Viscosity Of Material Research Articles

Study of magnetic field dependent viscosity and temperature on Jenkins model fluid flow over a rotating disk

The present work involves the study of ferrofluid flow over a stretchable rotating disk maintained at an uniform temperature in the presence of magnetic field dependent viscosity. The system is analyzed through the Jenkins model and the simplified governing equations are represented in the cylindrical co-ordinates. The obtained nonlinear coupled partial differential equations are reduced to a coupled nonlinear ordinary differential equations using the well known Von Karman transformation for rotating disk and solved numerically by the shooting method. The velocity profiles, pressure and temperature are studied by varying the stretching parameter, magnetic field dependent viscosity parameter, material constant, and Prandtl number. The comparison for limiting case of the present problem finds good agreement with the existing literature.

Real-time autonomous control of a continuous macroscopic process as demonstrated by plastic forming.

To meet the need for more adaptable and expedient approaches in research and manufacturing, we present a continuous autonomous system that leverages real-time, in situ characterization and an active-learning-based decision-making processor. This system was applied to a plastic film forming process to demonstrate its capability in autonomously determining process conditions for specified film dimensions without human intervention. Application of the system to nine film dimensions (width and thickness) highlighted its ability to explore the search space and identify appropriate and stable process conditions, with an average of 11 characterization-adjustment iterations and a processing time of 19 minutes per width, thickness combination. The system successfully avoided common pitfalls, such as repetitive over-correction, and demonstrated high accuracy, with R2 values of 0.87 and 0.90 for film width and thickness, respectively. Moreover, the active learning algorithm enabled the system to begin exploration with zero training data, effectively addressing the complex and interdependent relationships between control factors (material supply rate, applied force, material viscosity) in the continuous plastic forming process. Given that the core concept of this autonomous process can, in principle, be transferred to other continuous material processing systems, these results have implications for accelerating progress in both research and industry.

Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials

Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.

Strain Localization at Volcanoes Undergoing Extension: Investigation of Long‐Term Deformation at Krafla and Askja Volcanic Systems in North Iceland

AbstractVolcanoes in extensional environments may show gradual subsidence over decades during quiescent periods, due to various processes such as magma withdrawal, cooling, contraction, plate spreading and viscoelastic response. If significant rheological anomalies reside in volcano roots, due to the presence of magma and hot rock, they can influence the style of deformation. We use Finite Element Method (FEM) models to explore how strain localization due to extension can lead to volcano deflation. We apply rheological models comprising an elastic layer overlying a viscoelastic domain and include local up‐doming regions of low viscosity material beneath volcanic centers. The models reveal a localized subsidence above the rheological anomaly, influenced by the tectonic extension, and by the up‐doming volume and its viscosity. The models suggest that plate divergence may account for 4–5 mm/yr of observed subsidence at Krafla and Askja volcanic systems (KVS and AVS, respectively) in North Iceland.

Elucidating Rheological Properties of Cementitious Materials Containing Fly Ash and Nanosilica by Machine Learning.

Researching the rheology contributes to enhancing the physical and mechanical properties of concrete and promoting material sustainability. Despite the challenges posed by numerous factors influencing viscosity, leveraging machine learning in the era of big data emerges as a viable solution for predicting the general properties of construction materials. This study aims to create models to forecast the rheological properties of cementitious materials containing fly ash and nanosilica. Four models-Random Forest, XGBoost, ANN, and RNN (Stacked LSTM)-are employed to predict and assess shear rate versus shear stress and shear rate versus apparent viscosity curves. Through hyperparameter adjustments, RNN (Stacked LSTM) exhibits excellent performance, achieving a coefficient of determination (R2) of 0.9582 and 0.9257 for the two curves, demonstrating superior statistical parameters and fitting effects. The RNN (Stacked LSTM) exhibited a better generalization ability, suggesting it will be more reliable for future prediction in cementitious material viscosity.

