Defects In Additive Manufacturing Research Articles

Minimizing Defects in Additive Manufacturing and Laser Welding Through Energy Coupling Experiments and Dynamic Modelling

Additive manufacturing is a revolutionary technology that produces complex components as a single piece, replacing traditional assemblies and reducing waste. Laser powder bed fusion, a type of additive manufacturing, allows for limitless design freedom by building intricate parts layer-by-layer through melting and fusing metal powder. However, build quality remains inconsistent, with deformations like pores and spattering occurring. To improve modelling of the behaviour of melted metal, thus minimizing defects, an experiment which determined the metal’s absorptance of laser energy through integrating sphere radiometry was conducted. The integrating sphere, which spatially integrated a fraction of measured light to calculate full radiance, had to sit above the build plate and not interfere with powder spreading. I designed two parts which attached the integrating sphere to the motorized powder spreading blade. Utilizing small ridges in the enclosure as weight-bearing tracks, the blade’s torque was reduced while allowing precise movement of the sphere. Through implementing these parts, the experiment was conducted, whose results will aid in our understanding of laser-metal interactions. Defects like spattering also occur during laser welding, due to the vaporization of unstable liquid metal. To combat this, novel beam shapes have been seen to increase the stability of the keyhole. To model this effect, I created a phenomenological, dynamic model of the keyhole depth during a copper weld. Two differential equations were created to model the keyhole depth’s rate of change using a dual mode and single mode laser, depending on absorptance and incident laser intensity. The dynamic depth of the keyhole for both modes was numerical solved in Python and iterated using existing copper weld data. Through better quantitative understanding of energy coupling and numerical modelling of the resulting dynamics, defects in laser welding and additive manufacturing can be minimized.

Research on the laser quenching performance of Ti6Al4V-7.3 %Cu alloys fabricated by wire-powder collaborative arc additive manufacturing

In this work, a new arc wire powder synergistic additive manufacturing method was used to prepare Ti6Al4V-7.3 %Cu alloys. This method overcomes the inherent large columnar grain defects in additive manufacturing and the segregation phenomenon of titanium alloys. Three laser quenching processes were also used to surface-treat the Ti6Al4V-7.3 %Cu alloys, and the hardness, microstructure and corrosion properties were studied. The results showed that laser quenching can effectively refine the grains and improve the strength and corrosion resistance. A β to α phase transition occurred, and Ti2Cu was formed. When the β phase is transformed into the α phase, there is an obvious Burgers orientation relationship between the two phases, and the orientation of the α phase in the quenched layer is more concentrated in the build direction (BD). With increasing laser quenching power, the Ti2Cu content in the alloy first increased and then decreased. When the quenching power was 690 W, the performance of the alloy was the best. Compared with those of the as-fabricated sample, the hardness increased by 150 HV, the grain size decreased from 10 µm to 1 µm, and the corrosion potential (Ecorr) increased by 0.195 V. α-Ti + Ti2Cu exhibited better corrosion resistance than β-Ti + Ti2Cu.

Diamond machining of additively manufactured Ti6Al4V ELI: Newer mode of material removal challenging the current simulation tools

Single point diamond machining (SPDM) produces smooth machined surfaces that other production methods cannot match. While the mechanics of machining of cast alloys with SPDM is well-explored, the realm of SPDM for additively manufactured parts remains largely uncharted. This work reveals new insights into the surface generation process of an additively manufactured titanium alloy, specifically, a Ti6Al4V Extra Low Interstitials (ELI) alloy workpiece. Our examination of the chip morphology unveiled a distinct mode of chip removal, previously unrecorded in existing literature. During SPDM of additively made Ti6Al4V ELI workpiece, identification of numerous pores and discontinuities in the chips flowing on the tool rake face, indicating periodic intermittent cracking during the material's plastic flow was seen. To examine this phenomenon, a finite element analysis (FEA) model was developed. While the FEA model can well explain the machining mechanics and chip morphology of SPDM of cast Ti6Al4V ELI reported in the literature, it failed to describe the chip morphology that are obtained during machining of additively made workpiece in this work. This disparity underscores the need for innovative simulation approaches tailored for additively manufactured components. The experimental observations in this study highlight a unique form of chip formation in contrast to conventional Ti6Al4V alloy machining processes. At lower feeds, there was a presence of short, discontinuous chip formation with tearing at the outer periphery. Conversely, at higher feeds, a long, continuous ribbon-like chip formation was observed. In addition, some typical additive manufacturing defects appear on the machined surface and chips. Through optimisation of the SPDT parameters, a surface roughness (Ra) value of about 11.8 nm was achieved on additively manufactured Ti6Al4V ELI workpiece. This work provides a fresh perspective on the mechanics of SPDM for additively manufactured components, offering a stepping stone for subsequent studies.

