in the laboratory. The spindle servo motor current is collected from the CNC and normalized by the engagement area. The tool wear is correlated to cumulative chip thickness and an increase in the geometry-independent spindle motor current using a few tool wear measurements. It is shown that the tool wear progress can effectively be monitored by integrating simulation and motor current extracted from the CNC system. Similarly, chatter is also detected from sound spectrum measurement along the tool path by differentiating it from the air cut, transient vibrations and changes in the workpiece geometry with the aid of digital simulations. Chatter detection and avoidance algorithm is also enhanced by deactivating it at transient cutting zones. In some applications such as adaptive force control, it is necessary to measure cutting forces during machining. A commercial tool holder equipped with accelerometers is used to predict cutting forces from vibration data. The transfer function between the vibrations measured by the instrumented tool holder and the applied force is modeled. The cutting forces are predicted from the vibration measurements with the aid of Kalman filter and compared against the digital estimations along the tool path.

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The monitoring of machining process, tool damage and machine health are crucial to the part quality while avoiding damage to the machine tool. The cutting force provides important information about the state of the process and cutting tool because it correlates to the physics-based mathematical model of the process. This thesis presents methods for the estimation and prediction of cutting forces from the spindle and feed drive motor current data extracted from CNC. The spindle motor current is correlated to inertia, friction in the bearings, and cutting torque as a disturbance in closed-loop spindle speed controller. The Frequency response function (FRF) of the spindle servo drive is measured via a built-in CNC function. The state-space model of cutting torque disturbance of the spindle drive is modeled. Kalman Filter and Regularized Convolution (RD) are used to compensate the disturbance effects of electrical and mechanical dynamics to widen the bandwidth of cutting torque estimation from spindle motor current. Automatic tuning of Kalman Filter’s covariance and RD’s regularization factor is investigated by checking the Neural Network-based online tuning of Kalman Filter and the off-line L-curve tuning of both estimators. The proposed methods are experimentally illustrated in milling operations. It is shown that Kalman Filter can estimate the cutting forces on-line during machining. RD is used only in off-line estimation due to its computational cost, although it has higher accuracy due to its compensation at a wider frequency range. L-curve only achieves offline tuning due to its computational cost. Neural Network can auto-tune the estimator, but training the network requires high computational efforts. The feed drive motor current commands are used to predict the cutting forces in Cartesian coordinate system. The previous research illustrated that Kalman filter estimates the periodic cutting forces up to about 150 Hz bandwidth, provided that the FRF is not position-dependent, and friction is compensated. This thesis presents the use of average motor current to estimate the average cutting forces after friction compensation. The average forces are used to monitor the variation in cutting force coefficients which is used to calibrate the process simulation models in a digital twin environment.

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The demand in methods to manufacture parts with complex geometries accurately while minimizing the machining times have driven the technological development of computer numerical control (CNC) machine interpolator algorithms. However, the nature of these algorithms are hidden within the proprietary software of commercial CNC systems, making it difficult to predict the cycle time (i.e. machining time) of a part. This thesis presents the use of finite impulse response (FIR) filtering and polynomial interpolation methods in cycle time prediction algorithms for CNC machines. CNCs use either FIR filter or polynomial based motion trajectory generators to deliver smooth reference position commands to the machine tool’s feed drives. A FIR filter trajectory generation algorithm was developed using two cascaded first-order FIR filters with local corner smoothing by controlling the timing of velocity pulses for 5-axis CNC machine tools. Through the identification of FIR filter parameters from CNC machines using a simple linear toolpath, a cycle time prediction algorithm was developed from the trajectory generation algorithm suited for part programs with longer path segments. The FIR filter-based trajectory generation was experimentally validated to predict cycle times with less than 10% error. Another cycle time prediction algorithm was developed using a polynomial-based trajectory generation algorithm with a cubic acceleration motion profile and spline corner smoothing. The motion parameters such as corner feed, acceleration, and jerk required for polynomial-based interpolation methods are identified for a CNC machine using a corner variance test and approximated using exponential functions. The parameters are fed into the trajectory generation algorithm to predict the cycle time for a part program. The polynomial-based cycle time prediction was proven to predict cycle times within 1% of the actual measured time on the machine.

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Numerically controlled 5-axis machine tools are highly susceptible to potential errors due to their complex geometry. The machine tools comprise prismatic and revolute joints, and errors in each joint's geometry can cause a deviation from the desired tool position and orientation, resulting in volumetric errors. These errors can significantly affect the machine's performance, precision, and operation cost. Therefore, accurately predicting volumetric errors is crucial in developing compensation strategies.This thesis presents a generalized volumetric error model for all classes of machine tools, including rotary table, spindle rotary, and hybrid types. A screw-theory-based kinematic model is employed, which can be generalized to map the axis commands into the workpiece coordinate system, defining the relative motion of the tooltip with respect to the workpiece. The error model incorporates forty one geometric errors, and the effect of different classes of machines is studied to identify the position-independent geometric errors of rotary axes. A laser interferometer is used to measure fifteen position-dependent geometric errors of linear axes, including linear positioning, straightness, and angular errors. A Ballbar system is used to measure the remaining errors in a specific rotational test involving both linear and rotational axes to record the machine’s performance in a circular motion, including the position-dependent and independent geometric errors of the rotary axes and squareness errors. As the Ballbar recorded measurements are influenced by more than one error parameter, a decoupling method is implemented to individually identify each error parameter of rotary axes.The effectiveness of the proposed method is validated by machining a Pyramid-shaped test part under chatter-free conditions. The machined part is measured using a coordinate measuring machine, and the results demonstrate that the prediction method is 90% accurate for three-axis machining and 75% for five-axis machining.

