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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. 

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. 

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. 

This work concerns process monitoring in the laser powder bed fusion additive manufacturing process. In this work, we developed and applied a novel in-situ solution for process stability monitoring and flaw detection using acoustic emission sensing. Current process monitoring methods in laser powder bed fusion only focus on the top surface of the deposition process, using an array of sensors to capture data on a layer-by-layer basis. Common sensors used for in-situ monitoring of the laser powder bed fusion process are optical, infrared, and highspeed imaging cameras along with pyrometers and photodiodes. A critical flaw with traditional top surface monitoring methodologies is that they are unable to reliably monitor the subsurface phenomena that occur in the laser powder bed fusion process. These subsurface effects are caused by the meltpool penetrating multiple layers below the top surface, leading to the re-solidification of the microstructure and potentially generating keyhole porosity. By only monitoring the top surface of the laser powder bed fusion process, the meltpool depth aspects and effects are ignored. To overcome the limitations of current in-situ monitoring of subsurface effects, this work utilizes four passive acoustic emission sensors attached to the build plate. These acoustic emission sensors monitor the energy emissions generated from the surface-level laser material interactions. Moreover, the acoustic emission signals are capable of traveling through the previously deposited layers, through the build plate, and to the sensors. Therefore, the acoustic waveform generated by the laser can capture process phenomena ranging from the crystallographic level to the macro-scale layer level which are at the root of flaw formation inside the deposited part. Hence, acoustic emission monitoring has the ability to monitor the subsurface effects in the laser powder bed fusion process. To monitor and analyze this acoustic waveform, novel wavelet-based decomposition is combined with heterogeneous sensor fusion to not only capture the acoustic waveform in time, but also in locational space on the build plate. Locational acoustic emission data enables the ability to determine the source of the generated acoustic waveform which is advantageous when the location of flaws is desired. This extracted spatially placed acoustic waveform data is able to detect the effect of processing parameters with a statistical fidelity of 99%. The proposed locational acoustic waveform monitoring method correlates to the resulting surface roughness of manufactured samples with a fidelity of 86%. Additionally, we show that acoustic waveform monitoring detects the onset of part failure, recoater crashes, and warpage prior a priori to the actual failure point. 

Abstract The main research goal of this study is to decipher the intercorrelation between process-induced thermal-structure-property relationships of Stainless Steel 316L fabricated by laser powder bed fusion. The objective therein is achieved by explaining and quantifying the effect of processing parameters and part-scale thermal history on microstructure evolution and mechanical properties of these parts. Multiple previous works have correlated the effect of process parameters on flaw formation, microstructural features evolved and functional properties; however, a lack of understanding remains in the underlying effect of the thermal history on part microstructure and mechanical properties. The thermal distribution, or thermal history, of the part as it is being built layer-by-layer is influenced by the processing parameters, material properties and shape of the part. The thermal history influences the microstructure by changing the grain structure evolution, which affects the part properties. Therefore, the novelty of this paper lies in illuminating the process-thermal history-microstructure-property relationship in laser powder bed fusion. Characterization of tensile specimens processed at a variety of conditions reveal a direct influence of the choice of process parameters on the dendritic structure and the grain orientations. A high energy density leads to <100> textured columnar dendritic grains and low energy density leads to randomly oriented equiaxed grains as a result of the shifting heat influx. The tensile properties are correlated with the inherent microstructure. Through future work involving fracture surface analysis, the texture, grain size and porosity is expected to influence the inherent fracture mechanism. This work demonstrates that an understanding of thermal distribution within a printed part can inform the choice of processing conditions to generate the final microstructure as per the specified functional requirements. Thus, this paper lays the foundation for future prediction and control of microstructure and functional properties in laser powder bed fusion by identifying the root fundamental thermal phenomena that influences the microstructure evolution and part properties. 

We developed and applied a model-based feedforward control approach to reduce temperature-induced flaw formation in the laser powder bed fusion (LPBF) additive manufacturing process. The feedforward control is built upon three basic steps. First, the thermal history of the part is rapidly predicted using a mesh-free graph theory model. Second, thermal history metrics are extracted from the model to identify regions of heat buildup, symptomatic of flaw formation. Third, process parameters are changed layer-by-layer based on insights from the thermal model. This technique was validated with two identical build plates (Inconel 718). Parts on the first build plate were made under manufacturer recommended nominal process parameters. Parts on the second build plate were made with model optimized process parameters. Results were validated with in-situ infrared thermography, and materials characterization techniques. Parts produced under controlled processing exhibited superior geometric accuracy and resolution, finer grain size, and increased microhardness. 

The objective of this work is to predict a type of thermal-induced process failure called recoater crash that occurs frequently during laser powder bed fusion (LPBF) additive manufacturing. Rapid and accurate thermomechanical simulations are valuable for LPBF practitioners to identify and correct potential issues in the part design and processing conditions that may cause recoater crashes. In this work, to predict the likelihood of a recoater crash (recoater contact or impact) we develop and apply a computationally efficient thermomechanical modeling approach based on graph theory. The accuracy and computational efficiency of the approach is demonstrated by comparison with both non-proprietary finite element analysis (Abaqus), and a proprietary LPBF simulation software (Autodesk Netfabb). Based on both numerical (verification) and experimental (validation) studies, the proposed approach is found to be 5 to 6 times faster than the non-proprietary finite element modeling and has the same order of computational time as a commercial simulation software (Netfabb) without sacrificing prediction accuracy.