Process Parameter Optimization in Metal Laser-Based Powder Bed Fusion Using Image Processing and Statistical Analyses

The powder bed fusion additive manufacturing process has received widespread interest because of its capability to manufacture components with a complicated design and better surface finish compared to other additive techniques. Process optimization to obtain high quality parts is still a concern, which is impeding the full-scale production of materials. Therefore, it is of paramount importance to identify the best combination of process parameters that produces parts with the least defects and best features. This work focuses on gaining useful information about several features of the bead area, such as contact angle, porosity, voids, melt pool size and keyhole that were achieved using several combinations of laser power and scan speed to produce single scan lines. These features are identified and quantified using process learning, which is then used to conduct a comprehensive statistical analysis that allows to estimate the effect of the process parameters, such as laser power and scan speed on the output features. Both single and multi-response analyses are applied to analyze the response parameters, such as contact angle, porosity and melt pool size individually as well as in a collective manner. Laser power has been observed to have a more influential effect on all the features. A multi-response analysis showed that 150 W of laser power and 200 mm/s produced a bead with the best possible features.

Calibration Technique of Thermal Analysis Model for Metal Additive Manufacturing Process Simulation by Nonlinear Regression and Optimization

A numerical analysis model that can accurately predict the physical characteristics of the actually additive manufactured products can significantly reduce time and costs for experimental builds and tests. Thermal analysis for the metal AM process simulation requires a lot of analysis parameters and conditions. However, their accuracy and reliability are not clear, and the current understanding of their influence on the analysis results is very insufficient. Therefore, in this study, the influence of uncertain analysis parameters on the thermal analysis results is estimated, and a procedure to calibrate these analysis parameters is proposed. By using the thermal analysis results for parameter cases determined by a design of experiments, a regression analysis model is constructed to estimate the sensitivity of the analysis parameters to the thermal analysis results. Additionally, it is used to determine the optimal values of analysis parameters that can produce the thermal analysis results closest to the given reference data from actual builds. By using the melt pool size computed from a numerical model as reference data, the proposed procedure is validated. From this result, it is confirmed that a high-fidelity thermal analysis model that can predict the characteristics of actual builds from minimal experimental builds can be constructed efficiently.

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.

Feedback Control of Melt Pool Area in Selective Laser Melting Additive Manufacturing Process

Selective laser melting (SLM), a metal powder fusion additive manufacturing process, has the potential to manufacture complex components for aerospace and biomedical implants. Large-scale adaptation of these technologies is hampered due to the presence of defects such as porosity and part distortion. Nonuniform melt pool size is a major cause of these defects. The melt pool size changes due to heat from the previous powder bed tracks. In this work, the effect of heat sourced from neighbouring tracks was modelled and feedback control was designed. The objective of control is to regulate the melt pool cross-sectional area rejecting the effect of heat from neighbouring tracks within a layer of the powder bed. The SLM process’s thermal model was developed using the energy balance of lumped melt pool volume. The disturbing heat from neighbouring tracks was modelled as the initial temperature of the melt pool. Combining the thermal model with disturbance model resulted in a nonlinear model describing melt pool evolution. The PID, a classical feedback control approach, was used to minimize the effect of intertrack disturbance on the melt pool area. The controller was tuned for the desired melt pool area in a known environment. Simulation results revealed that the proposed controller regulated the desired melt pool area during the scan of multiple tracks of a powder layer within 16 milliseconds and within a length of 0.04 mm reducing laser power by 10% approximately in five tracks. This reduced the chance of pore formation. Hence, it enhances the quality of components manufactured using the SLM process, reducing defects.

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.