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Machining processes are widely used to achieve the net shape of the parts in the industry. To plan the process with desired productivity and accuracy, the mechanics and dynamics of machining operations must be modeled to predict the cutting forces, torque, power of the spindle, and deflection marks left on the cut surface.Namely milling, drilling, boring, and turning, all machining processes remove layers of the workpiece to achieve the final product. Due to the similarities between different machining operations in terms of the cutting principles, they are generalized in one mathematical model to predict the cutting mechanics and dynamics in this thesis.Although operations are varied in terms of tool setup and configurations, all the tools are modeled through basic CAD elements and combined to achieve the final tool assembly. Considering the tool and workpiece's relative translational and rotational motions, the process types are determined automatically, and cutting engagement angles in the contact zone are interpreted from the tool CAD model, addressing all the geometrical features.A unified kinematics model is proposed to predict the cutting forces, spindle torque, and deflection marks left on the cut surface. The process kinematics are also used for a fast and acceptably accurate prediction of the chatter stability in the frequency domain.The results of the developed generalized physics-based model are presented for multiple industrial applications, including milling, drilling, boring, and turning operations. The simulated cutting mechanics and chatter stability charts predicted by the analytical model are demonstrated to be valid through comparisons with experimental measurements and existing data available in the literature.The proposed generalized model allows the planners to predict and optimize the 3-axis machining operations by avoiding costly trial and error-based tests in the industry.

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Slender single point boring tools are highly sensitive to chatter vibrations and require additionalchatter suppression design features to avoid unstable vibrations. Tuned mass dampers (TMDs) inboring tools are vibration absorbers where the natural frequency and damping ratio of the TMD istuned to maximize the chatter resistance of the tool by increasing its dynamic stiffness. The chatterresistance of the tool is highly sensitive to the correct tuning of the TMD.A fixed TMD, where the TMD parameters are fixed upon installation, has intrinsic limitations inits use cases and effectiveness in suppressing chatter vibrations, namely due to suboptimal tuning, detuning, improper mounting, and the effects mounting the tool to a machine has on the dynamics of the structure. Adaptive TMDs, there the TMD parameters can be modified post-installation, address the limitations of fixed TMDs, however specialized modal analysis equipment and knowledgeable personnel are required in practical applications. This thesis investigates theimplementation of a high-fidelity simulation model of the dynamics of a boring bar – TMD systemdeveloped to perform the tuning of adaptive TMDs digitally to address the barriers restricting theadoption of adaptive TMDs in industrial settings.The simulation model of the system consists of the frequency response function (FRF) of theboring bar calculated using finite element analysis (FEA) coupled with the analytical determinedFRF of the TMD determining the dynamic behaviour of the combined system. A tuning algorithmis developed to determine the optimal damping ratio and natural frequency of the TMD accordingto the specific geometry of the boring bar and is compared against contemporary tuningmethodologies. A boring bar – TMD system with a manual tuning attachment has been designedand fabricated and the tool tip FRF of the system has been measured. The digital model is verifiedby comparing the simulated subcomponent (TMD, and boring bar) vibrational parameters(effective mass, natural frequency, and damping ratio) against the subcomponent vibrationalparameters identified from the measured FRF of the designed system.

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Milling is a subtractive manufacturing technique, where the material is continuously removed from a workpiece until the desired part shape is obtained. Heavily used in the automotive and aerospace industries, Computer Numerical Control (CNC) milling machines occupy a large chunk in the manufacturing process. The current research focuses towards machining and machine tool monitoring systems that are more self-sufficient and self-adjusting to adapt the processes to machine tools. Cutting torque delivered by the machine tool spindle is one of the key sensory signals for machining process monitoring. This thesis presents a method that automatically reconstructs cutting torque from motor current commands generated by the servo controller of the machine tool. To estimate the cutting torque from commanded spindle current, the dynamics between torque to current relationship must be modeled and compensated. The thesis first presents an automated identification of spindle dynamics using data-driven system identification methods. The frequency response function (FRF) of the spindle dynamics is measured manually using CNC internal diagnostic tools. The identified FRF is then automatically converted to a state-space model using the Eigensystem Realization Algorithm (ERA). To reduce overfitting, an optimal threshold is applied to the ERA method to limit the identified system order. And to ensure the stability of the identified system, unstable eigenvalues of the system are removed using Schur decomposition. The identified system is then augmented such that the unknown torque input is modeled as a state changed by a random process noise. This augmented system is used to create a Kalman Observer, which compensates the spindle dynamics and estimates the torque from the spindle nominal current. The Kalman Observer is tuned automatically by estimating the noise covariance values using machining simulations. The method was eventually validated on a Quaser UX 600 industrial CNC system.