AutoML-driven diagnostics of the feeder motor in fused filament fabrication machines from direct current signals

AbstractPart defects in additive manufacturing are more frequent compared to machining or molding. Failures can go unnoticed for hours, wasting resources and extending process cycle times. This paper describes a Machine Learning based method for automated sensing of onset failure in additive manufacturing machinery. Investigations are conducted on a Fused Filament Fabrication (FFF) 3D printer, and the same methods are then applied to a digital light processing 3D printer. The investigation focuses on signal-based analysis, specifically passive sensing of stepper motors relating DC current measurements to the torque on a stepper, as opposed to any active acoustic interrogation of the part. Passive methods are used to characterize the loading on a feeder stepper in an FFF machine, forming a model that can identify early signs of filament-based failure with 85.65% 10-fold cross-validation accuracy. Efforts show filament breakage can be detected minutes before material runout would cause a defect, allowing ample time to pause, correct, or control the print. The machine learning pipeline was not naively conceived but optimized through automated machine learning.

Structure and its defects in additive manufacturing of stainless steels by laser melting and electric arc surfacing

Currently, there is an active development and study of additive technologies. Metal 3D printing makes it possible to obtain parts and structures of complex configuration using a minimum of shaping operations, which can lead to a reduction in overall cost of the resulting products. In this paper, we studied the structure formation in manufacture of products made of stainless steels 10Cr12Ni10Ti (analogue of AISI 321) and 08Cr18Ni9 (analogue of AISI 304) by additive methods – SLM (Selective Laser Melting) and WAAM (Wire Arc Additive Manufacturing). In the course of microstructural analysis, it was found that during the manufacture of products using SLM technology, small austenitic grains oriented in the direction of heat removal are formed, and with WAAM method, austenite is formed mainly in form of dendrites. It is shown that porosity is formed during manufacture of the samples by SLM method, which is associated with non-melting of individual powder particles. When implementing additive manufacturing by WAAM (electric arc surfacing), there is no increased porosity. In the course of the study, a new defect of the structure formed during the manufacture of products by both methods was revealed – formation of interface boundaries between layers, which is associated with the technology of additive manufacturing itself. When manufacturing a WAAM product, it manifests itself more clearly than when obtaining metal by SLM. Boundaries of the surfacing rollers in the manufacture of products by SLM accumulate various intermetallides and structural defects more intensively, relative to WAAM. As a result of the small relative volume of one surfacing roller, compared with WAAM, accumulation of these defects and intermetallides can act as an effective barrier during movement of dislocations, which can lead to an increase in the strength properties of products.

Numerical investigation of the influence of defects on the multiaxial fatigue strength of additively manufactured alloys

Laser Powder Bed Fusion (L-PBF) is one of the Additive Manufacturing (AM) techniques that have revealed salient advantages in enabling the fabrication of 3D parts with intricate shapes and added functionalities. Despite many advances, scientific challenges still exist. One of them, especially where the additively manufactured industrial components undergo fatigue loading, is the defects (gas pores and Lack of Fusions (LoFs)) that are induced during the fabrication process. The present work aims to investigate the influence of AM defects on fatigue strength under multiaxial loading conditions. From numerical simulations, the key question addressed is the impact of defects’ morphology on their criticality under multiaxial loadings. Finite Element (FE) elastic simulations at load ratio R = -1 under multiaxial loading conditions (tension, torsion and tension-torsion) have been performed on numerically generated spherical defect and LoFs obtained from micro-CT scans of additively manufactured TA64 alloy. The Crossland criterion (stress-based) was used to evaluate the fatigue strength and the obtained numerical results were compared with the available experimental results.