Application of a shear cell for the simulation of extrusion to test the structurability of raw materials

The required raw material properties and the mechanisms behind fibrous structure formation during high moisture extrusion cooking (HMEC) to produce plant-based meat analogues are not sufficiently understood. The implementation of new raw materials is therefore a labour-intensive, empirical endeavour. Hence, this research explores the potential of a high-pressure shear cell (HPSC) as a tool for rapid screening of raw materials for HMEC suitability. During these texturisation tests, material viscosity was monitored to gain deeper mechanistic insights. For that, hydrated pea protein isolate (PPI) and soy protein concentrate (SPC) were sheared at 5-40 s−1 in the HPSC with a cone-plate or plate-plate geometry under extrusion-like conditions at 140–160 °C and 20 bar. Fibrous structures were successfully generated with both tested plant proteins, but reproducibility was limited to trials with SPC. Monitoring of viscoelastic properties revealed an onsetting polymerisation, followed by a decrease in viscosity, indicating fracturing of the forming network as underlying structuring mechanism. The suggested fracturing of an emerging network aligns with an observed increase in fibrousness in the plate-plate geometry compared to the cone-plate geometry due to the radial shear rate gradient. Finally, this research showcased a high similarity of the process-structure response pattern for SPC between the applied HPSC process and HMEC. Yet, further modifications, such as increasing the mechanical energy input capacity, are required to effectively apply HPSC processing for the prediction of the structuring behaviour in HMEC for the broad range of existing raw materials.

Ultrasonic vibration-assisted high-resolution electrohydrodynamic (EHD) printing

Electrohydrodynamic (EHD) printing has become a promising and cost-effective technique for producing high-resolution and large-scale features. One widely recognized obstacle in EHD printing is nozzle clogging due to solvent evaporation or ink polymerization. Moreover, printing highly viscous materials often requires pressure or other external force to assist the ink flow during the printing, which increases the complexity of process control and the required energy. In this work, we developed a novel ultrasonic vibration-assisted EHD printhead and associated process to effectively eliminate the nozzle clogging for the printing of high-viscosity and high-evaporation-rate inks. A series of experimental tests were conducted to characterize the printhead design and process parameters (i.e., vibration frequency, vibration amplitude, and printing voltage). The results demonstrated that superimposing ultrasonic vibration on the EHD printing nozzle can effectively enhance current EHD printing capabilities, such as reducing required pressure, eliminating nozzle clogging, and providing stable and continuous printing for high viscosity and high solvent evaporation rate material. With the optimal parameters, a filament with a diameter of around 1 µm can be continuously printed. In the paper, we successfully applied this developed ultrasonic-assisted EHD process to print high-resolution 2D patterns.

Complex dispersion analysis of viscoelastic effects on elastic waves in three-dimensional single-phase metamaterials

In this paper, we study the propagation of elastic waves in three-dimensional single-phase metamaterials using the finite element method. Both elastic and viscoelastic scenarios are considered, where the Kelvin-Voigt model is used to describe the solid material viscosity. We explore the influence of material viscosity on the complex band diagrams and the transmission spectra in detail. It is found that the single-phase metamaterials support both the Bragg scattering and locally resonant band gaps. When a small viscosity is introduced, the wave attenuation within the locally resonant band gaps degrades. However, such a small viscosity has negligible effects on the Bragg scattering band gaps. As the material viscosity increases, the wave attenuation is mainly ascribed to the material viscosity rather than the band gap effects. Additionally, the attenuation behavior of evanescent waves can be accurately predicted from the imaginary part of wave vectors identified in the complex band structures. This work provides a reference for the practical applications of viscoelastic metamaterials.