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Computer Numerical Controlled (CNC) Milling is used to remove excess metal from a blank to produce the final shape of the workpiece. One limitation of high material removal rates with reduced cost in machining operations is self-excited vibrations called chatter. Chatter results in poor surface finish, damage to the workpiece and machine tool’s spindle, and causes accelerated tool wear. Chatter detection and prevention have been one of the major fields of study in manufacturing to improve the quality and productivity of machining operations.This research proposes a self-evolving, online method to detect and avoid chatter in milling operations by fusing deep learning with the knowledge of chatter theory. The process is monitored by collecting vibration data during machining. Windows of data are converted into Short-time Fourier Transforms and processed through a Convolution Neural Network to identify five machining states: Air cutting, entrance to and exit from the cut, stable cut, and unstable cut with chatter. A base state detection model is built by modifying and training AlexNet architecture using experimental data with known states. A specialized Deep Learning architecture is designed based on the base model, using an Automated machine learning process, with high state detection accuracy and low complexity in mind. In parallel to the machine learning model, chatter detection with a physics-based model is executed to increase the robustness and accuracy of chatter detection. The forced vibrations which always occur in milling are removed by Kalman filter, and the occurrence of chatter is detected using an energy-based method. The hybrid system detects the chatter with a 98.8% success rate. The system has a built-in online self-improving capability. The system stops the machining process and commands a new spindle speed to force the machine to operate in chatter-free zone.

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Increasing demand for production speed on 5-axis computerized numerical control (CNC) machining technology has led the research studies to improve the precision and predict the machining cycle time hence the cost of manufacturing from the numerical control (NC) part programs. In this study, a recursive trajectory generation of splined paths is proposed to decrease the feed rate fluctuation. The trajectory points are found by the iterative bisection method to improve the accuracy by reducing the interpolation error. Next, the spline toolpath is interpolated backward from the end of the toolpath to generate the reference trajectory points. A full state feedback controller is introduced to force the tangential velocity to follow the desired motion trajectory and reference tool path points.Commercial CNC systems have hidden trajectory motion profilers that are difficult to model analytically. The thesis presents the identification of finite impulse response (FIR) filter settings of the machines from a standard tool path test conducted on the machine tool. The hidden corner strategy of the machine as a function of tolerance is approximated as an exponential function, and its empirical parameters are also identified from the same tool path test data. The NC part program with linear and circular tool path segments is parsed and the total machining time, i.e. cycle time, is estimated using the identified trajectory profile settings of the machine. The proposed cycle time prediction method is experimentally validated with 95% accuracy.

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Single point boring of highly slender workpieces with internal features requires vibration damped tooling to avoid unwanted chatter vibrations. The tuned mass damper (TMD) boring tool has made machining of these highly slender features possible by increasing the dynamic stiffness and therefore the chatter stability of the cantilevered tool. Chatter stability of these vibration damped tools is sensitive to how well the TMD has been tuned to the tool’s first bending mode, which varies between machine tools and mounting styles.This thesis presents a novel, automatically tunable TMD system for boring tools. The TMD mass is suspended in the frontal bore of the tool by adjustable springs in the form of compressible neoprene O-rings. The natural frequency of the TMD is adjusted automatically by a position control servo, which actuates a power screw and compresses the O-rings. The damping coefficient of the TMD is adjusted by identifying an oil of favorable viscosity. The TMD system requires the measurement of the boring tool’s Frequency Response Function (FRF) when it’s mounted on the machine tool. A computer-controlled electromagnetic force exciter has been developed. The impulse force delivered by the exciter is estimated from the measured magnetic field. Tool vibration response is measured from a standard non-contact capacitance probe or an accelerometer. With the tool mounted on the machine tool, automation software automatically tunes the TMD’s natural frequency in an iterative manner by commanding the position servo to compress the O-rings. At every iterative adjustment the tool’s FRF is measured using the computer controlled electromagnetic exciter. A search algorithm monitors the minimum real part of the tool’s FRF and adjusts O-ring compression until the negative real part is maximized, therefore maximizing chatter suppression. The proposed computer controlled, fully integrated excitation and tuning system has been built and experimentally validated in boring steel workpieces. It is shown that the system can increase stability relative to traditional slender boring tools by almost 100 times, and therefore provide increased capabilities and productivity.

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Additive manufacturing (AM) is a layer-based process for producing parts. Metal AM is an attractive technology for the aerospace and biomedical industries due to its ability to produce complex geometries from difficult to cut materials. Electron beam melting (EBM) is a form of metal AM, which uses an electron beam to melt metal powders into fully dense parts. The position and velocity of the electron beam are important parameters in determining the success of production in EBM. In order to provide robust control of the beam position, a model for real-time prediction of the electron beam position has been developed. The electron beam’s position is controlled by an electron beam deflection system, which uses electromagnetic poles to deflect the beam to a desired position on the build plate. This thesis presents an electron beam deflection system model, which can predict the beam position during EBM operation. The current behavior within the deflection coils is modelled using an equivalent circuit to determine the effective current within the coils. The prediction of the magnetic flux density distribution generated by the coils based on the effective current in the coils is described. The interaction between the generated magnetic flux density and the electron beam gun structure is modelled as a first order system, to predict the lag induced by eddy currents on the beam’s position. With the magnetic flux density distribution, the position of the electron beam was predicted using a discrete-time domain simulation. Crosstalk between the axes of the system was modelled with an empirical model. The proposed model was validated through FEM simulations and experimentation on a single-axis prototype as well as an EBM machine. Recommendations for hardware alterations within the EBM machine are made, which would reduce error in the beam’s position. Additionally, a pole-zero cancellation controller is designed to compensate for errors caused by eddy currents. A feed forward controller is designed, which predicts the crosstalk between the system’s axes and compensates for the error in real-time.

