Parameter optimization for low-porosity Ti-6Al-4V parts produced using accelerated PBF-LB process

Laser powder bed fusion of AlSi10Mg: Influence of energy intensities on spatter and porosity evolution, microstructure and mechanical properties

The aim of this study was to determine the influence of selective laser melting (SLM) process parameters on the formation of cracks in nickel-based single crystal alloy. Several sets of laser scanning parameters, with varied laser power, scanning speed and hatch distance, thus resulting in different volumetric and linear energy densities, were investigated. The relationship between energy density, specimen densification, cracks formation and evolution of directional microstructure was shown. It was found that higher linear energy density results in fewer cracking and defects. Cracking in lower energy samples was attributed to the formation of thermal shrinkage pores in the interdendritic spaces and precipitation of Ta, Mo and Nb carbides along the dendrites boundaries.

Ti-6Al-4V produced by laser powder bed fusion can achieve mechanical properties similar to its wrought counterpart if an appropriate post-process heat treatment is applied. The response of the material to post-process heat treatment depends on the prior-beta grain size in the as-built condition. The current article illustrates that a reduction in hatch distance can cause coarsening of prior-beta grains due to retained heat that reduces the cooling rate. On the other hand, an increase in scan speed refines prior-beta grains. Unfortunately, an increase in scan speed has to be accompanied by a reduction in hatch distance to prevent an increase in porosity and the potential grain refinement is then cancelled out. The use of a faster scan speed in combination with a smaller hatch distance significantly improves roughness of the top surface. EBSD in combination with reconstruction of prior-beta grains is an effective technique to quantify prior-beta grain size. Furthermore, the use of Rosenthal’s equations allows quick calculation of cooling rate that can be used to predict the dependence of prior-beta grain size on process parameters.

Experimental study and neural network model based prediction of layer thickness influence on LPBF IN625 single track geometry

Laser Additive Manufacturing using Powder Bed Fusion (LAM-PBF) is one of the revolutionary technologies playing a key role in fourth industrial revolution for redefining manufacturing from mass production to mass customization. To upkeep the pace, Raja Ramanna Centre for Advanced Technology (RRCAT) has indigenously developed an LAM-PBF system and it is being used for process and component development for various engineering applications. This paper reports a parametric investigation to evaluate the influence of process parameters on the sample properties and to develop the process window for fabricating complex shaped engineering components. In the present work, an experimental investigation is carried out to investigate the effect of Laser Energy density (LED) on the porosity, microstructure and mechanical properties of SS 316L bulk structures fabricated by LAM-PBF system. LED is a combined parameter simultaneously considering the effect of Laser Power (P), Scan Speed (v), hatch spacing (h) and layer thickness (t). The effect of three LED values such as 83.33 J/mm3, 253.97 J/mm3 and 476.19 J/mm3 is investigated in the present work by building cuboidal samples at a layer thickness of 75 microns. Porosity is estimated using area fraction method in optical microscopy and it is found that the minimum porosity of 0.14 % and pore area of 1.28 mm2 are observed at 253.97 J/mm3. Maximum porosity of 18.85 % is observed at 83 J/mm3 due to incomplete fusion defects. However, porosity observed at 475 J/mm3 is 6.56 % with average pore size of 17.8 mm2. Microstructural studies show primarily columnar growth in all the samples with fine dendrites. The dendrite size is observed to be 3.2 μm, 2.4 μm and 1.46 μm at 83.33 J/mm3, 253.97 J/mm3 and 476.19 J/mm3 respectively. Micro-hardness and single cycle automatic ball indentation studies are found to be in agreement with dendritic size, with lower hardness at larger dendrite size. X-Ray Diffraction (XRD) studies indicate similar peaks at all the conditions, with slight peak shift observed with increase in LED primarily due to higher amount of residual stress. Thus, it can be inferred that LED of 253.97 J/mm3 is suitable for fabricating engineering components due to combination of lower porosity and fine dendritic structure.

