Multi-laser Powder Bed Fusion Additive Manufacturing System for Accelerating Material Design, Process Optimization, and Feedback Control
Abstract
Funding is requested for the instrumentation of a state-of-the-art multi-laser powder bed fusion additive manufacturing (L-PBF AM) system (EOS M300-4) with in-situ monitoring and ex-situ characterization and computational capabilities. The instrumented multi-laser L-PBF system is envisioned to 1) accelerate the design of new AM materials and alloys, 2) streamline the determination of optimal processing windows for these new materials to fabricate defect free parts, and 3) establish a modern Industry 4.0 platform where real-time sensory signals are integrated with new machine learning algorithms to detect and mitigate process defects. The system will consist of a manufacturing platform with four independent precision fiber lasers ate Texas A&M (TAMU) that significantly increases the throughput of the AM process. New types of sensor systems will be developed that stem far beyond traditional thermal monitoring in AM to include acoustic emissions, high frame rate optimal imaging, and spatially resolved acoustic spectroscopy. Currently, feedback control using heterogeneous sensor signals in high throughput multi-laser metal AM systems is not achievable using existing facilities at TAMU. The proposed instrument will introduce a first-of-a-kind facility in the United States in a university setting. With the existing material characterization capabilities and scientific expertise at TAMU, the proposed instrument will significantly expedite the development of new alloys and subsequently accelerate the process of determining optimal processing windows for these alloys. Moreover, the computational infrastructure acquired through this project will enable the implementation of new machine learning-based algorithms to process large data streams fused from multiple heterogeneous sensors and potentially enable real-time control of the process. The suite of monitoring, characterization, and computational infrastructure developed through this project will result in a unique experimental testbed that integrates computational materials modeling with industrial internet of things (IIoT). Finally, the proposed instrument is highly beneficial to multiple active research projects funded by ARO and other federal agencies including NSF, ARL, and NASA, and industrial sponsors including Carpenter Tech, Baker Hughes, and Schlumberger.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2021
- Source ID
- W911NF2110036
Entities
People
- Alaa Mohamed Elwany
Organizations
- Army Contracting Command
- Texas Engineering Experiment Station
- United States Army