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US-20260124680-A1 - Method for Predictive Control of Laser Powder Bed Fusion

US20260124680A1US 20260124680 A1US20260124680 A1US 20260124680A1US-20260124680-A1

Abstract

A method for adaptive control of laser power during selective laser melting may include the steps of generating a toolpath for a part, calculating a mechanistic feature of the part, inputting a laser scanning speed and the calculated mechanistic feature of the part into a machine learning model, predicting meltpool temperature variations along the toolpath path based on the machine learning model, determining laser power adjustments based on predicted meltpool temperature variations. In some examples, the part may be generated from a metal, a ceramic, a polymer, or combinations thereof.

Inventors

  • Dominik Kozjek
  • Jon-Erik Mogonye
  • Conor Porter
  • Jian Cao
  • Fred M. Carter, III

Assignees

  • NORTHWESTERN UNIVERSITY

Dates

Publication Date
20260507
Application Date
20251104

Claims (20)

  1. 1 . A method for adaptive control of laser power during selective laser melting of comprising: generating a toolpath for at least one part; calculating at least one mechanistic feature of the at least one part; inputting a laser power and scanning speed and the calculated mechanistic feature of the at least one part into a machine learning model; predicting meltpool temperature variations along the toolpath path based on the machine learning model; determining laser power adjustments based on predicted meltpool temperature variations; and dynamically adjusting the laser power during a build process at specific points along the toolpath based on the predicted meltpool temperature variations to maintain a desired meltpool temperature profile; and generating the at least one part.
  2. 2 . The method of claim 1 , wherein the at least one part comprises a metal, a polymer, a ceramic, or combinations thereof.
  3. 3 . The method of claim 1 , wherein a portion of the at least one part comprises a metal, a polymer, a ceramic, or combinations thereof.
  4. 4 . The method of claim 1 , wherein the at least one mechanistic feature is calculated using a geometry of the part and the generated toolpath.
  5. 5 . The method of claim 1 , wherein a co-axial photodiode is used to collect meltpool spectral emissions during the build process.
  6. 6 . The method of claim 1 , wherein a thermal simulation model is used to determine laser power adjustments using a relationship between changes in laser power and meltpool temperature.
  7. 7 . The method of claim 1 , wherein meltpool temperature data is collected at least at 100 kHz.
  8. 8 . A system configured to generate a part in accordance with the method of claim 1 .
  9. 9 . The system of claim 8 , wherein the system comprises a 1070 nm wavelength, 400 W fiber laser.
  10. 10 . An adaptive laser power control system for selective laser melting configured to generate a part comprising: a machine learning model configured to predict optimal laser power settings based on a geometry of the part and a generated toolpath; a co-axial photodiode system for monitoring meltpool spectral emissions; a control algorithm that adjusts laser power dynamically along the toolpath, based on predicted meltpool temperatures from the machine learning model; and a laser.
  11. 11 . The system of claim 10 , wherein the part comprises a metal, a polymer, a ceramic, or combinations thereof.
  12. 12 . The system of claim 10 , wherein a portion of the part comprises a metal, a polymer, a ceramic, or combinations thereof.
  13. 13 . The system of claim 10 , wherein the control algorithm uses a relationship between laser power and predicted meltpool temperatures derived from a thermal simulation to dynamically adjust the laser power during a build process.
  14. 14 . The system of claim 10 , wherein the co-axial photodiode system utilizes a two-color pyrometry to calculate real-time meltpool temperatures.
  15. 15 . A method for manufacturing a part using selective laser melting, comprising: using a co-axial photodiode system to monitor real-time meltpool temperatures during a build process; implementing a machine learning model to predict laser power adjustments based on a geometry of the part and a generated toolpath; dynamically adjusting laser power during the build to optimize meltpool temperature; and generating the part.
  16. 16 . The method of claim 15 , wherein the part comprises a metal, a polymer, a ceramic, or combinations thereof.
  17. 17 . The method of claim 15 , wherein a portion of the part comprises a metal, a polymer, a ceramic, or combinations thereof.
  18. 18 . The method of claim 15 , wherein a control algorithm adjusts laser power dynamically along the generated toolpath based on predicted meltpool temperatures from the machine learning model and real-time meltpool temperatures during the build process.
  19. 19 . A non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising: generating a toolpath for a part for a selective laser melting build process; calculating at least one mechanistic feature of the part; inputting a laser scanning speed and the calculated at least one mechanistic feature of the part into a machine learning model; predicting meltpool temperature variations along the toolpath path based on the machine learning model; determining laser power adjustments based on predicted meltpool temperature variations; and dynamically adjusting the laser power during the build process at specific points along the toolpath based on the predicted meltpool temperature variations to maintain a desired meltpool temperature profile; and generating the part.
  20. 20 . The non-transitory machine-readable medium storing instructions of claim 20 , wherein the part comprises a metal, a polymer, a ceramic, or combinations thereof.