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Bone scaffolds are used to treat large bone defects. There are noted advantages for moving away from the standard of using human bone to create these scaffolds, and instead making them out of synthetic biomaterials. Dispensing Based Additive Manufacturing (DBAM) provides a method for creating these synthetic bone scaffolds using a biomaterial called alginate. This thesis outlines a method to create synthetic bone scaffolds from alginate using DBAM.First an ink was developed to be used with DBAM, consisting of sodium alginate, calcium carbonate and a photoacid generator. This ink was able to react when exposed to Ultraviolet (UV) light to partially gel, as required between layers in the DBAM process. Studies were conducted to find the ideal concentrations of the components of the ink. The gelation in between layers was modelled to determine the ideal layer thickness, UV lamp intensity and exposure time that would lead to the ideal mechanical properties for a partially gelled layer.The dispensing process was modelled to determine the height and width of an extruded line based on the applied pressure, needle diameter, and needle length. This model has been used to determine the ideal pressure to achieve a desired layer height. A commercial software was used to convert three-dimensional commanded shapes to two-dimensional layer toolpaths. Advanced trajectory techniques have been used to generate a time-based trajectory from these toolpaths that would minimize traversal time, while staying within the velocity, acceleration and jerk limits of the in-house developed Computer Numerical Controlled (CNC) machine. The dispensing was synchronized with the tangential speed of the machine to keep dispensed width constant.Sample parts have been manufactured using the developed DBAM process, with an improved dimensional accuracy and mechanical stiffness in comparison to results reported in literature.

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Five-axis Computer Numerical Controlled (CNC) machine tools are used to machine parts with complex, curved surfaces. When the reference toolpath contains frequencies beyond the bandwidth of the servo drive, the actual axis position lags the reference position commands which leads to axis tracking error. The tracking errors of three translational and two rotational drives are projected along the path using a kinematic model of the machine to form contouring errors i.e. the geometric deviation of the actual tool movement from the commanded toolpath. Parts with contouring errors larger than the design tolerance are often scrapped. Although contouring errors can be decreased by reducing the machining feedrate, this causes the cycle time to increase resulting in less productivity.This thesis proposes a feedrate optimization method which minimizes the process cycle time without violating contouring error limits as well as velocity, acceleration and jerk limits of the machine drives. First, discrete 5-axis tool positions are fitted to two quintic b-splines to represent the desired tooltip position and tool orientation trajectory. An initial, uniform feedrate spline is also generated in quintic b-spline form. Derived from the toolpath splines, discrete tool positions at uniform path displacement intervals are decomposed into axis commands based on the machine kinematics, and velocity, acceleration and jerk are calculated. The axis commands are also passed through the equivalent transfer function of each drive to predict their tracking errors, which are projected onto the toolpath to find contouring error. The optimization algorithm takes in the calculated axis tracking errors, contouring error, and cycle time for the given toolpath and feedrate profile. Within the optimizer, a gradient descent algorithm iteratively modifies the feedrate spline control points where the new feedrate profile is used to re-evaluate the cycle time, contouring error, and drive signals until a local minimum has been found. The final output of the algorithm is an optimized feedrate spline which ensures a minimum cycle time for the process, while maintaining drive and contouring error limits. The proposed algorithms are experimentally validated on a 5-axis machine tool controlled by an in-house developed open CNC system.

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Opening large number of holes takes considerable amount of time during manufacturing and assembly of aircrafts. Traditionally, tools having the same diameter of each hole have been used in drilling which take considerable amount of time for tool change and fixturing. Recently, orbital drilling technology has been introduced to open holes with a single set-up. The combined orbital motion around the hole and helical penetration in axial direction are either given by stationary computer numerically controlled (CNC) machines or hand held, portable heads that are attached to aircraft body with suction pads. Although the tool path and machine were developed, the process mechanics and dynamics have not been modeled to predict cutting forces, torque, power and chatter stability diagrams to identify most productive and safe cutting conditions. This thesis presents mathematical model to simulate the mechanics and dynamics of orbital drilling process. The mechanics of the process are modeled by identifying the chip thickness distribution along the peripheral and bottom cutting edges of the helical end mills used in orbital drilling, The pitch length of the path, tool and hole diameters, spindle speed, feed and material properties are used in the model which is experimentally proven by comparing predicted and measured cutting forces. The flexibilities of the orbital drilling head and tool are incorporated to the mechanics model to predict the dynamics of the system. It is shown that the additional delay contributed by orbital motion of the tool can be neglected, and the regenerative delay is dominated by the spindle speed. However, structural dynamic modes of the system need to be oriented along the tangential feed direction since it varies continuously along the orbital path. The chatter stability of the system has been developed in both frequency and semi-discrete time domains. The experimentally verified stability model considers spindle speed, tool and hole geometries, structural dynamics, material properties and orbital drilling pitch length. The proposed orbital drilling model allows optimal selection of spindle speed, feed, orbital speed, pitch length and tool diameter for a given work material without overloading the machine and chatter while achieving highest possible material removal rates.