A novel defect-related cyclic damage model driven by strain energy density for LPBF titanium alloy

The unavoidable defects in additive manufacturing (AM) material have an impact on the deformation behavior and fatigue lifetime. In this study, a series of experiments on Laser powder bed fusion (LPBF) Ti-6Al-4 V under symmetrical tension–compression cyclic loading were conducted, and the corresponding damage mechanism was then investigated. It is revealed that due to the nucleation, growth, and coalescence of micro-voids, LPBF Ti-6Al-4 V exhibits accelerated softening behavior after reaching cyclic stability. Based on the physical mechanism, a cyclic damage model with a critical initiation criterion was developed using fracture mechanics principles, in which plastic strain energy as the driving parameter is introduced to describe the defect evolution, and the void’s closure effect during the tension–compression cycle is considered. Experimental validation has demonstrated that the damage model can accurately predict the cyclic stress–strain curves for LPBF Ti-6Al-4 V vertical and horizontal specimens, with errors in predicting crack initiation and growth lifetime falling within the ± 1.6 and ± 2.5 scatter bands.

Evaluation of defect-related fatigue performance of additive manufacturing GH4169 via small punch test

Suffering from irregularly distributed defects, the fatigue performance of additive manufacturing (AM) GH4169 was scattered and difficult to evaluate. Therefore, the micro-scale small punch fatigue (SPF) test was employed on the deposited and heat-treated GH4169. Convolutional neural networks (CNN) and image processing technology (IPT) were used to extract defect features of SPF samples, respectively. The effects of AM defects on SPF life were analyzed via mathematical models, and the results showed that defect size damaged SPF life the most. Various machine learning (ML) algorithms, including regression based on deep learning (RDL), support vector regression (SVR), and random forest (RF) were employed to anticipate SPF life. Combined with defects features from CNN and IPT, the SVR model showed high prediction accuracy with a coefficient of determination of 0.98, compared with the classic model and other ML models.

Machine learning-enabled predictions of as-built relative density and high-cycle fatigue life of Ti6Al4V alloy additively manufactured by laser powder bed fusion

Ensuring high forming quality and performance of as-built components via laser powder bed fusion (LPBF) additive manufacturing (AM) necessitates, meticulous consideration of the material-process-structure-property relationships, particularly due to multiple interrelated AM process parameters exert complex non-linear effects on macro and micro-structures of as-built parts. The quest for optimal process parameters through iterative trial-and-error experiments incurs long time periods and substantial costs, while simultaneously suffering from a lack of precision in discerning the optimized parameters. To address these challenges, this study integrates machine learning (ML) and ML feature engineering techniques for predicting relative densities of LPBF-built Ti6Al4V alloy parts. The prediction performance of Bayesian network (BN) and Multilayer Perceptron (MLP) models are compared, concluding that the MLP prediction model has higher efficiency and accuracy. This prediction model can be inverted to derive optimized LPBF process parameters based on desired density values. The influences of AM defects characterized by micro-computed tomography (μ-CT) on high-cycle fatigue life of LPBF-built Ti6Al4V alloy was studied, for establishing a ML-based pathway for predicting the fatigue life with another MLP model. The efficient approaches offer valuable insights into developing high-precision and widely applicable mapping models that encompass the interplay between process, defects, and fatigue life.

