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1. Predicting Microstructure Evolution in Laser Powder Bed Fusion Additive Manufacturing Using Physics-Based Machine Learning

   https://doi.org/10.1115/MSEC2024-125230  

   Riensche, Alexander; Bevans, Benjamin; King, Grant; Krishnan, Ajay; Cole, Kevin; Rao, Prahalada (June 2024, American Society of Mechanical Engineers)

   Abstract The long-term goal of this work is to predict and control microstructure evolution in metal additive manufacturing processes. As a step towards this goal, the objective of this paper is the rapid prediction of the microstructure evolution in parts made using the laser powder bed fusion (LPBF) additive manufacturing process. To realize this objective, we developed and applied an approach which combines physics-based thermal modeling with data-driven machine learning to predict two important microstructure-related characteristics in Nickel Alloy 718 LPBF-processed parts: meltpool depth and primary dendritic arm spacing (PDAS). Microstructure characteristics are critical determinants of functional physical properties, e.g., yield strength and fatigue life. Currently, the microstructure of laser powder bed fusion parts is optimized through a cumbersome and costly build-and-characterize empirical approach. This makes the development of rapid and accurate models for predicting microstructure evolution practically valuable: these models reduce process development time and enable fabrication of parts with consistent properties. Unfortunately, due to their computational complexity, existing physics-based models for predicting microstructure evolution are limited to only a few layers and are challenging to scale to practical parts. To overcome the drawbacks of current microstructure prediction techniques, this paper establishes a novel physics and data integrated modeling approach. The approach consists of two steps. First, a rapid, part-level computational thermal model was used to predict the temperature distribution and cooling rate in the entire part before it was printed. Second, the foregoing physics-based thermal history quantifiers were used as inputs to a simple machine learning model (support vector machine) trained to predict the meltpool depth and primary dendritic arm spacing based on empirical materials characterization data. As an example of its efficacy, when tested on a separate set of samples from a different build, the approach predicted the PDAS with root mean squared error ≈ 110 nm. The modeling approach was further able to predict meltpool depth with a root mean squared error of 0.012mm. This model performance was validated through the creation of 21 geometries created under 7 different process parameters. Optical and scanning electron microscopy was conducted resulting in more than 1200 primary dendritic arm spacing and meltpool depth measurements. Primary dendritic arm spacing predictions were also validated on parts of a unique geometry created in a separate work. The model was able to successfully transfer to this build without further training, indicating that this method is transferrable to other parts made with laser powder bed fusion and Nickel Alloy 718. This work thus presents an avenue for future physics-based optimization and control of microstructural evolution in laser powder bed fusion. 

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2. Rapid, Autonomous, and Shape-Agnostic Physics-Guided Thermal History Control to Improve Part Quality in Laser Powder Bed Fusion Additive Manufacturing

   https://doi.org/10.1115/1.4068695  

   Riensche, Alex; Deshmukh, Kaustubh; Carrington, Antonio; Darji, Mihir; Bevans, Benjamin; Snyder, Kyle; Sions, John; Plotnikov, Yuri; Cole, Kevin D; Rao, Prahalada (August 2025, Journal of Manufacturing Science and Engineering)

   This work concerns the laser powder bed fusion (LPBF) additive manufacturing process. We developed and implemented a physics-based approach for layerwise control of the thermal history of an LPBF part. Controlling the thermal history of an LPBF part during the process is crucial as it influences critical-to-quality characteristics, such as porosity, solidified microstructure, cracking, surface finish, and geometric integrity, among others. Typically, LPBF processing parameters are optimized through exhaustive empirical build-and-test procedures. However, because thermal history varies with geometry, processing parameters seldom transfer between different part shapes. Furthermore, particularly in complex parts, the thermal history can vary significantly between layers leading to both within-part and between-part variation in properties. In this work, we devised an autonomous physics-based controller to maintain the thermal history within a desired window by optimizing the processing parameters layer by layer. This approach is a form of digital feedforward model predictive control. To demonstrate the approach, five thermal history control strategies were tested on four unique part geometries (20 total parts) made from stainless steel 316L alloy. The layerwise control of the thermal history significantly reduced variations in grain size and improved geometric accuracy and surface finish. This work provides a pathway for rapid, shape-agnostic qualification of LPBF part quality through control of the causal thermal history as opposed to expensive and cumbersome trial-and-error parameter optimization. 

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3. Toward Rapid Process Qualification of Laser Powder Bed Fusion: Model Predictive Control of Part Thermal History

   https://doi.org/10.1115/MSEC2024-125241  

   Riensche, Alexander; Bevans, Benjamin; Sions, John; Snyder, Kyle; Plotnikov, Yuri; Hass, Derek; Rao, Prahalada (June 2024, American Society of Mechanical Engineers)

