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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. 

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 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. 

This work discusses new methodologies for identifying the grain boundaries in color images of metallic microstructures and the quantification of their grain topology. Grain boundaries have a large impact on the macro-scale material properties. Particularly, this work employs the experimental microstructure data of Titanium-Aluminum alloys, which can be used for various aerospace components owing to their outstanding mechanical performance in elevated temperatures. The grain topology of these metallic microstructures is quantified using the concept of shape moment invariants. In order to capture the grains using the shape moment invariants, it is necessary to identify the grain boundaries and separate them from their respective grains. We present two methodologies to detect the grain boundaries. The first method is the tolerance-based neighbor analysis. The second method focuses on creating three-dimensional space of pixel intensity values based on the three color channels and measuring the Euclidean distance to separate different grains. Additionally, since the grain boundaries may not possess the same material properties as the grain itself, this work investigates the effect of including the grain boundaries when determining the homogenized material properties of the given microstructure. To generate adequate statistical information, microstructures are reconstructed from the experimental data using the Markov Random Field (MRF) method. Upon separating the grains, we use the shape moment invariants to quantify the shapes of different grains. Using the shape moment invariants and the experimental material property values, three neural network functions are developed to investigate the effects of grain boundaries on material property predictions.