Detection and Characterization of Defects in Additive Manufacturing by Polarization-Based Imaging System

Additive manufacturing (AM) technology such as selective laser melting (SLM) often produces a high reflection phenomenon that makes defect detection and information extraction challenging. Meanwhile, it is essential to establish a characterization method for defect analysis to provide sufficient information for process diagnosis and optimization. However, there is still a lack of universal standards for the characterization of defects in SLM parts. In this study, a polarization-based imaging system was proposed, and a set of characterization parameters for SLM defects was established. The contrast, defect contour information, and high reflection suppression effect of the SLM part defects were analyzed. Comparative analysis was conducted on defect characterization parameters, including geometric and texture parameters. The experimental results demonstrated the effects of the polarization imaging system and verified the feasibility of the defect feature extraction and characterization method. The research work provides an effective solution for defect detection and helps to establish a universal standard for defect characterization in additive manufacturing.

Recurrence Network-Based 3D Geometry Representation Learning for Quality Control in Additive Manufacturing of Metamaterials

Abstract Metamaterials are designed with intricate geometries to deliver unique properties, and recent years have witnessed an upsurge in leveraging additive manufacturing (AM) to produce metamaterials. However, the frequent occurrence of geometric defects in AM poses a critical obstacle to realizing the desired properties of fabricated metamaterials. Advances in three-dimensional (3D) scanning technologies enable the capture of fine-grained 3D geometric patterns, thereby providing a great opportunity for detecting geometric defects in fabricated metamaterials for property-oriented quality assurance. Realizing the full potential of 3D scanning-based quality control hinges largely on devising effective approaches to process scanned point clouds and extract geometric-pertinent information. In this study, a novel framework is developed to integrate recurrence network-based 3D geometry profiling with deep one-class learning for geometric defect detection in AM of metamaterials. First, we extend existing recurrence network models that focus on image data to represent 3D point clouds, by designing a new mechanism that characterizes points’ geometric pattern affinities and spatial proximities. Then, a one-class graph neural network (GNN) approach is tailored to uncover topological variations of the recurrence network and detect anomalies associated with geometric defects in the fabricated metamaterial. The developed methodology is evaluated through comprehensive simulated and real-world case studies. Experimental results have highlighted the efficacy of the developed methodology in identifying both global and local geometric defects in AM-fabricated metamaterials.

Online monitoring of direct laser metal deposition process by means of infrared thermography

Direct laser metal deposition (LMD–DED) is an additive manufacturing (AM) process that is used to build up and repair high-quality metal components. It works by overlapping layers of powder material and melting them with a laser. To get a stable process without defects and to reach, at the same time, high mechanical properties, a robust assessment and control of the process parameters, and above all of their combination, is required. The ideal goal is to assure the online control, to stop or correct the process in case of unexpected anomalies. In this work, a robust online monitoring of the laser metal deposition (LMD–DED) process based on the use of infrared thermography was developed and proposed. After choosing the suitable process parameters, a customized design of experiments (DOE) was set, and the statistical analysis of different thermal features was carried out to develop the most robust models that correlate them with the input process parameters (laser power, scanning speed, and powder flow rate). The proposed procedure was based on the extraction of different thermal features from suited regions of interest (ROI), performing statistical analyses by means of analysis of variance (ANOVA) and building regression models to correlate the process parameters with the thermal behavior. The obtained results demonstrated the possibility to control the process by means of the chosen thermal features, independent of the position of the ROI. Moreover, the possibility to use the models to detect typical AM defects, and anomalies, online directly during the process, has been proved and verified by destructive macrographs carried out on the manufactured coupons.

Contrastive learning-based semantic segmentation for In-situ stratified defect detection in additive manufacturing

Supervised semantic segmentation has been widely utilized for the quality assurance of the additive manufacturing process. However, abnormal states (those with defects) happen significantly less frequently than normal ones (those without defects), resulting in the collected dataset usually containing fewer defect samples than normal ones, which deteriorates the performance of defect detection in additive manufacturing. To address this issue, a novel contrastive learning-based semantic segmentation model, termed cLass-aware Semantic Contrast and Attention amalgamation (LSCA), is proposed to accurately detect the in-situ stratified defect with imbalanced data. LSCA is equipped with a defect memory bank which is devised to store and retrieve class-aware semantic memory from poorly labeled training data. In particular, class-aware semantic contrast is devised to drive network learning by contrasting all categorical defect regions, leading to a more holistic defect pattern understanding. Further, class-aware attention amalgamation is developed to assemble various relationship contexts in the defect memory to enrich semantic representations. The extensive experiments demonstrate that, despite the imbalanced labeling of the training dataset, our LSCA consistently outperforms cutting-edge semantic segmentation techniques for detecting in-situ stratified defects in additive manufacturing.