All-printed MXene/WS2-based flexible humidity sensor for multi-scenario applications

As non-contact sensing, non-invasive medical diagnostics and environmental humidity monitoring technologies advance, the demand for high-performance humidity sensors is becoming increasingly apparent. Ti3C2Tx MXene is an attractive humidity-sensitive candidate due to its excellent chemical activity and hydrophilic surface. However, achieving fast responsiveness and high sensitivity of dynamic changes in humidity is still challenging. Herein, this work introduced a WS2-modified Ti3C2Tx MXene composite material as humidity sensing material with superior sensing performance. Then sodium carboxymethyl cellulose solution was added to the Ti3C2Tx MXene/WS2 composite material to adjust viscosity of the composite material, enabling the formulation of printable humidity-sensitive ink. Utilizing screen-printing technology, a mass-producible humidity sensor (CMXW2) was successfully fabricated. Experimental results show that the sensor has short response and recovery times of 4.65 s/1.33 s in the relative humidity of 11–97 % with a high humidity response of 1707 % and excellent mechanical properties. The efficacy of the humidity sensor for skin moisture monitoring, real-time respiration monitoring, and non-contact switch was also evaluated. The successful demonstration of multi-scenario applications using the CMXW2 humidity sensor opens up new possibilities and applications in the fields of smart healthcare and non-contact sensing.

Flow and heat transfer of non-miscible micropolar and Newtonian fluid in porous channel sandwiched between parallel plates

An analytical approach has been considered to inspect the flow and heat transfer of immiscible Newtonian and micropolar fluid in a porous medium. This study aims to elucidate the Darcy effect on the model in which isotropic porous regions with different permeability are used. The model of the current problem explains that the flow region is divided into two regions: Newtonian fluid flows in the upper layer, and micropolar fluid flows in the lower layer. Different constant temperatures imposed at boundary walls, heat transfer does not affect the pressure gradient. The governing equations are solved analytically by applying a linear differential equation (LDE). The effect of associated physical parameters such as material parameter, Darcy number, Eckert number, Prandtl number, and viscosity ratio on velocity, micro-rotation, heat transfer, flow rate, heat transfer rate, wall shear stress, and Nusselt number have been inspected. The most significant finding of this research work is that increasing the Darcy number signifies enhanced permeability, resulting in a higher flow rate. The heat transfer rate at the top occurs maximum when the material parameter’s range is minimum while raising the viscosity ratio leads to an increasing heat transfer rate at the bottom. Enhancement in material parameter influences Nusselt number and decreases in nature. The findings of our study are verified with the previously established results. The present work has a setup that is useful in petroleum extraction, transport problems in reservoir rock of an oil field, improving nutrient transport and thermal regulation in tissue engineering, and designing more efficient drug delivery systems and biomedical devices.

MINIMIZING TEMPERATURE DEVIATIONS IN RUBBER MIXING PROCESS BY USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables, including mixing parameter settings, rheological behavior, compound viscosity, and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an artificial intelligence–based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method uses feedforward neural networks (FFNs) to enable early identification of batch-specific temperature deviations, thereby enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a mean absolute percentage error of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.

Diversity of short-term DC outcomes in bulk-fill RBCs subjected to a 3 s high-irradiance protocol

ObjectivesTo determine the short-term (5 min) initial effects of a high-irradiance light-curing (LC) protocol on light transmission (LT%), radiant exposure (RE) and degree of conversion (DC%) of different bulk-fill resin-based composites (RBCs). Materials and methodsSix bulk-fill composites with different viscosities were investigated: OBF (One Bulk Fill, 3 M), EB (Estelite bulkfill,Tokuyama), PFill, PFlow, ECeram and EFlow (PowerFill, Poweflow, Tetric EvoCeram bulkfill, Tetric Evoflow bulkfill, Ivoclar), subjected to different LC protocols: one ultra-high-intensity (3 W/cm2 −3 s via PowerCure LCU) and two conventional (1.2 W/cm2 −10 s and 20 s via PowerCure and Elipar S10 LCUs). Specimens (n = 5) were polymerized within their molds (ϕ5 mm × 4 mm depth) to determine LT% and RE at 4 mm using a MARC-LC spectrometer. For real-time DC% measurements by FTIR, similar molds were utilized. Data were analyzed by one-way ANOVA and Tukey post-hoc tests at 5 % significance. ResultsRegardless of the applied LC protocols, OBF and low-viscosity RBCs (EB, PFlow and EFlow) had the lowest and highest LT%, RE, DC% and RPmax, respectively. RE results of all RBCs were in the same sequence: Elipar-20 s > PCure-10 s > PCure-3 s. DC% of PFill and PFlow displayed no significant difference between the applied LC protocols (p > 0.05). The polymerization kinetic in all materials was well described by an exponential sum function (r2 varied between 0.85 and 0.98), showing a faster polymerization with the PCure-3 s protocol. SignificanceThe measurement of LT% and DC% at 5 min gave an insight into the developing polymerization process. The initial response of these bulk-fill composite to a high-irradiation protocol varied depending on their composition and viscosity, being faster for low viscosity materials. Nevertheless, even though multiple resin composites are designed to be efficient during photopolymerization, care should be taken when selecting materials/curing protocol.