From mine to part: directed energy deposition of iron ore

Purpose This paper aims to gain an understanding of the behaviour of iron ore when melted by a laser beam in a continuous manner. This fundamental knowledge is essential to further develop additive manufacturing routes such as production of low cost parts and in-situ reduction of the ore during processing. Design/methodology/approach Blown powder directed energy deposition was used as the processing method. The process was observed through high-speed imaging, and computed tomography was used to analyse the specimens. Findings The experimental trials give preliminary results showing potential for the processability of iron ore for additive manufacturing. A large and stable melt pool is formed in spite of the inhomogeneous material used. Single and multilayer tracks could be deposited. Although smooth and even on the surface, the single layer tracks displayed porosity. In case of multilayered tracks, delamination from the substrate material and deformation can be seen. High-speed videos of the process reveal various process phenomena such as melting of ore powder during feeding, cloud formation, melt pool size, melt flow and spatter formation. Originality/value Very little literature is available that studies the possible use of ore in additive manufacturing. Although the process studied here is not industrially useable as is, it is a step towards processing cheap unprocessed material with a laser beam.

Model Predictive Control of Melt Pool Size for the Laser Powder Bed Fusion Process Under Process Uncertainty

Laser power bed fusion (LPBF) process is one of popular additive manufacturing techniques for building metal parts through the layer-by-layer melting and solidification process. To date, there are plenty of successful product prototypes manufactured by the LPBF process. However, the lack of confidence in its quality and long-term reliability could be one of the major reasons prevent the LPBF process from being widely adopted in industry. The existing LPBF process is an open loop control system with some in-situ monitoring capability. Hence, manufacturing quality and long-term reliability of the part cannot be guaranteed if there is any disturbance during the process. Such limitation can be overcome if a feedback control system can be implemented. This paper studies the control effectiveness of the PID control and the model predictive control (MPC) for the LPBF process based on a physics-based machine learning model. The control objective is to maintain the melt pool width and depth at required level under process uncertainties from the powder and laser. A sampling-based dynamic control window approach is further proposed for MPC as a practical approach to approximate the optimal control actions within limited time constraint. Control effectiveness, pros, and cons of the PID control and the MPC for the LPBF process are investigated and compared through various control scenarios. It is demonstrated that the MPC is more effective than the PID control under the same conditions, but the MPC demands a valid digit twin of the LPBF process.

A Single Crystal Process Window for Electron Beam Powder Bed Fusion Additive Manufacturing of a CMSX-4 Type Ni-Based Superalloy

Using suitable scanning strategies, even single crystals can emerge from powder during additive manufacturing. In this paper, a full microstructure map for additive manufacturing of technical single crystals is presented using the conventional single crystal Ni-based superalloy CMSX-4. The correlation between process parameters, melt pool size and shape, as well as single crystal fraction, is investigated through a high number of experiments supported by numerical simulations. Based on these results, a strategy for the fabrication of high fraction single crystals in powder bed fusion additive manufacturing is deduced.

Numerical Investigation of Thermo-Mechanical Field during Selective Laser Melting Process with Experimental Validation

In this study, thermo-mechanical simulation was conducted to predict thermal and stress behavior in Selective Laser Melting (SLM). Temperature-dependent material properties for processed material 304L stainless steel were incorporated into the model in order to capture the change from powder to fully dense solid stainless steel. Temperature and thermal stress history were tracked under conditions of different parameter sets which were designed to reduce defect formation. The thermal model predicted the temperature history for multi-track scans under different process parameters, such as laser power, effective scanning speed and hatch spacing. Subsequently, the corresponding melt-pool size, solidification rate and temperature gradients could be calculated from simulated temperature data. These three parameters from the simulation were compared with experimental melt pool size, grain structure and cell spacing data obtained from a Renishaw AM250. The experimental data were also used to determine unknown simulation parameters required by the continuum model, e.g., the optical penetration depth and thermal conductivity multiplier for the molten region. This allowed the simulation model to accurately predict melt pool size and solidification structure of SLM 304L stainless steel. Simulated stress showed that the subsequent thermal cyclic melting in successive scanned tracks resulted in alternating compressive and tensile thermal stresses. This work will provide insight for studying microstructure morphology, residual stress and deformations in the SLM process of 304L stainless steel.