Predicting additive manufacturing defects with robust feature selection for imbalanced data

Promptly predicting defects during an additive manufacturing process using only copious log data provides many advantages, albeit with computational limitations. We focus on predicting defects during electron beam melting with the black box nature of the manufacturing machine. For an accurate prediction of defects, which are rare ( < 2 % ), we extract temporal information to track abnormalities and formulate a feature selection algorithm that maximizes the expected value of a cost-sensitive accuracy. Correct identification of features responsible for the defects increases predictive power and informs manufacturers of potential corrective/preventive actions for process improvement. We solve the feature selection through resampling strategies integrated with ensemble procedures to handle data uncertainty and imbalance. Exploiting data uncertainty in our search leads to finding robust features with consistent predictive power. Our proposed methodology shows a 43% improvement in predicting defects (recall) without losing precision. Beyond additive manufacturing, this methodology has general application for rare-event prediction and imbalanced datasets.

Classification of CMT Additive Manufacturing Defects based on Statistical Features

Aiming at the problem of the CMT(cold metal transfer) additive manufacturing process defect monitoring method being susceptible to environmental influence, this paper proposes a classification method for additive manufacturing process defects with current as the research object. By setting up three sets of experiments, experimenting and collecting the current parameters of each group, using the statistical feature method to pretreat the current, and then classifying the pretreated data into random forests, to highlight the superiority of statistical characteristics plus random forests, two sets of control groups are also set up for comparison. It was concluded that the principal component analysis method could not classify the non-defective group and the non-protective gas group, while the use of random forest could achieve two by two classifications of the three working conditions; After the statistical feature method treatment, the random forest classification is compared with the original current direct random forest classification, and the accuracy of the training set, verification set, and test set has been improved.

Spectroscopy-based smart optical monitoring system in the applications of laser additive manufacturing

The potential defects during the additive manufacturing (AM) process greatly deteriorate the mechanical properties of the fabricated structures and, as a result, increase the risks of part fatigue failure and even disasters. As laser additive manufacturing is such a complex process, many different physical phenomena such as electromagnetic radiation, optical and acoustic emission, and plasma generation will occur. Unlike vision and acoustic methods, the spectroscopy based smart optical monitoring system (SOMS) provides atomic level information revealing mechanical and chemical condition of the product. By monitoring plasma, multiple information such as line intensity, standard deviation, plasma temperature, or electron density, and by using different signal processing algorithms such as vector machine training or wavelet transforming, AM defects have been detected and classified. Utilizing two fiber optic components, a bifurcated fiber and a split fiber, the experimental results were performed to improve SOMS signal-to-noise ratio. Defects, including subsurface pores and sudden changes of process parameters including shielding gas shut-off and foreign substance, were identified by the spectroscopy based SOMS. For chemical composition characterization, a degree of dilution in terms of chemical element variation is identified by a spectral peak intensity ratio through the SOMS. It turned out that the information on the Cr/Fe ratio of deposit at a certain layer is vital to design the mechanical property in the IN625 deposition on the mild steel case. The SOMS has also demonstrated that the chemistry ratio can be determined from the calibration curve method based on the known alloy samples and that the ratio of the maximum intensities of multiple species provides more information about the quality of the alloy.