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Additive manufacturing (AM) technologies are used in three-dimensional (3D) printing of parts by depositing the material layer-by-layer on the computer numerical controlled machine tools. While laser and electron beam guns are used to melt, and deposit the metals, thermoplastic materials are heated and deposited by the extruders. When the material deposition is not synchronized with the tangential velocity of the machine, an excess material is accumulated at sharp curvatures where the machine slows down. This thesis presents a novel algorithm for the synchronized deposition of thermo-plastic materials with the tangential path velocity of the machine under constant temperature. The temperature of the thermoplastic filament needs to be kept at a constant temperature (i.e. 220 Celsius) in a heater chamber. The transfer function of the temperature and current input to the heater is modelled as a first order system whose parameters are time varying as a function of material’s extrusion rate. An adaptive pole placement controller is designed to maintain the temperature of the material by manipulating the current supply to the heater as the extrusion rate vary. The tool path is first smoothed by a fifth order B-spline. The tangential path velocity is also smoothed by a third order spline while respecting heater’s power limit as well as the jerk, acceleration and velocity limits of two drives which are used in printing the material layer by layer. The extrusion rate is controlled proportionally to the tangential path velocity while keeping the temperature of the deposited thermo-plastic material at the desired temperature by adaptively controlling current supply to the heater. The experimentally proven algorithm leads to more uniform material deposition at sharp curvatures and resulting improved dimensional accuracy of printed parts. The proposed methodology can be extended to laser and electron beam based metal printing applications.

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The current trend in industry is to achieve intelligent, Computer Numerical Controlled (CNC) machine tools which can monitor its performance and take corrective actions automatically during machining operations. Cutting forces are the most accepted indicators of the tool condition, load on the machine and part, and abnormalities in the machining operations. The objective of this thesis is to predict the cutting forces from the current drawn by each drive during five axis machining operations. The cutting forces generated at the tool–workpiece contact zone are transmitted to the three translational and two rotary drive motors through ball screws and gear boxes. The torque received by individual motors is transformed as disturbance current by the motor amplifiers. The cutting force transmitted to each feed drive acts as a disturbance to the closed loop servo controller, which reacts by supplying torque command in addition to the torque required to overcome the friction and inertial motions. The accurate prediction of cutting forces from the motor current measurements requires the separation of the effects of cutting and inertial motion forces from the total motor current values. The transfer function between the applied force at the tool tip and motor current is identified at each drive. The effects of structural modes are canceled through extended Kalman Filter designed for each drive. Both Coulomb and Viscous Friction forces have been identified, and their effects are also removed from the state measurements of all drives. The cutting forces at the tool tip are predicted by applying extended Kalman Filter on motor current signals, and transmitting them to the tool tip through forward kinematic model of the machine, the contributions are proven using machining tests conducted on a five axis machining center.

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This thesis presents a six degree of freedom magnetically levitated rotary tablethat has one unlimited rotation axis. The actuation force is achieved by the Lorentzforce. The underside of the actuator has an axial Halbach array mounted circumferentially near the outside edge. Force is produced in the stator coils which are made using a printed circuit board. The purpose of this table is for a micro-machining rotary table application. Benefits of this table are its compact, lightweight, no friction and high precision characteristics.Control of the table in six degrees of freedom is achieved by dividing the stator into quarters. Each quarter is driven by a linear three phase current amplifier.For each quarter two forces can be generated, one in the levitation direction andone in the tangential direction. This creates eight independently controlled forcesallowing for full six degrees of freedom control. Position feedback for the stageis achieved by using four capacitive displacement measurement probes and fouroptical encoders around a circular optical grating. Performance of the table hasbeen tested and the results show that the closed loop bandwidth for all axes is between ∼ 250Hz and ∼ 550Hz. Regulation error in the X,Y and Z axes is less than55nm while the A,B and C axes are better than 1.2µrad (0.248 arc seconds). Forcecapacity has been tested up-to 70N with a theoretical limit of 140N.

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The growing demand for high speed machining in aerospace, automotive and die and mold industry has directed the interest of research community towards prediction and reduction of machining cycle time. In this thesis, a cycle time prediction scheme is proposed for milling operations based on identified CNC machine dynamics in exact-stop and continuous mode. Various system identification techniques are utilized to identify the implemented trajectory generation and corner smoothing technique and feed drive dynamics of the CNC system. An analytical approach for predicting cycle time based on the identified CNC system dynamics and given part program is presented. It is shown that the cycle time of NC machining process is predominantly affected by trajectory generation and corner smoothing techniques implemented on CNC systems. The closed-loop feed drive dynamics does not have much influence on the cycle time, since the tracking delay is insignificant in position control servos. The proposed algorithm is validated in experiments and experimental results has shown that the cycle time prediction error remains within 5% for various 2-axis, 3-axis and 5-axis toolpaths. In the later half of the thesis, a new decoupled approach for five-axis corner smoothing is presented to reduce the cycle time of milling operations. Toolpath position and orientation are smoothed by inserting quintic and normalized septic micro-splines, respectively between the adjacent linear toolpath segments. Optimal control points are calculated for position and orientation splines to achieve C³ continuity at the junctions between the splines and the linear segments while respecting user-defined corner position tolerance and orientation tolerance limits. Synchronization of position and orientation splines is carried out. After geometrical modification of the toolpath, feedrate planning is performed using C³ continuous cubic acceleration feedrate profile to preserve jerk continuity in toolpath motion. The proposed C³ continuous toolpath motion is compared against the unsmooth and C² continuous motion in experiments and simulations to show improvements in cycle time, tracking accuracy and smoothness throughout the toolpath.

