IMECE2004-60386 THERMAL CONTROL OF LASER POWDER DEPOSITION-HEAT TRANSFER CONSIDERATIONS

ABSTRACT Laser based solid free-form fabrication is an emerging metallurgical forming process aimed at rapid production of high quality, near net shape products directly from starting powders. Laser powder deposition shares, with other free-form technologies, the common characteristic that part fabrication occurs directly from a 3-D computer aided design (CAD) model. The microstructure evolution and resulting material properties of the component part (strength, ductility, etc.) fabricated using laser deposition are dependent upon process operating parameters such as melt pool size, laser power, head (manipulator) speed, and powder flow rate. Presently, set points for these parameters are often determined through manual manipulation of the system control and trial and error. This paper discusses the development of a path-planning, feed-forward, process-driven control system algorithm that generates a component part thermal history within given constraints, thereby assuring optimal part quality and minimizing final residual stresses. A thermal model of the deposition process drives the control algorithm. The development of the thermal model is the subject of this paper. The model accounts for temperature-dependent properties and phase change processes. Model validation studies are presented including comparisons with known analytic solutions as well as comparisons with data from experiments conducted in the laser laboratory at SDSM&T. INTRODUCTION Laser based solid free-form fabrication is an emerging metallurgical forming process aimed at rapid production of high quality, near net shape products directly from starting powders The SDSMT Advanced Materials Processing (AMP) center has a continuous wave (CW) 3 kW Nd: YAG laser equipped with two metal powder-feed systems and mounted on a Fanuc 16Mi robot. This equipment allows for direct laser deposition, solid freeform fabrication, and graded alloy research and development programs on titanium, nickel, and other refractory metal alloys. Current research includes projects aimed at improving performance of armored vehicles [2] and aerospace vehicles There is significant experimental evidence indicating a relationship between laser powder deposition operatin

Feedback Error Learning Neural Network for Trans-Femoral Prosthesis

Feedback-error learning (FEL) neural network was developed for control of a powered trans-femoral prosthesis. Nonlinearities and time-variations of the dynamics of the plant, in addition to redundancy and dynamic uncertainty during the double support phase of walking, makes conventional control methods very difficult to use. Rule-based control, which uses a knowledge base determined by machine learning and finite automata method is limited since it does not respond well to perturbations and environmental changes. FEL can be regarded as a hybrid control, because it combines nonparametric identification with parametric modeling and control. This paper presents simulation of a powered trans-femoral prosthesis controlled by a FEL neural network. Results suggest that FEL can be used to identify inverse dynamics of an arbitrary trans-femoral prosthesis during simple single joint movements (e.g., sinusoidal oscillations). The identified inverse dynamics then allows the tracking of an arbitrary trajectory such as a desired walking pattern within a multijoint structure. Simulation shows that the identified controller responds correctly when the leg motion is exposed to a perturbation such as a frequent change of the ground reaction force or the hip joint torque generated by the user. FEL eliminates the need for precise, tedious, and complex identification of model parameters