In-situ monitoring of laser powder bed fusion applied to defect detection

Additive manufacturing technologies, particularly laser powder bed fusion (LPBF), have received much attention recently due to their numerous advantages over conventional manufacturing methods. However, the use of LPBF is still quite restricted, mainly due to two factors: its typically low productivity, which makes the technology less competitive in applications with moderate to high production volumes, and its limited reliability, particularly relevant for applications where high performance is required from the materials.The issue of low productivity is addressed in this thesis by adjusting the main LPBF process parameters. An equation for the build rate was formulated based on these parameters, determining their contributions and enabling strategies for build rate maximization. The changes in microstructure and defect populations associated with increasing productivity were determined.The reliability issue was explored by investigating defect formation, detectability and mitigation, since a major factor compromising reliability and materials’ performance is the presence of defects. Internal defects were deliberately created in LPBF-manufactured material to assess their detectability via in-situ monitoring. Two main routes of deliberate defect formation have been identified while preserving defect formation mechanisms; therefore, this thesis can be divided into two parts according to the approach employed to create defects.Defects are generated systematically if suboptimal process parameters are employed. The types, quantities, and sizes of defects in nickel-based alloy Hastelloy X resulting from varying processing conditions were thoroughly characterized. Analyzing data obtained from in-situ monitoring made it possible to distinguish virtually defect-free material from defective material.Defects are generated stochastically due to the redeposition of process by-products on the powder bed. With the aid of in-situ monitoring data, the presence of these defects can be inferred from the detection of the process by-products responsible for their formation. The comparison of data obtained in-situ with data obtained through ex-situ material characterization allowed determining how precisely detections corresponded to actual defects. The impact of these defects on the mechanical properties of Hastelloy X was assessed. A couple of in-process mitigation strategies were investigated, and their performances were evaluated. By establishing means to use LPBF process monitoring to distinguish high-quality from defective material and detect random, unavoidable defects, this thesis enables the prediction of LPBF material quality. It creates conditions necessary for the first-time-right production of defect-free material at increased build rates

Detection and classification of internal flaws in laser powder bed fusion: application of in-situ monitoring for quality control of Hastelloy X builds

Additive manufacturing technologies, in particular laser powder bed fusion (LPBF), have received much attention in recent years due to their multiple advantages over traditional manufacturing. Yet, the usage of additively manufactured products is still quite limited, mainly due to two factors: the low repeatability, which is particularly relevant for applications where high performance is required from the materials, and the typically low productivity, particularly relevant for products with a substantial production volume.The main factor that affects repeatability and compromises the performance of the materials is the presence of flaws. Hence, to assess the quality of a product and to predict its performance, it is crucial to recognize which flaws are present and ensure their detectability. Moreover, if the flaws can be detected during the manufacturing process, corrective actions can be taken. In this thesis, internal flaws were deliberately created in LPBF manufactured material to assess their detectability via in-situ monitoring. Two main routes of deliberate flaw formation have been identified while preserving flaw formation mechanisms; therefore, this thesis is split into two parts, according to the approach employed to create flaws.Flaws are generated systematically if inadequate process parameters are employed. By varying the processing conditions, different types, amounts and sizes of flaws are created. By monitoring the manufacturing process with long-exposure near-infrared imaging and applying supervised machine learning, it was possible to distinguish process conditions that generate the different flaw categories with accuracy, precision and recall of at least 96%.Flaws are created stochastically as a result of the redeposition of process by-products on the build area. It was found that substantial amounts of flaws can be provoked through this route when increasing the nominal layer thickness in the build, thus enabling the validation of the monitoring system in their detection. After applying an image analysis algorithm to all the images output from in-situ monitoring in three builds, it was possible to identify trends in the spatial distribution of spatter redeposits. Ex-situ inspection and material characterization provided cross-check for the distribution of flaws.The low productivity of LPBF makes it less competitive in applications with moderate to high production volumes. This issue is briefly addressed in this thesis. Even though one of the main approaches to increase productivity is to tune the main process parameters, dissimilar strategies were identified in the literature towards this goal. Thus, parametrization of build rates was done and applied to the processing conditions deemed to provide material with acceptable quality, based on the quantity and types of flaws present. The material manufactured in these conditions was characterized, and it was found that substantially different microstructures can be achieved within the process window, depending on the build rate

Linking in situ melt pool monitoring to melt pool size distributions and internal flaws in laser powder bed fusion