Description

CROSS REFERENCE TO RELATED APPLICATION(S) This application is a continuation of and claims priority to U.S. Application No. 63/715,910 entitled Method for Predictive Control of Laser Powder Bed Fusion filed on Nov. 4, 2024, which is incorporated by reference in its entirety. STATEMENT OF GOVERNMENT INTERESTS This invention was made with government support under grant numbers W911NF-21-2-0199 AND W911NF-20-2-0292 awarded by the U.S. Army Research Laboratory. The government has certain rights in the invention. FIELD OF THE INVENTION The present disclosure relates to selective laser melting (SLM), a type of additive manufacturing process, and specifically to adaptive laser power control guided by machine learning to optimize the quality of manufactured parts. BACKGROUND Selective Laser Melting (SLM), also known as Powder Bed Fusion (PBF), is a key technology in additive manufacturing, especially in industries that require highly precise and durable parts. Traditionally, SLM processes utilize static laser power settings, which fail to adapt to changing geometries and complex thermal behaviors during the manufacturing process. This can result in inconsistent quality, such as variations in porosity size, reduced material density, and defects in mechanical properties. While closed-loop control (CLC) methods have been developed to address these issues, they primarily respond to observed defects and are limited by low temporal resolution. Existing methods lack the ability to dynamically and preemptively adjust laser power during the build process, especially on a sub-layer or intra-layer basis. SUMMARY The system and methods disclosed herein are directed to an adaptive laser power control system for SLM that leverages real-time data from a co-axial photodiode monitoring system combined with a machine learning (ML) algorithm. The ML model, utilizing a random forest (RF) algorithm, predicts optimal laser power settings based on mechanistic features derived from the toolpath and geometry of a part to be manufactured. This adaptive control adjusts laser power dynamically during the build process, which results in improved material quality and reduced porosity. In another example, the system and methods disclosed herein are directed to an additive manufacturing process including the steps of calculating a prescribed toolpath for at least one part, and generating the at least one part via the prescribed toolpath via a variable-power processing system and an in-situ sensor, in which the variable-power system dynamically adjusts power during a build process at specific points along the prescribed toolpath based on a predicted toolpath temperature. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 depicts a schematic overview of the adaptive laser power control system, showing the workflow and a completed build with the part of interest circled in red (MPM-meltpool monitoring; GAMMA-Generalized Analysis of Multiscale Multiphysics Applications, a thermal simulation code). FIG. 2 graphically depicts the linear relationship between changes in laser power and changes in meltpool temperature, derived from thermal simulations. FIG. 3 depicts laser power corrections applied to a single layer during the SLM process. FIG. 4 shows XCT analysis of a baseline sample with external geometric accuracy displayed on the left and porosity shown as red dots on the right. FIG. 5 depicts a baseline sample compared with an adaptive 0.5× sample. FIG. 6 graphically depicts TEP distribution metric (TEP-DM) for baseline and controlled builds for the entire build height with moving average filter of 10 layers. FIG. 7 graphically depicts TEP distribution metric (TEP-DM) for baseline and controlled builds for layers 55-85. DETAILED DESCRIPTION Improved dynamic control of Selective Laser Melting (SLM), also referred to as Powder Bed Fusion (PBF), is crucial for its broader adoption in high-stakes industries. Currently, most commercially produced SLM parts are manufactured using static laser parameters that were developed on simple, constant cross-section witness coupons. However, these parameters, optimized for uniform geometries and toolpaths, do not effectively transfer to complex, real-world parts due to varying meltpool and thermal behavior influenced by intricate geometries and toolpaths. Addressing this discrepancy requires adaptive control mechanisms that can adjust laser parameters in real-time based on part geometry and toolpath variations during the build process. Previous research into SLM control has primarily focused on closed-loop control (CLC) methods. Cai et al. [1] provided a comprehensive review of control methodologies in SLM, which can be broadly categorized into inter-layer and intra-layer control approaches. Inter-layer control strategies typically adjust laser power for subsequent layers based on the heat accumulated in prior layers. For instance, Rezaeifar et al. [2] employed coaxial pyrometry to measure average layer temperatures, demonst