   Abstract This work pertains to the laser powder bed fusion (LPBF) additive manufacturing process. The goal of this work is to mitigate the expense and time required for qualification of laser powder bed fusion processed parts. In pursuit of this goal, the objective of this work is to develop and apply a physics-based model predictive control strategy to modulate the thermal history before the part is built. The key idea is to determine a desired thermal history for a given part a priori to printing using a physics-based model. Subsequently, a model predictive control strategy is developed to attain the desired thermal history by changing the laser power layer-by-layer. This is an important area of research because the spatiotemporal distribution of temperature within the part (also known as the thermal history) influences flaw formation, microstructure evolution, and surface/geometric integrity, all of which ultimately determine the mechanical properties of the part. Currently, laser powder bed fusion parts are qualified using a build-and-test approach wherein parameters are optimized by printing simple test coupons, followed by examining their properties via materials characterization and testing — a cumbersome and expensive process that often takes years. These parameters, once optimized, are maintained constant throughout the process for a part. However, thermal history is a function of over 50 processing parameters including material properties and part design, consequently, the current approach of parameter optimization based on empirical testing of simple test coupons seldom transfers successfully to complex, practical parts. Rather than instinctive process parameter optimization, the model predictive control strategy presents a radically different approach to LPBF part qualification that is based on understanding and modulating the causal thermal physics of the process. The approach has three steps: (Step 1) Predict – given a part geometry, use a rapid, mesh-less physics-based simulation model to predict its thermal history, analyze the predicted thermal history trend, isolate potential red flag problems such as heat buildup, and set a desired thermal history that corrects deleterious trends. (Step 2) Parse – iteratively simulate the thermal history as a function of various laser power levels layer-by-layer over a fixed time horizon. (Step 3) Select – the laser power that provides the closest match to the desired thermal history. Repeat Steps 2 and 3 until the part is completely built. We demonstrate through experiments with various geometries two advantages of this model predictive control strategy when applied to laser powder bed fusion: (i) prevent part failures due to overheating and distortion, while mitigating the need for anchoring supports; and (ii) improve surface integrity of hard to access internal surfaces. 

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4. In-Process Monitoring of Part Quality in Laser Powder Bed Fusion Additive Manufacturing Process using Acoustic Emission Sensors

   https://doi.org/10.1115/1.4067848  

   Bevans, Benjamin; Riensche, Alexander; Carrington, Antonio; Deshmukh, Kaustubh; Darji, Mihir; Plotnikov, Yuri; Sions, John; Snyder, Kyle; Hass, Derek; Rao, Prahalada (February 2025, Journal of Manufacturing Science and Engineering)

   Abstract In this work, we used in-situ acoustic emission sensors for online monitoring of part quality in laser powder bed fusion (LPBF) additive manufacturing process. Currently, sensors such as thermo-optical imaging cameras and photodiodes are used to observe the laser-material interactions on the top surface of the powder bed. Data from these sensors is subsequently analyzed to detect onset of incipient flaws, e.g., porosity. However, these existing sensing modalities are unable to penetrate beyond the top surface of the powder bed. Consequently, there is a burgeoning need to detect thermal phenomena in the bulk volume of the part buried under the powder, as they are linked to such flaws as support failures, poor surface finish, microstructure heterogeneity, among others. Herein, four passive acoustic emission sensors were installed in the build plate of an EOS M290 LPBF system. Acoustic emission data was acquired during processing of stainless steel 316L samples under differing parameter settings and part design variations. The acoustic emission signals were decomposed using wavelet transforms. Subsequently, to localize the origin of AE signals to specific part features, they were spatially synchronized with infrared thermal images. The resulting spatially localized acoustic emission signatures were statistically correlated (R2 > 85%) to multi-scale aspects of part quality, such as thermal-induced part failures, surface roughness, and solidified microstructure (primary dendritic arm spacing). This work takes a critical step toward in-situ, non-destructive evaluation of multi-scale part quality aspects using acoustic emission sensors. 

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5. Thermal modeling in metal additive manufacturing using graph theory – Application to laser powder bed fusion of a large volume impeller

   Yavari, Reza; Williams, Richard; Riensche, Alex; Hooper, Paul; Cole, Kevin; Jacquemetton, Lars; Halliday, Harold; Rao, Prahalada (May 2021, Additive manufacturing)

   null (Ed.)

   Despite its potential to overcome the design and processing barriers of traditional subtractive and formative manufacturing techniques, the use of laser powder bed fusion (LPBF) metal additive manufacturing is currently limited due to its tendency to create flaws. A multitude of LPBF-related flaws, such as part-level deformation, cracking, and porosity are linked to the spatiotemporal temperature distribution in the part during the process. The temperature distribution, also called the thermal history, is a function of several factors encompassing material properties, part geometry and orientation, processing parameters, placement of supports, among others. These broad range of factors are difficult and expensive to optimize through empirical testing alone. Consequently, fast and accurate models to predict the thermal history are valuable for mitigating flaw formation in LPBF-processed parts. In our prior works, we developed a graph theory-based approach for predicting the temperature distribution in LPBF parts. This mesh-free approach was compared with both non-proprietary and commercial finite element packages, and the thermal history predictions were experimentally validated with in- situ infrared thermal imaging data. It was found that the graph theory-derived thermal history predictions converged within 30–50% of the time of non-proprietary finite element analysis for a similar level of prediction error. However, these prior efforts were based on small prismatic and cylinder-shaped LPBF parts. In this paper, our objective was to scale the graph theory approach to predict the thermal history of large volume, complex geometry LPBF parts. To realize this objective, we developed and applied three computational strategies to predict the thermal history of a stainless steel (SAE 316L) impeller having outside diameter 155 mm and vertical height 35 mm (700 layers). The impeller was processed on a Renishaw AM250 LPBF system and required 16 h to complete. During the process, in-situ layer-by-layer steady state surface temperature measurements for the impeller were obtained using a calibrated longwave infrared thermal camera. As an example of the outcome, on implementing one of the three strategies reported in this work, which did not reduce or simplify the part geometry, the thermal history of the impeller was predicted with approximate mean absolute error of 6% (standard deviation 0.8%) and root mean square error 23 K (standard deviation 3.7 K). Moreover, the thermal history was simulated within 40 min using desktop computing, which is considerably less than the 16 h required to build the impeller part. Furthermore, the graph theory thermal history predictions were compared with a proprietary LPBF thermal modeling software and non-proprietary finite element simulation. For a similar level of root mean square error (28 K), the graph theory approach converged in 17 min, vs. 4.5 h for non-proprietary finite element analysis. 

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