Recent years have witnessed increasing demand for selective laser melting (SLM) in practical applications; however, determining the appropriate process parameter range remains challenging. In this study, a framework was developed to determine the appropriate process parameter range considering the occurrence of defects and cracks by conducting a single-track test and thermal elastoplastic analysis. Keyholing, balling, and the residual unmelted regions were considered defects. The occurrence of solidification cracking, which is predominant in the SLM of solution-strengthened Ni-based alloys, was considered. Using the proposed framework, we could fabricate a part with largely no defects or cracks, except for the edges, under the determined optimal process parameters.

Wire arc additive manufacturing achieves high efficiency and low costs by using a melting wire for directional depositions. Thermal analyses and the finite element method have been applied to predict residual stress and the deformation of fabricated parts. For Ni-based alloy production, a method for predicting solidification microstructure evolution with segregation is needed in order to design precise heat treatment procedures. In this study, a multi-phase field method coupled with a CALPHAD database is developed to simulate the solidification microstructure evolution of a practical Ni-based alloy. Thermal analyses of a wire arc additive manufacturing model were performed by the process modeling of multi-pass depositions with a running cyclic arc. Solidification microstructure evolution was obtained using the temperature profile in each deposited layer by the multi-phase field method. These predicted microstructures are compared with experimental measurements. It is confirmed that the multi-phase field method coupled with the CALPHAD database is effective for predicting solidification microstructure and segregation in the engineering of Ni-based alloys.

Defects occur in laser powder bed fusion (L-PBF) such as the keyholing, lack of fusion, and the balling depending on the laser power (P) and the scan speed (V). The figure shows that the occupied regions of each defect are the process window and are essentially important to fabricate a high-quality part. This paper is a study of process window generation using single-track experiments and finite-element method simulation of thermal conduction for Inconel738LC alloy. A series of single-track experiments were conducted varying the range of P and V and the results were classified into keyholing, lack of fusion, balling, and good track. A series of simulations were conducted and validated by comparison with the experiments. To quantitively identify the balling, the isolines from the contour map generated by the results of simulations and the balling criteria of the ratio of melt pool length and the depth (L/D) of 7.69 were determined considering the past theoretical studies. The lack of fusion criteria: the ratio of the overlap depth in fabrication using multi-scan (D_ov) and powder layer thickness (t) of 0.1 was obtained. Using the criteria obtained from the experiments and simulation, the process window was generated.

Selective laser melting (SLM) produces a near-net-shaped product by scanning a concentrated high-power laser beam over a thin layer of metal powder to melt and solidify it. During the SLM process, the material temperature cyclically and sharply rises and falls. Thermal analyses using the finite element method help to understand such a complex thermal history to affect the microstructure, material properties, and performance. This paper proposes a novel calibration strategy for the heat source model to validate the thermal analysis. First, in-situ temperature measurement by high-speed thermography was conducted for the absorptivity calibration. Then, the accurate simulation error was defined by processing the cross-sectional bead shape images by the experimental observations and simulations. In order to minimize the error, the optimal shape parameters of the heat source model were efficiently found by using Bayesian optimization. Bayesian optimization allowed us to find the optimal parameters with an error of less than 4% within 50 iterations of the thermal simulations. It demonstrated that our novel calibration strategy with Bayesian optimization can be effective to improve the accuracy of predicting the temperature field during the SLM process and to save the computational costs for the heat source model optimization.

To understand the correlation between process, structures, and properties in laser powder bed fusion (L-PBF), it is essential to use numerical analysis as well as experimental approaches. A finite element thermal analysis uses a moving heat source model represented as a volumetric heat flux to simulate heat input by laser. Because of its computational efficiency, finite element thermal analysis is suitable for iterative procedures such as parametric study and process optimization. However, to obtain valid simulated results, the heat source model must be calibrated by comparison with experimental results for each laser scanning condition. The need for re-calibration limits the applicable window of laser scanning conditions in the thermal analysis. Thus, the current study developed a novel heat source model that is valid and precise under any laser scanning condition within a wide process window. As a secondary objective in the development, we quantitatively evaluated and compared the four heat source models proposed to date. It was found that the most suitable heat source model for the L-PBF is conical one among them. Then, a multiple linear regression analysis was performed to represent the heat source model as a function of laser power and scanning velocity. Consequently, the thermal analysis with the novel model is valid and precise within the wide process window of L-PBF.