SBIR Phase II: Vacuum Arc Control using Arc Position Sensing and Induced Magnetic Fields

This Small Business Innovation Research (SBIR) Phase II project will provide commercial validation at scale of feedback-based control of arc behavior within the vacuum arc remelting (VAR) process. This will improve VAR performance in the production of specialty metals, resulting in improvements to ingot quality while reducing electricity consumption. Specialty metals, such as titanium and nickel alloys, are used in critical high-performance parts in industries such as aerospace, energy, and medicine, where the failure of these parts may lead to catastrophic systems failures and potentially life-threatening situations. In a VAR furnace, extreme temperature gradients from constricted and/or diffuse arcs sustained between the melting electrode and ingot can cause non-homogeneous material and inclusion defects, resulting in up to 8% yield loss per ingot, representing $1.024 billion in losses across the domestic industry. Improved control over the arc distributions during the melting of these metals is expected to decrease the frequency of defects in the final product and increase overall yield from the process. The proposed project is expected to reduce these loses by up to 50% through the application of active, feedback control of the arc dynamics. This type of control is expected to increase yield, decrease energy requirements, and increase safety of the manufacturing process industry-wide. This project will result in a system capable of detecting and manipulating the distribution of the arcs utilized during VAR processing. The Phase I effort showed that it is possible to simultaneously detect arc locations on the electrode and influence their movements, using electromagnetic coils, in real time. In Phase II, the arc measurements will be coupled through feedback to control the arc distribution in an industrial-scale research VAR, providing proof of concept at industrial scale. In so doing, the optimal electromagnetic coil geometry, hardware, and materials for driving the arc motion at scale will be identified and constructed. A series of industrial experiments are planned to validate the control system. The chemical composition of the ingots produced during controlled and uncontrolled conditions will be characterized to correlate defects with observed arc behaviors and to identify optimal control parameters. Similarly, the measured arc distributions will be used to validate the computational solidification modeling, which will be used to identify probable defect regions. The combination of experimental data and validated simulation results will be used to inform the VAR feedback control system regarding optimal arc distribution, yielding an improved control strategy for tailoring the melt process and improving ingot quality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.