Raking Process for Powder Bed Fusion of Ti–6Al–4V Alloy Powder Analyzed by Discrete Element Method

In order to avoid the formation of defects in additive manufacturing (AM) by powder bed fusion (PBF) process, it is crucial to understand the relationship between the quality of powder bed and a powder spreading process. In this study, the influences of conditions of powder raking process on the densities and homogeneity of powder bed have been examined by computer simulation using Discrete Element Method (DEM) and experiment of powder bed formation using a blade-type spreader for Ti–6Al–4V powder by way of example. The results were analyzed with a special focus on the effects of the relative size of powder particles with respect to the gap between the blade and the build platform. It has been clearly shown that the gap needs to be larger than the upper bound of the distribution of powder particles diameter to obtain a high-density powder bed. The DEM simulation indicated that the blade can sweep even powder particles that are not in direct contact with the blade when the powder particles are in a close-packed tetragonal configuration. This is the probable reason for the experimental fact that powder particles smaller than half of the gap are suitable for PBF.

NUMERICAL AND EXPERIMENTAL INVESTIGATION OF THE EFFECT OF DELAMINATION DEFECT AT MATERIALS OF POLYETHYLENE TEREPHTHALATE (PET)PRODUCED BY ADDITIVE MANUFACTURING ON FLEXURAL RESISTANCE

Polyethylene terephthalate (PET) material, which is widely used in the packaging industry due to its thermal and mechanical properties, high chemical resistance, and low gas permeability, is among the most widely used polymer materials in the world. These properties have made their use in additive manufacturing methods widespread. Determining how some common additive manufacturing defects affect the products produced by these methods will increase the adoption of these technologies in the final product production. In this study, the investigation of the effect of layer non-joining defect called delamination on the impact strength of PET material produced by additive manufacturing method at different layer thicknesses was carried out experimentally and numerically. The effects to flexural stress on the artificially created layer adhesion defect on the middle layers of the parts produced and modeled with a layer thickness of 0.1/0.2/0.3mm were investigated. It has been observed that the increase in layer thickness decreases flexural strength. In addition, while the flexural strength of the specimens containing delamination decreased, the increase in layer thickness accelerated this decrease.

Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing

A methodology based on step-heating thermography for predicting the length dimension of small defects in additive manufacturing from temperature data measured on thermal images is proposed. Regression learners were applied with different configurations to predict the length of the defects. These algorithms were trained using large datasets generated with Finite Element Method simulations. The different predictive methods obtained were optimized using Bayesian inference. Using predictive methods generated and based on intrinsic performance results, knowing the material characteristics, the defect length can be predicted from single temperature data in defect and non-defect zone. Thus, the developed algorithms were implemented in a laboratory set-up carried out on ad-hoc manufactured parts of Nylon and polylactic acid which include induced defects with different sizes and thicknesses. Using the trained algorithm, the deviation of the predicted results for the defect size varied between 13% and 37% for PLA and between 13% and 36% for Nylon.

Multicaloric effect in Ni–Mn–Sn metamagnetic shape memory alloys by laser powder bed fusion

Metamagnetic shape memory alloys with giant caloric effects and sophisticated geometries are promising candidates for solid-state refrigeration. Additive manufacturing (AM) overcomes the machining difficulties and may provide the geometric freedom. Whereas the metallurgical defects in AM limits their applications, and the multicaloric effects of AM metamagnetic shape memory alloys have been poorly understood. Here, Ni 45 Mn 44 Sn 11 metamagnetic shape memory alloys are prepared via laser powder bed fusion ( L -PBF). A magnetic entropy change of 10.2 J kg −1 K −1 and adiabatic temperature change of 0.8 K is achieved under the external field change of 5 T and 1.5 T, respectively. An improved relative density of ∼99.5% and high compressive strength of 519 MPa is achieved. These result from the defects mitigation which shows homogeneous grain structure/composition and crack healing during rapid printing and subsequent annealing. Particularly, we have proposed a multicaloric approach of “magnetic-field-assisted elastocaloric cooling” to enhance the temperature change up to 7.4 K, which is 20% higher than the sole elastocaloric effect. These results provide a basis for future studies of the multicaloric effect in metamagnetic shape memory alloys fabricated via AM.