PSI - Issue 69

Mohammadjavad Abdollahzadeh et al. / Procedia Structural Integrity 69 (2025) 2–19

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1. Introduction Additive manufacturing (AM), often referred to as 3D printing, has precipitated a paradigm shift in production methodologies. In contrast to the traditional manufacturing landscape, which required complicated molds, dies, and high-cost equipment, AM transforms complex part creation into a more feasible task. This technique significantly reduces energy and material waste, while improving the precision and accuracy of the resultant parts [1-6]. In the contemporary era, underscored by relentless technological advancement, Laser Powder Bed Fusion Additive Manufacturing (LPBF-AM) has emerged as a cornerstone in multiple sectors. Recognized for its efficacy in fabricating superior quality 3D components, LPBF-AM is characterized by a strategic process. It commences with the laser induced melting of a specific area, culminating in a single-track formation. This operation is iteratively performed to yield multiple single tracks on the base layer through fine adjustments of hatch spacing. Subsequently, various heat transfer mechanisms, including conduction, radiation, and convection, solidify the initial layer. The powder bed is then incrementally lowered, and a new layer is introduced, a cycle that continues until a dense, three-dimensional object is materialized [7-10]. Within the wide spectrum of materials used in additive manufacturing, Nickel Titanium (NiTi) alloys have attracted significant scholarly interest, attributable to their unique mechanical properties. Celebrated for their superelasticity and shape memory characteristics, NiTi alloys find diverse applications spanning the medical, aerospace, and automotive industries. In the realm of LPBF-AM, NiTi alloys introduce unprecedented potential for fabricating intricate, tailor-made structures that challenge conventional manufacturing capabilities. The prospect of 3D printing with NiTi harnesses the extraordinary properties of the alloy, thus driving innovation and design. Indeed, a detailed study of NiTi within the context of the LPBF-AM process promises to illuminate our understanding of the material's response to specific process conditions, consequently influencing the quality of the final component [11-13]. In the application of LPBF, engineers consistently face challenges in the form of manufacturing defects. Cracking, porosity, and delamination often follow from this methodology, causing significant implications for the quality and function of the finished product. These defects are intrinsically linked to process parameters. The use of LPBF necessitates careful inspection of many parameters including laser power, scan speed, scan strategy, laser spot diameter, hatch spacing, and powder layer thickness, all of which require precise adjustments for optimal results. Extensive experimental worek has been undertaken by researchers to unravel the intricate relationship between these process parameters and the formation quality of NiTi materials [14-24]. Despite the substantial progress made through experimental studies, relying solely on a trial-and-error methodology to discern the optimal process parameters is neither feasible nor efficient due to its resource-intensive nature. Additionally, accurate monitoring of vital attributes such as temperature and stress field—crucial to understanding LPBF-NiTi formation—poses a significant challenge. Consequently, the implementation of numerical methods to estimate solutions presents a more logical and suitable alternative. Two prominent numerical approaches commonly adopted for modelling the LPBF process are the Finite Element Method (FEM) [25-29] and finite volume (FVM) [30-48]. The FVM proves advantageous when exploring the dynamics of melt pool flow; it exhibits superior conservation properties, offers flexibility in accommodating foundational assumptions, demonstrates adaptability in response to mesh modifications, and effectively avoids the constraints linked to dispersion phenomena. Conversely, while the FEM necessitates fewer computational resources and enables accelerated computational speeds, it presents limitations. The integration technique employed by FEM often lacks precision, and the resultant computations for multi-cell mobility analyses fall short of desired accuracy. These deficits render FEM less appropriate for tackling the intricate challenges presented by advanced fluid dynamics problems [49]. Contemporary research efforts have predominantly focused on materials such as Ti-6Al-4V [30, 33, 45, 50], Bulk Metallic Glasses (BMGs) [31], 316L stainless steel [32, 40], Inconel-718 [35, 39], Co-Cr-Mo [36], AlCu5MnCdVA [37], H13 steel [38], AlSi10Mg [43], K418 [44], and Copper [47]. These works have greatly advanced the numerical modeling of the LPBF process. However, the focus on numerical modeling of NiTi alloys remains considerably limited in comparison. In one study, Zhu et al. examined Nitinol, aiming to predict melt pool dimensions and determine criteria for defect formation. They integrated an analytical model with experimental data to achieve these objectives [25]. Another study underscored the significant influence of hatch distance on microstructure; researchers determined varied microstructures by manipulating this parameter. The study evaluated the Finite Element (FE) model's predictive