This study examined the influence of process parameters on porosity and mechanical properties in Laser Powder Bed Fusion (LPBF) of Beta 21S (Ti–15Mo–3Nb–3Al–0.2Si) and Ti5553 (Ti-5Al-5V-5Mo-3Cr) titanium alloys, with Ti64 (Ti-6Al-4V) as the reference material. Using the volumetric energy density – a function of laser power, scan speed, hatch spacing, and layer thickness – to inform the design space, a means to achieve optimal process parameters which minimise porosity content was explored. By employing a constrained design space and response surface methodology, windows of process parameters were identified. Regression models developed for predicting porosity achieved R-squared values of 0.822 for Ti64, 0.859 for Beta 21S, and 0.788 for Ti5553, indicating strong data fit. The model for Ti64 was validated against five literature sources, with four predictions falling within confidence bands and showing an error range of 0.07 to 0.48%. For Beta 21S and Ti5553, the combined error range for validation samples was 0.24 to 0.45%. Optimal volumetric energy density parameters were suggested to maximise relative density. Vickers hardness was assessed and compared favourably to existing literature. Significant variation was found amongst hardness data, suggesting that LPBF parameters influence mechanical performance. Further investigation is recommended to understand the effects of thermal history on the observed hardness behaviour.

Process parameter optimization for removable partial denture frameworks manufactured by selective laser melting

A comparison of Inconel 718 obtained with three L-PBF production systems in terms of process parameters, as-built surface quality, and fatigue performance

In the recent years, Additive Manufacturing (AM) has become a very demanding process utilized by majority of the manufacturing industries due to its complex geometrical capabilities, high accuracy, minimal material usage and better build quality. Among the various types of AM methods, Direct Metal Laser Sintering (DMLS) is a feasible method with many applications and a lot of scope for research. This paper focuses on optimizing the important parameters of DMLS process for better build quality of AlSi10Mg Aluminium alloy. The parameters identified are laser power, hatch distance, scanning speed, and layer thickness. Grey–Taguchi multi response optimization method has been used. Experimental specimens were fabricated and tested. Micro-Vickers hardness and average surface roughness were studied. Experiments were analyzed to determine the best combination of parameters of the DMLS process. A detailed micro-structural study was conducted on the specimens. Also, crystallographic study was made. The resultant specimen thus produced had a better build quality along with satisfying micro-structural characteristics.

This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training and testing machine learning (ML) models using variable process parameters: laser power (250-350 W), scanning speed (1050-1300 mm/s), and hatch spacing (65-90 μm). Eight ML models were investigated: 1. Support Vector Regression (SVR), 2. Kernel Ridge Regression (KRR), 3. Stochastic Gradient Descent Regressor, 4. Random Forest Regressor (RFR), 5. Extreme Gradient Boosting (XGBoost), 6. Extreme Gradient Boosting with limited depth (XGBoost LD), 7. Extra Trees Regressor (ETR) and 8. Light Gradient Boosting Machine (LightGBM). All models were trained using the Fast Library for Automated Machine Learning & Tuning (FLAML) framework to predict the relative density of the fabricated samples. Among these, the XGBoost model achieved the highest predictive accuracy, with a coefficient of determination R2=0.977, mean absolute percentage error MAPE = 0.002, and mean absolute error MAE = 0.017. Experimental validation was conducted on 27 newly fabricated samples using ML predicted process parameters. Relative densities exceeding 99.6% of the theoretical value (7.76 g/cm3) for all models except XGBoost LD and KRR. The lowest MAE = 0.004 and the smallest difference between the ML-predicted and PBF-LB validated density were obtained for samples made with LightGBM-predicted parameters. Those samples exhibited a hardness of 604 ± 13 HV0.5, which increased to approximately 630 HV0.5 after tempering at 550 °C. The LightGBM optimised parameters were further applied to fabricate a part of a forging die incorporating internal through-cooling channels, demonstrating the efficacy of machine learning-guided optimisation in achieving dense, defect-free H13 components suitable for industrial applications.

Tuning hatch distance to optimize microstructure and mechanical properties of 2205 duplex stainless steel produced by laser powder bed fusion

