Search for: All records

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 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. 

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. 

Abstract The objective of this work is to provide experimental validation of the graph theory approach for predicting the thermal history in additively manufactured parts that was recently published in the ASME transactions. In the present paper the graph theory approach is validated with in-situ infrared thermography data in the context of the laser powder bed fusion (LPBF) additive manufacturing process. We realize this objective through the following three tasks. First, two types of test parts (stainless steel) are made in two corresponding build cycles on a Renishaw AM250 LPBF machine. The intent of both builds is to influence the thermal history of the part by changing the cooling time between melting of successive layers, called interlayer cooling time. Second, layer-wise thermal images of the top surface of the part are acquired using an in-situ a priori calibrated infrared camera. Third, the thermal imaging data obtained during the two builds were used to validate the graph theory-predicted surface temperature trends. Furthermore, the surface temperature trends predicted using graph theory are compared with results from finite element analysis. As an example, for one the builds, the graph theory approach accurately predicted the surface temperature trends to within 6% mean absolute percentage error, and approximately 14 Kelvin root mean squared error of the experimental data. Moreover, using the graph theory approach the temperature trends were predicted in less than 26 minutes which is well within the actual build time of 171 minutes. 

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.