In situ alloying: investigation of the melt pool stability during powder bed fusion of metals using a laser beam in a novel experimental set-up

The limited access to materials for the Powder Bed Fusion of Metals using a Laser Beam (PBF-LB/M) is compensated by in situ alloying. Individual melt pool characteristics can be specifically influenced to improve the mechanical properties of the final part. However, conventional PBF-LB/M machines allow only limited access for detailed observation of the process zone and, in particular, the melt pool. This paper presents a methodology for systematically analyzing the melt pool in the cross section to determine the in situ variation of the melt pool depth. A custom PBF-LB/M test bench was devised to enable investigation of the process zone using high-speed infrared cameras. The image data were processed automatically using a dedicated algorithm. The methodology was applied to analyze the effect of additives on the melt pool stability. Stainless steel 316L powder was blended with the aluminum alloy AlSi10Mg by up to 20 wt.%. It was found that the blended powder significantly reduced the variation of the melt pool depth.

A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

Prediction of Melt-Pool Characteristics in SLM Process for Ti6Al4V Using a Semi-Analytical Model

Selective Laser Melting (SLM) is a powder-based layer-by-layer manufacturing technique to produce metallic customized shape components. The exceptionally high thermal gradient induces residual stress and distorts the part geometry affecting the yield quality. Computational models are instrumental in optimizing the process controls to fabricate high-quality components, and hence several such methods have been explored to simulate the thermal behavior of the process and the heat transfer in the melt-pool. Most of the practiced techniques are computationally expensive, making it infeasible to perform a parametric study. Based on closed-form exact heat conduction solution and Finite Volume Method (FVM), a pseudo-analytical thermal modeling approach has been employed to estimate the melt-pool characteristics and temperature distribution of the SLM process. A moving volumetric Gaussian heat source laser model and Green’s function have been adopted to model the heat input by conduction. The heat loss by conduction and convection has been calculated by implementing Finite Volume discretized equations on a 2-dimensional thin-walled domain with appropriate part boundary conditions. Additionally, the Alternating Direction Implicit iterative technique has been implemented for the fast convergence of the simulation. The model is used to demonstrate the influence of the process parameters and non-linear material phase change for a single-line single layer and multilayer part fabrication. The computed melt-pool dimensions and temperature distribution for varying laser-power, scanning velocity, and layer thickness for Ti6Al4V are validated with the experimental data from the literature with fair agreements.

A Data Driven Approach to the Online Monitoring of the Additive Manufacturing Process

Process monitoring in additive manufacturing (AM), i.e. in laser powder bed fusion (LPBF) of metal parts, has been identified as the crucial bottleneck in accelerating the AM industrialization process. To reduce the cost and time needed to produce and qualify an AM part, an online monitoring system of the manufacturing process is desirable. While the currently available systems capture a large amount of process data, they still lack the ability to interpret the acquired data adequately. In this work we present the first steps towards an automated evaluation of online monitoring data i.e. melt pool data. It is shown that a well-trained convolutional neural network (CNN) is able to detect artificially induced process deviations on the basis of melt pool characteristics.

Camera-Based Coaxial Melt Pool Monitoring Data Registration for Laser Powder Bed Fusion Additive Manufacturing

The quality of powder bed fusion (PBF) built parts is highly correlated to the melt pool characteristics. Camera-based coaxial melt pool monitoring (MPM) is widely applied today because it provides high-resolution monitoring on the time and length scales necessary for deep PBF process understanding, in-process defect detection, and real-time control. For such functions, MPM data has to be registered correctly to a well-defined coordinate system. This paper presents methods for camera-based coaxial melt pool monitoring (MPM) data registration using the build volume coordinate system defined in ISO/ASTM52921, for both open architecture AM systems and 3rd party MPM augmented closed commercial systems. Uncertainties are evaluated for the proposed methods and case studies provided to demonstrate the effectiveness of the methods.

From Scan Strategy to Melt Pool Prediction: A Neighboring-Effect Modeling Method

The quality of additive manufacturing (AM) built parts is highly correlated to the melt pool characteristics. Hence, melt pool monitoring and control can potentially improve the AM part quality. This paper presents a neighboring-effect modeling method (NBEM) that uses a scan strategy to predict melt pool size. The prediction model can further assist in scan strategy optimization and real-time process control. The structure of the proposed model is formulated based on the physical principles of melt pool formation, while experimental data are used to identify the optimal coefficients. Compared to the traditional power-velocity prediction models, the NBEM model introduces the cumulative neighboring-effect factors as additional input variables. These factors represent the neighborhood impact of scan path on the focal point melt pool formation from time and distance perspective. Two experiments use different scan strategies to collect in situ measurements of the melt pool size for model construction and validation. By introducing the neighboring-effect factors, the global normalized root-mean-square Error (NRMSE) is improved from around 0.10 to 0.08. More importantly, the local NRMSE of irregular melt pool area prediction is improved to around 0.15 for more than 50% improvement. The case studies verify that the proposed method can predict the melt pool variations by seamlessly integrating scan strategy considerations.

From Scan Strategy to Melt Pool Prediction: A Neighboring-Effect Modeling Method

The quality of AM built parts is highly correlated to the melt pool characteristics. Hence melt pool monitoring and control can potentially improve AM part quality. This paper presents a neighboring-effect modeling method (NBEM) that uses scan strategy to predict melt pool size. The prediction model can further assist in scan strategy optimization and real-time process control. The structure of the proposed model is formulated based on the physical principles of melt pool formation, while experimental data is used to identify the optimal coefficients. Compared to the traditional power-velocity prediction models, NBEM model introduces the cumulative neighboring-effect factors as additional input variables. These factors represent the neighborhood impact of scan path on the focal point melt pool formation from time and distance perspective. Two experiments use different scan strategies to collect in-situ measurements of melt pool size for model construction and validation. By introducing the neighboring-effect factors, the global Normalized Root Mean Square Error (NRMSE) is improved from around 0.10 to 0.08. More importantly, the local NRMSE of irregular melt pool area prediction is improved to around 0.15 for more than 50% improvement. The case studies verify that the proposed method can predict the melt pool variations by seamlessly integrating scan strategy considerations.

Analysis of Melt Pool Characteristics and Process Parameters Using a Coaxial Monitoring System during Directed Energy Deposition in Additive Manufacturing

The growing number of commercially available machines for laser deposition welding show the growing acceptance and importance of this technology for industrial applications. Their increasing usage in research and production requires process stability and user-friendly handling. A commercially available DMG MORI LT 65 3D hybrid machine used in combination with a CCD-based coaxial temperature measurement system was utilized in this work to investigate what information relating to the intensity distribution of melt pool surfaces could be appropriate to draw conclusions about process conditions. In this study it is shown how the minimal required specific energy for a stable process can be determined, and it is indicated that the evolution of a plasma plume depends on thermal energy within the base material. An estimated melt pool area—calculated by the number of pixels (NOP) with intensities larger than a fixed, predefined threshold—builds the main measure in analysing images from the process camera. The melt pool area and its temporal variance can also serve as an indicator for an increased working distance.