Advanced Machines, Monitoring, and Control for Additive Manufacturing

Summary

Metal Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM) enables the direct production of functional metal components from three-dimensional digital designs in a single process. It integrates seamlessly with modern manufacturing by leveraging data, automation, and advanced process control. Despite its potential, the broader adoption of AM is hindered by two persistent challenges: inconsistent part quality and low production efficiency. This project addresses these issues by developing and implementing advanced control and monitoring methods, and by demonstrating their measurable impact on improving both quality and productivity. These innovations will be integrated into NIST’s AM machines and testbeds, providing a robust platform for evaluation and technology transfer.

The Advanced Machines, Monitoring, and Control (AMMC) project advances foundational measurement science, control strategies, and open-architecture testbed capabilities to enhance AM productivity, quality, and material performance. Efforts include real-time sensing, feedback control, and advanced scan strategies for LPBF and related processes. Through collaborations with national laboratories, industry, and academia, AMMC accelerates the development and adoption of next generation AM technologies with a focus on microstructure control, multi-material manufacturing, and process interoperability.

Description

Objective
To develop, integrate, and implement real-time feedback and other advanced process control techniques using monitoring tools such as high-speed imaging and diffraction for single- and multi-laser AM systems, enabling improved productivity, part quality, and material performance.

Technical Idea
Current AM control methods are largely adapted from machine tool technology, which is fundamentally different from AM’s additive nature. Machining starts with a homogeneous bulk material and is relatively insensitive to parameter changes, using line-wise control via G- and M-code. This approach cannot adapt continuously to local thermal conditions, limiting advanced scan strategies and quality improvements.

To overcome this, NIST developed a pointwise AM control method that varies laser power and speed at each point rather than along fixed lines. This enables continuous adaptation to local conditions and supports advanced scan strategies. The pointwise framework also serves as a measurement platform, synchronizing machine commands with in-situ sensor data for comprehensive process studies.

Initially implemented on the AMMT and later on the portable LPDT for synchrotron experiments, pointwise control has produced high-value datasets such as AM-Bench 2022 and many highly cited NIST AM data sets. With mature algorithms and hardware, it now supports three main deliverables:

1. Enhanced AM Research Platforms – AMMT 2.0 with dual-laser, real-time feedback; LPDT with 3D build and coaxial imaging.
2. Innovative Control/Monitoring Methods – Adaptive scan strategies to prevent defects, enable graded properties, and optimize microstructure.
3. High-quality Data Production – Consistent generation of datasets to advance research, modeling, and AM data management. 
    

Research Plan
The AMMC project focuses on developing, integrating, and validating novel control and monitoring capabilities, with demonstrations on the AMMT 2.0 and LPDT platforms.

1.  Advanced Real-Time Feedback Control – Develop two feedback approaches for AMMT 2.0: In-line melt pool control will use FPGA-processed melt pool imaging as the process variable, with laser power as the control variable, enabling sub-100 µs response times. Layer-wise feedforward control will use surface images or 3D profiles to create power masks for subsequent layers based on metrics such as powder layer thickness.
2. In-Situ High-Energy X-ray Measurements – Integrate high-contrast X-ray imaging and diffraction analysis with LPDT to resolve melt pool morphology, keyhole dynamics, and phase transitions during AM. These capabilities will support validation of control strategies such as elliptical scans for keyhole mitigation and phase engineering in alloys.
3. Temperature Field Control Strategies – Explore novel scan patterns (elliptical, Hilbert), inverse heat placement, and synchronized multi-laser operation to achieve targeted thermal profiles for microstructure control and defect mitigation, leveraging both physics-based simulations and machine learning.
4. Transient State Studies – Investigate process behavior during laser on/off events and galvo acceleration/deceleration, focusing on melt pool stability, local temperature fluctuations, and emissions such as plume and spatter. Insights will guide optimized scan parameter transitions in 3D builds.
5. Build Efficiency Enhancement – Study controlled keyhole-mode scanning to enable thicker powder layers and faster builds without porosity. Explore alternative scan patterns, such as elliptical scans, to maintain favorable melt pool geometries and keyhole stability.
6. Powder Denudation Reduction – Reduce powder loss and related defects by exploring pre-sintering with diffused beams, interleaving scan patterns, and wobbling secondary lasers. Monitor effects with in-situ X-ray imaging, pulsed laser illumination, and 3D laser profiling.
7. AM software and hardware development – Develop and release SAM software modules and LabVIEW-based controllers to support the development, adaptation, and deployment of next-generation pointwise AM control technologies.
8. Advanced Machine Metrology – Develop and implement methods for measuring laser power density distributions, multi-beam alignment, galvo synchronization, beam positioning accuracy, and power stability. Perform cross-comparisons across instruments and machines to establish best practices and standards for AM platform characterization.