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Feed drives of High Speed Machine (HSM) tools deliver fast motions for rapid positioning of tool or work-piece. The inertial forces generated by acceleration and deceleration of large machine tool components excite structural modes of the machine tools and cause residual vibrations. Unless avoided, the vibrations lead to poor surface finish and instability of the drive's control loop. In this thesis, structural flexibilities are represented by linear and torsional springs and dampers to develop a mathematical model of the feed drive dynamics. The model includes the contribution of structural vibrations in measuring table position by a linear encoder. An identification algorithm is introduced to facilitate the estimation of rigid body and structural dynamics in frequency domain. The identified mathematical model is used to mimic the real machine in simulations with the purpose of analyzing the interaction between structural dynamics and a high bandwidth adaptive sliding mode controller. Meanwhile, efficiency of finite element modeling approaches in predicting this interaction prior to the physical production is investigated by replacing the machine dynamics by a FEM based model. The mathematical model is used to design a Kalman Filter which estimates the table's acceleration by taking double digital derivative of the encoder signal. The table's acceleration is used to modify the control loop to minimize the effect of undesired structural vibrations. It is shown that the vibrations can be actively damped, and the bandwidth of the drive can be increased. The increase in the servo loop bandwidth provides smoother motion and improves the tracking performance significantly.

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The present Computer Numerical Controlled (CNC) machine tools can provide internal states of the machine (such as speed, feed, current, power, torque, and axis tracking errors) to external computers, which in turn can manipulate spindle speeds and feeds through Ethernet communication tools. This thesis presents on-line detection and avoidance of chatter vibrations, on-line prediction of cutting torque and its adaptive control during milling operations. Chatter is detected by monitoring the frequency spectrum of sound signals during machining operations. The forced vibrations that occur at spindle and tooth passing frequencies are removed through a comb filter. The chatter frequency and its magnitude are predicted. The spindle speed is automatically changed to enter the process into the nearest stability pocket if it lies within the first five stability lobes. If the process cannot be stabilized due to missing lobes at low speeds, the spindle speed is harmonically varied without violating the power limit of the spindle drive. The algorithm is implemented on a five axis Mori Seiki NMV5000 Machining Center with a FANUC 30i controller. The communication with an external PC is handled through Ethernet and FOCAS command library of Fanuc.The cutting torque is also predicted by monitoring the current of a three phase induction motor in real time. The cutting torque is estimated through Extended Kalman Filter from the steady state model of the motor after removing the friction component. The estimated torque is used to keep the cutting torque on the machine at desired and safe levels by manipulating the feed rate with adaptive pole placement controller.The thesis shows that it is possible to add process monitoring and control functions to the machine without having to add costly and impractical sensors on the machine, leading to safer and more productive machining operations.

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This thesis presents experimentally verified smooth spline interpolation and contour error pre-compensation algorithms developed for 5-axis machine tools. In the smooth spline interpolation algorithm, the cutter location data from the computer-aided manufacturing system is first fitted independently to decouple the relative changes in position and orientation. The tool tip positions are fitted to a quintic B-spline, achieving geometric jerk continuity. Next, the tooltip orientations are fitted to another quintic B-spline in spherical coordinates, achieving geometric jerk continuity and feasible orientations at all points of the spline. The nonlinear relationship between spline parameters and displacement are approximated with 9th order and seventh order feed correction splines for position and orientation, respectively. The 9th order feed correction spline is fit adaptively to minimize fitting error while preserving C³ continuity. The seventh order feed correction spline is optimized to minimize jerk while preserving C³ continuity as well. In the contour error pre-compensation algorithm, the position commands generated in the smooth spline interpolation algorithm, are first fitted to piecewise quintic splines while respecting velocity, acceleration and jerk continuity at the spline joints. The transfer function of each servo drive is kept linear by compensating the disturbance effect of friction with a feed-forward block. Using the analytically represented 5-axis, splined tool path, splined tracking errors and kinematic model of the five-axis machine tool, contouring errors are predicted ahead of axis control loops. The contouring errors are decoupled into three linear and two rotary drives, and the position commands are modified before they are sent to servo drives for execution. The methods developed in this thesis have been evaluated on a 5-axis machining center with a tilting-table configuration, and are directly applicable to other 5-axis kinematic configurations such as spindle-tilting or hybrid configurations. The experiments show improvements in fitting accuracy, reduction in vibrations, reduction in tracking errors, and significant reductions in contour error for five-axis tool paths.

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In machining, the desired final shape is created in Computer Aided Design (CAD) environment and this information is forwarded to Computer Aided Manufacturing (CAM) phase in which the toolpath is generated and converted to machine specific commands for part manufacturing. The steps in CAD/CAM environments are geometry dependent only, and do not include the physics of the process. However, mathematical modeling of the machining operation gives the flexibility of identifying and resolving process related issues i.e. tool breakage, chatter vibrations and tolerance violations beforehand, which in turn leads to increased productivity. The first step of process modeling is to model the mechanics of the operation that leads to the prediction of the cutting forces experienced by the cutting tool and the workpiece. In this study the mechanics of ball-end tool which is commonly used to machine parts with free-form geometric features are studied. The main problem in ball-end milling mechanics is tool indentation which leads to inaccurate force prediction in tool axial direction, and has previously been solved experimentally only for specific cases. This thesis presents a generalized ball-end tool indentation detection and indentation force prediction model for any kind of work material and cutting tool geometry combinations. The static ball-end milling forces with indentation forces are predicted by developing an analytical cutting edge indentation model. The proposed model utilizes indentation mechanics of punch and wedge shape indenters, describes the required conditions for indentation occurrence and evaluates plastic and elastic contact pressures at the cutting edge and workpiece interface using the material properties of the workpiece. Cutting edge indentation mechanism is also studied through finite element (FE) modeling. A general FE model is obtained for the problem and results are reported only for the material cut in the thesis. The model proposed in the thesis has been verified experimentally. After integrating the developed indentation force prediction model into the cutting force model, predictions in tool axial direction are improved by 15-40% depending on type of the operation. The contribution of the thesis can be used in cutting force based ball-end milling process optimization and analysis for industrial applications.

