Self-Supervised Multilabel Melt Pool Anomaly Classification in Powder Bed Fusion Additive Manufacturing

Laser powder bed fusion (LPBF) is a widely used Additive Manufacturing (AM) process involving a high-powered laser to fuse metal powder and build up successive layers to create complex structures. Process parameter selection significantly impacts the dimensional characteristics of the melt pool morphology, including its depth and width, which is vital to the final print quality. Accurately predicting the melt-pool depth is critical for ensuring the quality of the final product. Various methods for different materials have been investigated, for example, thermal image analysis, analytical methods like scaling law, machine learning, and so on. However, most of the promising results are only studies for one material, and it is not necessarily fully applied to another material. In this paper, our study involves utilizing machine learning models to make predictions of melt-pool depth in the LPBF process and compare their performance to an analytical approach based on scaling law with Nickel alloys IN625 and IN718. We trained three different machine learning models which are Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Neural Networks (NN), and evaluated their performance using R-squared error and Root Mean Squared Error (RMSE) against the performance of the analytical scaling law. Our results show that the machine learning models outperform the scaling law approach significantly. This study demonstrates the potential of machine learning for improving the accuracy of melt-pool depth prediction in LPBF and provides insights into the strengths and limitations of different machine learning models for this task.

Transient dynamics of powder spattering in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-energy x-ray imaging

Advances in computational modeling for laser powder bed fusion additive manufacturing: A comprehensive review of finite element techniques and strategies

Fatigue performance of shelled additively manufactured parts subjected to hot isostatic pressing

Development and validation of a material evaporation assisted thermal model for time-efficient calculation of thermal and solidification parameters during laser powder bed fusion process for Ti6Al4V

In the present study, microstructural and crystallographic features of an Al-Fe binary alloy with a near eutectic composition (Al-2.5wt%Fe) fabricated by laser powder bed fusion (LPBF) process were examined. The LPBF processing for the Al-Fe alloy powder was conducted at room temperature using a 3D Systems ProX 200 operating at a laser power of 204 W and a laser scan speed of 0.6 m/s and 0.8 m/s to fabricate cube samples with high relative density above 99 %. The fabricated sample exhibited characteristic microstructure consisting of a number of melt pools in which the regions had locally melted and rapidly solidified by laser irradiation during the LPBF process. Numerous fine particles of Al-Fe intermetallics with a mean size below 100 nm were found within the α-Al matrix in the observed melt pools, whereas relatively coarsened particles were localized around melt pool boundaries. Electron backscatter diffraction (EBSD) analyses revealed a number of columnar a-Al grains with a mean size of approximately 10 μm. The LPBF-fabricated Al-Fe alloy exhibits a high hardness of approximately 90 HV, which is more than twice higher than the conventionally casted Al-Fe alloy.

PurposeThis study aims to investigate the relationship between melt pool dimensions and acoustic emission (AE) signal magnitudes obtained during laser powder bed fusion (L-PBF) process of austenitic stainless steel. Specifically, by varying laser power and scan speed, the widths, depths and aspect ratios of melt pools were compared with AE signal magnitudes across a frequency range of 100–200 kHz.Design/methodology/approachExperiments were conducted under different laser powers at a fixed scan speed and scan speeds at a fixed laser power. Melt pool dimensions were measured from cross-sectional optical images, and AE signals were obtained using a piezoelectric AE sensor installed beneath the build plate. Short-Time Fourier Transform (STFT) was applied to AE signals, and the magnitudes of frequency components were obtained.FindingsA strong correlation between melt pool dimensions and STFT magnitude was obtained. Pearson correlation coefficients between melt pool dimensions and STFT magnitudes were above 0.9 and the p-values were below 0.05. Increasing the laser energy enlarged the volume of melt pool and intensified the oscillation of melt pool. When scan speed exceeded 1,100 mm/s, STFT magnitude showed a slight increase owing to the increase in the vapor pressure.Originality/valuePrevious studies used AE signals to detect defects, but this study found a correlation between STFT magnitude and melt pool dimensions in L-PBF process. It was also found that STFT magnitude was more affected by vapor pressure at higher scan speeds. Monitoring STFT magnitude can help to understand melt pool dynamics, maintain process consistency and identify irregularities in real time.

Hybrid microstructure-defect printability map in laser powder bed fusion additive manufacturing

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6~\mu \text{m}$ </tex-math></inline-formula> and a standard deviation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7~\mu \text{m}$ </tex-math></inline-formula> . By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.45~\mu \text{m}$ </tex-math></inline-formula> and a standard deviation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5~\mu \text{m}$ </tex-math></inline-formula> . In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa Note to Practitioners—As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of previous layer have a strong influence on the printing quality of current layer. The variations of surface roughness will lead to the fluctuation of mechanical properties of the fabricated components. Additionally, to the best of author’s knowledge, current commercial L-PBF machines can not actively control the surface roughness of samples during the process. The proposed IAMA in this work can predict and control the roughness on the top surface of each layer during L-PBF process. Therefore, the constructed architecture has strong potential for integrating into the commercial L-PBF machine for controlling the surface roughness on the top of the parts during the manufacturing process. As a result, the quality of the fabricated components is expected to be in consistent. Accordingly, L-PBF machine equipped with IAMA will have a strong potential in applying for mass production of the components in aerospace and automobile industries.

Effect of powder bed preheating on the crack formation and microstructure in ceramic matrix composites fabricated by laser powder-bed fusion process

Predicting meltpool depth and primary dendritic arm spacing in laser powder bed fusion additive manufacturing using physics-based machine learning

Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning

PurposeThis paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.Design/methodology/approachA systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.FindingsThe findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.Research limitations/implicationsThis paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.Originality/valueThis paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

The optimization of processing parameters is indispensable for the laser powder bed fusion (L-PBF) process. The deposited energy density (DED) is one of the process indexes for the L-PBF process and has a simplified formula of P·v−0.5, where P is the laser power, and v is the scan speed. This parameter describes the change in the relative density and the melt pool morphology with laser power and scan speed well, whereas it does not include the effect of other processing parameters, e.g., hatch spacing (S). In the present study, an attempt was made to incorporate the effect of S into DED. Al–12Si (mass%) alloy cube samples were fabricated by L-PBF under various P, v, and S, for evaluating the relative density and the melt pool morphology. The melt pool depth and width of L-PBF-manufactured Al–12Si alloy increased linearly with P·v−0.5 and did not exhibit a clear correlation with S. Based on the experimental observation, the effect of hatch spacing on DED was estimated to be S−0.5, and a new index of P·v−0.5·S−0.5 was proposed. This index described the change in the relative density of the L-PBF-manufactured Al–12Si alloy with laser conditions (P, v, and S) well when the thermal conduction mode melting was dominant. This study also indicated the limitation of the applicability of P·v−0.5·S−0.5 under the keyhole or transition mode melting.

A lightweight Fe–Mn–Al–C austenitic steel with ultra-high strength and ductility fabricated via laser powder bed fusion