CAREER: Multiobjective Learning Control Strategies for Additive Manufacturing

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

The research objective of this Faculty Early Career Development (CAREER) award is to create system-theoretic design and analysis tools for high-throughput and high-precision control of additive manufacturing. Despite their tremendous potential, additive manufacturing have not yet delivered functional parts at a production scale due to poor process reliability. A key reason for the low reliability is the lack of accurate process models coupled with open-loop process control. To counter this challenge, the unique layer-by-layer material deposition mechanism of additive manufacturing will be exploited by learning algorithms that rely on iterative refinement based on sensor data in conjunction with partial model information. Inspired by multi-objective optimization, the proposed layer-to-layer learning control algorithm will simultaneously address multiple conflicting objectives to deliver acceptable part geometry and mechanical properties, in the presence of modeling errors and uncertainties in operating conditions. Deliverable of this project include (1) design and analysis tools for multi-objective learning control algorithms tailored towards jet-based printing and selective metal melting processes, (2) verification of proposed algorithms on experimental test beds, (3) development of hands-on experimentation and interactive course modules on additive manufacturing and automation at the K-12 level, and (4) training of undergraduate and graduate students for careers in automation, manufacturing, and control. The successful completion of this project will make a strong positive impact on the rapidly expanding billion-dollar additive manufacturing industry by increasing reliability, repeatability, and production rate. Specifically, enhancing reliability and performance of polymer-based additive manufacturing process can have a significant impact on the fabrication of one-off mass-customized parts including biomedical implants, opto-mechanical components, and tissue engineering. On the other hand, reliable metal-based additive manufacturing processes can produce parts for safety-critical applications such as in aviation industry. Through the proposed outreach and education plan, this project will spread awareness about the importance and opportunities in advanced manufacturing, train students in critically needed skills, and inspire talented young engineers to pursue careers in automation, control, and advanced manufacturing.