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The present machine tools travel at high velocities with large acceleration and jerk, which creates large inertial loads with wide frequency content, resulting in excitation of the natural frequencies of the machine. This thesis presents a command shaping module which avoids structural vibrations and improves contouring accuracy in five-axis machine tools.Inertial-induced vibrations of feed drives are avoided by applying a sequence of input shaping impulses on the axis position commands as a function of natural frequencies of the machine. Input shapers block those harmonics of the command which can excite the modes of the machine. While command shaping avoids the excitation of transient vibrations caused by inertial loads, it introduces a time delay which increases the contouring errors in multi-axis motion control systems.Although input shaping can effectively avoid vibrations, it distorts the toolpath in multi-axis contouring due to added time delays. The distortion increases the contour errors caused by the limited bandwidth of axis servo drives, resulting in relatively large geometric errors and violation of the required tolerance. The transfer function of the axis drives and kinematic configuration of the machine tool are used to estimate the contouring error, which is decoupled into axis components, and compensated prior to sending the position commands to each drive.The proposed integrated input shaping and contour error compensation module can be applied on any arbitrary trajectory, and the shaped-compensated command results in vibration-free and accurate contouring of the desired path. The input shaping and contouring error compensation techniques introduced in the thesis have been experimentally validated in single, two and five axis machine tools.

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Peripheral milling of thin walled aerospace components takes considerable amount of machining time as blank blocks are cut down to thin webs under excessive structural oscillations during the process. Unstable chatter vibrations and stable forced vibrations cause poor surface finish on the machined part. Predicting the process mechanics in advance eliminates the time consuming trial and error approach in reducing the vibrations which are within the tolerance limits of the part. This thesis presents the mathematical modeling of the peripheral milling of the thin walls with helical end mills. The cutting forces, vibrations and dimensional form errors left on the finish surface are predicted under stable but forced vibration conditions. The chatter stability diagram of the operation is predicted by using both frequency and semi-discrete time domain models. The relative vibrations between the flexible part and slender end mill are consi-dered. The tool and the workpiece are discretized along the contact axis to include effect of varying cutting forces and structural dynamics. The differential milling forces are evaluated from the static chip loads contributed by the rigid body motion of the milling operation, and dynamic chip loads caused by the relative vibrations between the flexible tool and flexible thin part. The different cylindrical end mill geometries with regular and non-uniform pitch and helix angles, and low speed process damping effects are included in the dynamic force model. The dynamic properties of the flexible structures are represented by expe-rimentally evaluated modal model in order to reduce the number of linear, periodic, delayed differential equations solved in frequency and time domain computations. The periodic, delayed differential equations are solved by the semi discrete time domain method to predict the amplitude of vibrations and forces. The equations of motion are simplified to constant coefficient type ordinary differential equations, and surface location errors are calculated by frequency domain solver. Chatter stability lobes are calculated using semi discrete time domain and fre-quency domain methods. Chatter stability solvers are validated by conducting chatter tests for roughing and finishing stages of thin walled aluminum part at high cutting speeds, and low speed machining of rigid steel block.

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Feed drives are used in positioning of machine tools. The drives are actuated either by linear or rotary servo motors. The ball screw drives are driven by rotary motors; hence they have flexibility and added friction due to nut interface. Direct drives are driven by linear motors which have more mechanical stiffness, but less disturbance rejection due to missing load reduction mechanism. This thesis presents the modelling and control of drives with rigid and flexible structures.A single degree of freedom flexible oscillator is mounted on a high speed, rigid feed drive table for experimental illustration of system identification and the active control method proposed in the thesis. The rigid feed drive dynamics include the mechanical component of the rigid body mass and viscous damping, and the electrical component of the power amplifier and motor. The flexible component is modelled by springs, mass and damping elements. Both rigid and flexible dynamics of the system are identified experimentally through unbiased least square, sine sweep and impact model tests. The vibration of the single degree of freedom system is actively damped by an acceleration feedback inserted in the velocity loop. A Kalman filter is used to minimize the drift and noise on the acceleration measurements. The position loop is closed with a proportional controller.It is experimentally demonstrated that the vibrations of the flexible structure can be well damped. However, the acceleration feedback used at the resonance frequency greatly minimizes the bandwidth close to the vibration frequency. Further methods need to be used to expand the bandwidth beyond the natural frequency of the flexible structure by coping with the anti-resonant effect of the acceleration feedback.

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The manufacturing planning of parts is currently based on experience and physical test trials. The parts are modeled, and Numerically Controlled (NC) tool paths are generated in Computer Aided Manufacturing (CAM) environment. The NC programs are physically tested, and if the process faults are found, the NC program is re-generated in the CAM environment. The objective of this thesis is to develop Virtual Turning System that predicts the part machining process ahead of costly physical trials.Tool–workpiece engagement geometry is calculated along the tool path by a proposed polycurve method. The part geometry is imported as a stereolithography (STL) model from the CAM system, and the cross section around the turning axis is reconstructed. The tool and part cross sections are modeled by polycurves, which are constructed by series of arcs and lines. The tool–part geometries are intersected using boolean operations to obtain the engagement conditions.The turning process is modeled by predicting the chip area and equivalent chord angle. The process forces are modeled proportional to the material dependent cutting force coefficients, depth of cut and equivalent chord length that depends on the nose radius and approach angle of the tool. The chatter stability of the process is examined using Nyquist criterion at each tool–workpiece engagement station along the path.The virtual turning simulation simulates the forces and detects the chatter stability, and adjusts the feeds at each tool-part engagement station. The physical turning of parts with arbitrary geometry can be simulated, and cutting conditions that leads to most optimal machining operation is automatically determined without violating the limits of the machine tool and part.