Laser powder bed fusion has been widely investigated for shape memory alloys, primarily NiTi alloys, with the goal of tailoring microstructures and producing complex geometries. However, processing high temperature shape memory alloys (HTSMAs) remains unknown. In our previous study, we showed that it is possible to manufacture NiTiHf HTSMA, as one of the most viable alloys in the aerospace industry, using SLM and investigated the effect of parameters on defect formation. The current study elucidates the effect of process parameters (PPs) on the functionality of this alloy. Shape memory properties and the microstructure of additively manufactured Ni-rich NiTiHf alloys were characterized across a wide range of PPs (laser power, scanning speed, and hatch spacing) and correlated with energy density. The optimum laser parameters for defect-free and functional samples were found to be in the range of approximately 60–100 J/mm3. Below an energy density of 60 J/mm3, porosity formation due to lack-of-fusion is the limiting factor. Samples fabricated with energy densities of 60–100 J/mm3 showed comparable thermomechanical behavior in comparison with the starting as-cast material, and samples fabricated with higher energy densities (>100 J/mm3) showed very high transformation temperatures but poor thermomechanical behavior. Poor properties for samples with higher energies were mainly attributed to the excessive Ni loss and resultant change in the chemical composition of the matrix, as well as the formation of cracks and porosities. Although energy density was found to be an important factor, the outcome of this study suggests that each of the PPs should be selected carefully. A maximum actuation strain of 1.67% at 400 MPa was obtained for the sample with power, scan speed, and hatch space of 100 W, 400 mm/s, and 140 µm, respectively, while 1.5% actuation strain was obtained for the starting as-cast ingot. These results can serve as a guideline for future studies on optimizing PPs for fabricating functional HTSMAs.

Additive manufacturing (AM) by powder bed fusion using a laser beam (PBF-LB) is often considered as the process of choice for industrial applications. Small changes in process parameters and intrinsic or extrinsic material parameters can significantly affect the final as-built part properties in this highly complex AM process. Choosing a machine configuration with optimal process parameters can be time-consuming, especially when new powders, i.e., nano-modified feedstocks or batches, are used. Even if a set of parameters works for one PBF-LB machine, the same parameters may produce unsatisfactory results on another machine model. Dimensionless parameters can be beneficial in simplifying complex phenomena. In this study, a semi-analytical approach based on printing monolayers was semi-blind tested by five laboratories on different PBF-LB machines. Virgin polyamide 12 (PA12), silver, and carbon black nano-particle-modified PA12 powders were tested. Two dimensionless numbers were used to describe the process. The first number describes the energy conversion dimensionless. The second dimensionless number proposes a minimum energy input demand for full densification. Both numbers combine process parameters (e.g., laser power, scan speed) and material properties (latent heat and solid density), while energy conversion number is considered by experimental results (monolayer thickness). Results indicate how nanoparticles influence thermal conductivity and energy absorption. Suggested surface energy densities based on the dimensionless numbers were compared with mechanical properties. The prediction of the highest overall mechanical values (tensile strength and elongation at break) matched the best mechanical properties. The monolayer approach presents an experimental simple method for predicting suitable machine settings and narrowing the process window in an efficient and material-conserving way. Graphical abstract

Avoiding or eliminating thermal abnormalities in powder bed fusion (PBF) is critical since the abnormalities can lead to a higher failure rate of printing complex parts, a longer manufacturing lead time, and/or additional post-processing. Controlling the thermal evolution of the process can hinder or minimize some of the most frequently encountered thermal abnormalities. To achieve such an objective, the prediction and control of temperature distribution throughout an exposure layer is a crucial step. The generation of uniform temperature distribution throughout the printed layers and the avoidance of overheated zones are two primary sub-objectives for controlling the thermal evolution of the process. However, the complex and non-linear nature of the process has limited the ability to derive a universal analytical equation to correlate the process parameters with the thermal distribution of a printed layer. Laser specifications such as laser power and scanning speed are among the main process parameters that predominantly govern the temperature distribution throughout the layer. In this paper, we employ an artificial neural network (ANN) to correlate laser power with the temperature of the printed area around the melt pool in Inconel 718. In our first variant, we investigate the effectiveness of using the multilayer perceptron Radial Basis Neural Network (RBNN) to model the function for predicting the temperature distribution for various laser power. We use the Rosenthal equation to generate adequate inputs-outputs for training our function. We then compare the output with the simulation results for five different laser powers. The results show that the function was trained successfully with a low mean square root error of 9.7157 using 2000 samples, a wider gap exists between the trained function and the simulated data. In the second variant, we use a recurrent neural network (RNN), which enables temporal histories to be used for training. To fulfill such objective, we acquire real thermal data using a photon-counting IR camera for different printed layers. This step allows the training of a function to predict the temperature distribution precisely for different laser power and thermal history. As future work, we will employ the function to adjust the laser power to minimize the overheated zones and distribute the temperature uniformly throughout each exposure layer.KeywordsPowder bed fusion processArtificial neural networkFBFN neural networkTemperature predictionAdjusting laser power

Electron beam melting of Ti6Al4V: Role of the process parameters under the same energy density