In situ monitoring of the melt pools in laser powder bed fusion (LPBF) has enabled the elucidation of process phenomena. There has been an increasing interest in also using melt pool monitoring to identify process anomalies and control the quality of the manufactured parts. However, a better understanding of the variability of melt pools and the relation to the incidence of internal flaws are necessary to achieve this goal. This study aims to link distributions of melt pool dimensions to internal flaws and signal characteristics obtained from melt pool monitoring. A process mapping approach is employed in the manufacturing of Hastelloy X, comprising a vast portion of the process space. Ex situ measurements of melt pool dimensions and analysis of internal flaws are correlated to the signal obtained through in situ melt pool monitoring in the visible and near-infrared spectra. It is found that the variability in melt pool dimensions is related to the presence of internal flaws, but scatter in melt pool dimensions is not detectable by the monitoring system employed in this study. The signal intensities are proportional to melt pool dimensions, and the signal is increasingly dynamic following process conditions that increase the generation of spatter

A neural network for identification and classification of systematic internal flaws in laser powder bed fusion

Quality control of mechanical components is crucial to ensure their expected performance and prevent their failure. For components manufactured additively, quality control performed in-process is particularly interesting, as the sequential deposition and remelting of layers represent a possibility to mitigate existing flaws. The first step towards closed-loop control is to ensure that the monitoring setup and the data analytics approach can flag and discriminate flaws. This study aims to assess the potential of a layerwise monitoring system associated with a supervised machine learning approach to identify and classify internal flaws in laser powder bed fusion of Hastelloy X. For that, systematically generated internal flaws were mapped ex-situ in 72 distinct process conditions. The outputs of the near-infrared long-exposure acquisition system were labeled according to the ex-situ characterization and used to train a fully convolutional neural network. The network was then used to classify previously unseen monitoring images into three classes, according to the predominant flaw type expected, lack of fusion, keyhole porosity, or residual porosity. Accuracy, precision and recall over 96% are obtained, indicating that the monitoring system combined with this supervised machine learning approach successfully identifies and classifies internal flaws

Surface chemical analysis of spatter particles generated in laser powder bed fusion of Hastelloy X in process atmospheres with high and low oxygen content

Additive manufacturing, particularly laser powder bed fusion (LPBF), has received much attention in recent years because of its multiple benefits over traditional manufacturing. One of the key factors affecting the repeatability and performance of the materials is the existence of defects. Defects can be driven by process by-products called spatters, which consist of particles covered by an oxide layer formed during their travel time in the process atmosphere. As a standard process atmosphere consists of argon containing a residual oxygen level of around 0.1%, one possible way of addressing spatter-driven defect formation is by reducing the oxygen level, thereby reducing the oxidation of the spatter particles. In this study, Hastelloy X powder was processed by means of LPBF in an argon atmosphere containing 1000-ppm O2 or 50-ppm O2. Spatter particles were collected in a controlled manner, allowing sampling particles of different sizes, which were analyzed in terms of their surface chemical composition by means of X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). By combining these two tools, a comprehensive assessment of the surface chemical composition was conducted, taking advantage of XPS for the evaluation of the overall surface chemistry and of Auger nanoprobe analysis for high lateral analytical resolution combined with depth profiling. It is shown that tighter oxygen control will both limit the overall oxidation and affect the surface chemical composition. At regular O2 level in the process atmosphere (1000 ppm), spatter particles are covered by a 70-nm-thick oxide layer, on average. The thickness is substantially greater than that measured in spatter particles collected from the process atmosphere containing 50-ppm oxygen, which averages 6\ua0nm and is comparable with that of the virgin powder, thus revealing a potential for defect mitigation through control of the process atmosphere. Nonetheless, substantial differences in the surface chemical composition were identified between spatters and virgin powder, notably with the appearance of Al- and Ti-oxides on spatter particles, revealing the influence of the manufacturing process on surface characteristics

Increasing productivity of laser powder bed fusion manufactured Hastelloy X through modification of process parameters

One of the factors limiting the use of additive manufacturing, particularly powder bed processes, is their low productivity. An approach to increasing laser powder bed fusion (LPBF) build rate without costly hardware modifications is to alter process parameters. This study evaluates the possibilities to increase build rates through this route without compromising material quality. Equations for productivity are derived based on process parameters and build geometry, and applied on the process window for Hastelloy X in LPBF. It is demonstrated that virtually flaw-free parts can be printed at build rates that differ up to tenfold. To investigate potential variations in the microstructure and performance, Hastelloy X specimens manufactured at varying build rates were characterized. Electron backscattered diffraction (EBSD) analysis revealed that the specimen built at the lowest rate shows strong texture with columnar grains, while the specimen built at the highest rate presents significantly more random orientation and evident melt pool contours with pockets of very fine grains at the bottom. Despite the major differences in microstructure, the tensile properties do not necessarily vary substantially. Thus, the results indicate that the build rate of LPBF Hastelloy X can be significantly varied based on process parameters, still yielding consistent mechanical properties