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This thesis presents a continuous tool motion trajectory generation algorithm for high speed free form surface machining. A NURBS toolpath generation algorithm is presented to fit the discrete motion commands generated from free-form CAD-models. By using a NURBS representation of the machine part, the toolpath is interpolated continuously to direct the synchronized motion of the 5-axis CNC machine. The higher continuity of the motion trajectory allowed for tighter machining tolerances and reduced feedrate fluctuations and the undesired acceleration harmonics in the overall feed motion and in each of the motor motions. An optimal and feasible feedrate profile have been used to continuously maneuver the cutting tool with the interpolated reference tool position and tool orientation commands such that the kinematic constraints of the drives are not violated. Commonly used least squares curve fitting of discrete data points forces the curve to weave through the data points and results in a fluctuating toolpath. By making use of the defined basis function distributions of the NURBS control points, a higher smoothness fit has been achieved through a minimization on the chord error and the third derivative of the curve. The feasibility of this toolpath generation algorithm has been extended using the double spline representation to represent both the tool position and the tool orientation with minimal fitting error. The real time interpolation of the fitted NURBS toolpath has also been implemented using the multi-segment Feed Correction Polynomial. This method provides an adaptive mapping between the nonlinear relationship of the NURBS curve parameter and the curve displacement to allow for a consistent feedrate in the cutting motion. Additionally, the kinematic compatibility conditions are considered based on the inverse kinematics of the 5-axis CNC machine. The proposed algorithm ensures that an overall efficient feed constraint is placed such that none of the individual drives are overdriven. The results from experiments and simulations are presented to demonstrate the effectiveness of the developed trajectory generation algorithms.

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The complexity and variation of parts are continuously increasing due to technologically oriented consumers. The objective of present manufacturing industry is to increase the quality while decreasing the machining costs. This thesis presents a smart machining strategy which allows the automated prediction of chatter-free cutting conditions using sensors integrated to Computer Numerical Controlled (CNC) machine tools. The prediction of vibration free spindle speeds and depth of cuts require to have material's cutting force coefficient and frequency response function (FRF) of the machine at its tool tip. The cutting force coefficients are estimated from the cutting force measurements collected through dynamometers in laboratory environment. The thesis presents an alternative identification of tangential cutting force coefficient from average spindle power signals which are readily available on machine tools. When tangential, radial and axial cutting force coefficients are needed, the forces need to be collected by piezoelectric sensors embedded to mechanical structures. The structural dynamics of sensor housings distort the force measurements at high spindle speeds. A Kalman filter is designed to compensate the structural modes of the sensor assembly when the spindle speed and its harmonics resonate one of the modes the measuring system. The FRF of the system is measured by a computer controlled impact modal test unit which is integrated to CNC. The impact head is instrumented with a piezo force sensor, and the vibrations are measured with a capacitive displacement sensor. The spring loaded impact head is released by a DC solenoid controlled by the computer. The impact force and resulting tool vibrations are recorded in real time, and the FRF is estimated automatically. The measured FRF and cutting force coefficient estimated from the spindle power are later used to predict the chatter free depth of cuts and spindle speeds. The machine integrated, smart machining system allows the operator to automatically select the chatter-free cutting conditions, leading to improved quality and productivity.

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Rotating shafts are used in the power train components of aircraft and automotive engines. The shafts are turned on lathes. Engine cylinders and bearing housings are finish machined using boring bars with single or multiple inserts. The cutting forces excite the structural dynamics of the turned shafts or boring bars during machining, leading to a poor surface finish and possible damage to the machined parts. This thesis presents mathematical models of single and multiple point turning/boring operations with the aim of predicting their outcome ahead of costly physical trials on the shop floor. Turning and boring operations are conducted at low angular speeds where the system dynamics is dominated by the process damping mechanism. The dynamic forces are modeled proportional to the static and regenerative chip thickness, tool geometry, and velocities of the vibration. The process damping coefficients, which are dependent on the material, tool geometry, cutting speed and vibrations, are identified from chatter tests conducted at the critical speeds and depths. The structural dynamics of the long boring bars are modeled using the Timoshenko Beam elements in Finite Element model which allows parametric placement of the boundary conditions, such as the bearing supports. The dynamics of the interaction between the cutting process and the structure are modeled. The stability of the operations is solved in frequency domain, analytically when the velocity and vibration dependent process damping is neglected. When the process damping is included, but the periodicity of the dynamic forces is neglected, the stability of the process is solved using the Nyquist criterion. When the periodicity and process damping are considered, the dynamic system is represented by a set of differential equations with periodic, time delayed forces. The stability of such systems, which are found in the line boring of crank and cam shaft housings, is solved in the time domain using an analytical but semi-discrete method. The thesis presents a complete set of solutions in predicting the static and dynamic forces, as well as the critical depths of cuts and speeds to avoid chatter vibrations in single point, multi-point and line boring operations.

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