Powder Bed Fusion MPC for Internal Temperature Control
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Solution Overview
Problem
Powder bed fusion (PBF) processes lack advanced process monitoring tools for internal temperature measurement, leading to defects such as residual stresses, porosity, and anisotropy due to incomplete temperature history data, which are crucial for predicting material properties and phase formation.
Innovation Solution
Implementing a model predictive control (MPC) system using an Ensemble Kalman Filter (EnKF) to estimate internal temperatures by discretizing the part geometry into finite units, applying a thermal transport model, and using a Kalman filter to adjust control inputs based on noise distributions and temperature measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote thermal sensing is used to monitor temperature during PBF process, then temperature data can be obtained, but internal temperature distributions cannot be measured because fused metal is opaque to infrared transmission
Solution Approach 1:
The patent introduces an intermediary computational thermal model that acts as a mediator between the limited surface temperature measurements and the desired internal temperature distribution. The model uses surface temperature data from pyrometers as boundary conditions and computes the internal temperature field through heat transfer equations, effectively bridging the measurement gap caused by metal opacity to infrared radiation.
2Ease of manufacture
If no advanced process monitoring tools are used, then the PBF system operates simply, but defects such as residual stresses, porosity, and anisotropy occur due to incomplete temperature history data
Solution Approach 1:
The patent implements a feedback control system where the computational thermal model continuously predicts internal temperature distributions based on current process parameters and surface measurements. This temperature history information feeds back to detect and mitigate defects by comparing predicted temperatures against acceptable ranges, enabling quality control without requiring complete direct measurement of all internal temperatures.
Solution Approach 2:
The system performs preliminary computational analysis of temperature distributions before defects manifest. By using the thermal model to predict internal temperature fields in advance based on current process conditions, the system can identify potential defect formation and adjust process parameters proactively, preventing residual stresses and porosity before they occur.
3Loss of information
If direct internal temperature measurements are implemented, then complete temperature history can be obtained, but the system complexity increases significantly and direct measurement is not possible during the PBF process
Solution Approach 1:
The patent creates a computational copy or virtual model of the internal temperature field rather than attempting physical direct measurement. The thermal model replicates the thermal behavior of the actual part by solving heat transfer equations with boundary conditions from surface measurements, providing a virtual temperature history that mirrors what direct measurement would reveal without the associated complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables closed-loop process monitoring and control, reducing defects by accurately predicting internal temperature distributions and allowing for real-time adjustments to achieve desired material properties.
Implementation Method 1
selectively melting a pattern of desired geometry into the powder by application of a high-powered laser or electron beam
Implementation Method 2
heat transport between elements
Implementation Method 3
a thermal transport model that predicts heat distribution and temperature changes within the part
Implementation Method 4
describes heat transport between elements
Implementation Method 5
a measurement of a temperature of the part during the process at specific locations
Data Source
AI summary
Estimation algorithms, methods, and systems are provided that estimate the internal temperatures inside of a part being built using powder bed fusion (PBF). Closed-loop state estimation is applied to the problem of monitoring temperature fields within parts during the PBF build process. A simplified linear time-invariant (LTI) model of PBF thermal physics with the properties of stability, controllability and observability is presented. In some aspects, Model Predictive Control (MPC) may be used as an expanded application of an Ensemble Kalman Filter (EnKF) methods to control the PBF process. MPC is used to forecast the PBF build process behavior N time steps into the future and identifies inputs that drive the temperature of a corresponding node in a mesh of n nodes towards a predetermined target temperature.