In-situ detection of stochastic spatter-driven lack of fusion: Application of optical tomography and validation via ex-situ X-ray computed tomography

The presence of random defects in laser powder bed fusion (LPBF) parts is an issue that challenges the reliability of this manufacturing process and hinders its employment in structural, defect-sensitive components. A potential solution to increase the reliability of LPBF is employing in-process monitoring targeting defect detection. This study aims to detect stochastic defects driven by spatter particles via in-situ monitoring and validate the detection method ex-situ via X-ray computed tomography (XCT). By means of in-situ optical tomography (OT), monitoring images were registered layerwise during the manufacturing of Hastelloy X specimens. The images were analyzed to detect spatters landing within specimen boundaries, and the spatial coordinates of the detections were obtained. The specimens were also measured ex-situ by means of XCT, from which key features and coordinates of internal defects were obtained. The in-situ spatter detection method was then compared to the XCT measurements. It was found that 79 % of lack of fusion defects were detected in OT images. The detection was particularly successful for large defects. Spatter-induced lack of fusion defects were present in the specimens manufactured with optimized processing parameters in different degrees, depending on the robustness of the processing conditions to spatters. This study demonstrates the applicability of optical tomography in-situ monitoring for indirect detection of stochastic lack of fusion, whose presence is inferred from spatter redeposits on the powder bed

Effect of layer thickness on spatters oxidation of Hastelloy X alloy during powder bed fusion-laser beam processing

This study investigates the impact of powder layer thickness on spatter generation and oxidation behavior during the processing of Hastelloy X. In-situ monitoring using optical tomography reveals that thicker powder layers result in a higher number of hot spatters generated during laser-melt-powder interaction. Scanning electron microscopy and Auger electron spectroscopy analysis demonstrate the presence of different types of spatters that oxidize differently depending on their origin. X-ray photoelectron spectroscopy analysis further shows that the surface enrichment of oxide-forming elements such as Al, Ti, Cr, and Fe varies with the type of spatter particle. Additionally, depth profile analysis using X-ray photoelectron spectroscopy indicates that the average oxide layer thickness increases from ∼2.5 nm in virgin to ∼68 nm in spatters generated at 150 μm powder layer thickness. The findings suggest that powder layer thickness is a crucial factor in controlling spatter generation and oxidation behavior during powder bed fusion-laser beam processing

In-situ detection of redeposited spatter and its influence on the formation of internal flaws in laser powder bed fusion

The pervasive adoption of laser powder bed fusion (LPBF) as an industrial manufacturing technique relies on the improvement of its repeatability, currently limited by the stochastic formation of flaws. Considering that large flaws can form randomly and despite the optimization of process parameters, an in-situ monitoring technique suitable for detecting deviations that originate these critical flaws is paramount. The redeposition of spatters on the build area has previously been identified as one of the factors responsible for the rise of internal flaws, but so far limited are the efforts towards their detection. This study aims to detect spatter redeposits via in-situ monitoring and to couple the detections to lack of fusion. For that, long-exposure near-infrared in-situ monitoring associated with image analysis is employed to determine the exact locations and quantify the incidence of spatter redeposits across three full builds performed at varying layer thicknesses. The existence and distribution of internal flaws is verified ex-situ by means of ultrasonic inspection and metallography. The formation of internal flaws is attributed to spatter redeposits after detailed characterization of size, particle and surface morphology of spatter and identification of particles with identical characteristics on the fracture surface in the adjacencies of lack of fusion. It is found that spatters preferentially redeposit on the adjacencies of the gas outlet, but that the affected portion of the build area and the prevalence of detections is heavily dependent on the powder layer thickness employed in the manufacturing process. The monitoring system setup preferentially acquires signal from spatters redeposited on print regions, making it particularly suitable for flaw detection

Pixel Intensity Of Near-Infrared Long-Exposure Images Acquired In-Situ As A Quality Control Tool In Laser Powder Bed Fusion Of Ni-Base Hastelloy X

Reliability improvement of laser powder bed fusion relies on implementing in-situ process monitoring and control. One of the monitoring systems that has shown promising advances towards this goal is optical tomography based on long-exposure near-infrared layerwise imaging. The system outputs grayscale images that require adequate processing to flag undesirable deviations. This study aims to assess the viability of analysis of pixel intensity values to identify systematic internal defects. Hastelloy X specimens were manufactured with varying process parameters to generate varying defect types and contents. The outputs of the monitoring system were used for estimating the defect content in the specimens. The results show the suitability of pixel intensity analysis to discriminate processability and occurrence of systematic defects to have limited applicability, which is attributed to the predominant influence of process parameters in the pixel intensity values