Robotizing the conventional and Hot-Forging Wire Arc Additive Manufacturing processes for producing 3D parts with complex geometries

Wire Arc Additive Manufacturing (WAAM) is an Additive Manufacturing (AM) process which has high deposition rates at reduced costs, being suitable to produce large size compo-nents. Hot-Forging WAAM (HF-WAAM) is a WAAM variant which uses an oscillating hammer to forge the material as it is deposited, improving mechanical properties and the microstruc-ture of the produced parts. This study aimed to use and validate the WAAM and HF-WAAM to robotize the pro-duction of compact metallic and complex geometry parts. Thus, a welding torch capable of performing forging was redesign, developed and assembled in a 6 degree-of-freedom (6-DoF) manipulator robot. 316LSi stainless steel parts were produced using WAAM and HF-WAAM processes. During their production, the vibration signal of the robot was acquired and then processed and compared. The AM robotic system demonstrated to be suitable to build these parts, since the tool tip speed and tool tip to substrate distance are controlled, and the tool path optimized. It was also observed that vibration did not negatively affect the built parts quality.O Wire Arc Additive Manufacturing (WAAM) é um processo de Manufatura Aditiva (MA) que apresenta elevadas taxas de deposição a custos reduzidos sendo adequado para produzir peças de grandes dimensões. O Hot-Forging WAAM (HF-WAAM) é uma variante do WAAM que usa um martelo oscilante para forjar o material à medida que este vai sendo depositado, melhorando as propriedades mecânicas e a microestrutura das peças produzidas. Este trabalho tem como objetivo usar e validar o WAAM e HF-WAAM para robotizar a produção de peças metálicas com geometria complexa. Para isto, uma tocha de soldadura com capacidade de realizar forjamento foi redesenhada, fabricada, e montada num robô manipu-lador de 6 graus de liberdade (6-DoF). Foram produzidas peças em aço inoxidável 316LSi uti-lizando os processos de WAAM e HF-WAAM. Durante a sua produção, o sinal de vibração do robô foi adquirido e posteriormente processado e comparado. O sistema robótico de MA demonstrou ser adequado para produzir peças quando a velocidade da ponta da ferramenta e a distância da ponta da ferramenta ao substrato estavam controladas e o percurso da ferramenta otimizado. Também se observou que a vibração não afetou negativamente a qualidade das peças produzidas

ACCURACY IMPROVEMENT OF INDUSTRIAL SERIAL MANIPULATORS FOR MANUFACTURING APPLICATIONS

Modern Industrial robots are designed to be highly repeatable (< 0.1 mm) but not as globally accurate (<2 mm). Global accuracy, however, is necessary for tasks where it is not convenient to “teach” the robot the set of poses it needs to run through to perform the task. In addition, some of these tasks, like machining, may involve high time-varying external forces which cause the robot to deflect and its accuracy to suffer further. This dissertation investigates modeling and control strategies for the purpose of improving the global accuracy of the robot for manufacturing tasks including machining. First, a comparison of stiffness modeling techniques is conducted to examine when it is important to account for the structural dynamics of the robot, versus when static stiffness calibrations are sufficient. Next, a new method of performing a highly accurate state estimation of the robot end-effector by combining instantaneous inertial and pose measurements is proposed and evaluated. Finally, a new method for performing stability-prediction of closed-loop systems involving industrial manipulators and external sensors, which involves representing real-time position corrections as force inputs, is presented and evaluated.Ph.D

Accuracy Improvement in Robotic Milling Through Data-Driven Modelling and Control

Six degree of freedom (6-dof) articulated arm industrial robots are promising candidates for aerospace machining operations such as milling due to their low-cost and large workspace compared to Computer Numerical Control (CNC) machine tools. However, the instantaneous position accuracy of industrial robots during milling is dependent on the vibratory behavior of the end effector tool tip. Consequently, it is important to model and predict the robot’s tool tip vibration as the arm configuration changes over the workspace. This dissertation addresses the modeling, prediction, and control of instantaneous tool tip vibrations of a 6-dof industrial robot over its workspace using data-driven methods. First, a data-driven modeling approach utilizing Gaussian Process Regression (GPR) of data acquired from modal impact hammer experiments to predict the modal parameters of a 6-dof industrial robot as a function of its arm configuration is presented. The GPR model is found to be capable of predicting the robot’s dominant natural frequency of vibration, stiffness, and damping coefficient in its workspace with root mean squared errors of 3.31 Hz, 150 KN/m, and 810 Ns/m, respectively. The predicted modal parameters are used to predict the average peak-to-valley vibrations of the tool tip during robotic milling. The results show that the average peak-to-valley vibrations predicted by the model follow the experimental trends with a maximum error of 0.028 mm. The prediction errors are attributed to the fact that the model only predicts the modal parameters corresponding to the dominant mode of vibration instead of the entire Frequency Response Function (FRF) of the robot. The GPR model is also used to create a Linear Quadratic Regulator (LQR) based pose-dependent optimal controller to suppress tool tip vibrations of a 6-dof industrial robot during milling. Robotic milling experiments show that the LQR controller reduces tool tip vibration amplitudes by an average of 47%. However, offset mass experiments show that the optimal controller has a bandwidth limitation of 24 Hz due to an intrinsic delay in the robot controller response to control commands. Finally, a hybrid statistical modelling approach that augments the GPR model of the robot’s pose-dependent FRF derived from experimental modal analysis, i.e. impact hammer tests, with the robot’s FRF derived from operational modal analysis, which utilizes milling forces and tool tip vibrations to compute the FRF, is presented. The hybrid model augmentation approach is demonstrated to be an efficient method to improve the prediction accuracy of the robot’s FRF with minimal optimization iterations. Specifically, the hybrid model is shown to reduce the root mean squared errors in predicting the FRF by 34% and the number of optimization iterations by 50%.Ph.D