Droplet volume prediction methods in electrohydrodynamic jet printing based on multi-source data fusion

Electrohydrodynamic jet printing technology has garnered significant interest in additive manufacturing due to its advantages in higher resolution printing and greater viscosity material applicability. Nonetheless, the challenges associated with femtoliter-level droplets and the micrometer-scale printing space have rendered traditional optical observation methods for volume calculation impractical for integration into the printing systems. Obtaining the droplet volume accurately has become a formidable challenge in electrohydrodynamic printing. While existing research utilizes machine learning for the end-to-end prediction of process parameters to droplet volume, traditional prediction methods cannot achieve online prediction due to the complex spray states characteristic of electrohydrodynamic jet printing and pose integration difficulties within printing systems. This paper introduces a multi-source data fusion model suitable for electrohydrodynamic printing droplet volume prediction. The model employs the VGG network to extract features from Taylor cone images in the jetting state and synchronously fuse these with process parameter features. The fused features are then correlated with droplet volume labels through the MLP network for comprehensive model training. The performance of the proposed model has improved by 10% in prediction accuracy compared to single modal data. We integrated the prediction method into an electrohydrodynamic jet printing system and experimented with printing pixel-pit substrates. The results indicate that the prediction accuracy of the volume prediction system is over 92%. The printing efficiency has improved approximately 3 times compared to the traditional method, significantly enhancing overall performance.

On the rheological properties of silica – Polyethylene oxide dispersions: Shake gels I the effect of polymer concentration and temperature

Shake-gels are aqueous mixtures of silica and Poly(ethylene-oxide) (PEO) that form reversible gels when subjected to an applied force, such as shaking. This shear-thickening effect can be observed using a rheometer, which shows discrete, sudden changes in the material's viscosity. Preliminary qualitative investigations have shown that the gelation time varies depending on the conditions used. We also measure the time required for the gel to relax and revert to its liquid state. To quantify the relationships between these variables and better understand the process and kinetics, a systematic investigation was conducted, focusing on the effects of polymer concentration and temperature on gelation and relaxation times. Experiments were carried out using a rheometer with double-gap concentric cylinder geometry, applying a constant shear rate to samples with varying polymer concentrations and temperatures. The results showed that, at a fixed shear rate, gelation and relaxation times varied significantly, ranging from a few seconds to over an hour. Both gelation and relaxation occurred more rapidly as polymer concentration increased. Additionally, higher temperatures led to faster gelation and relaxation times and vice versa.

Modified Combined Impression Technique for Cawood & Howell Class V/VI Mandibular Ridges: A Case Report

Geriatric prosthodontic rehabilitation with compromised residual ridge has become challenging, the principal concern being rejuvenating and maintaining complex stomatognathic functional harmony with severe residual ridge resorption (RRR). Modification of contemporary treatment techniques should be considered to achieve a patient’s functional and esthetic requirements. Contemporary prosthodontics utilizing combination of Neutral zone technique (NZ) and all green impression (AGI) for the management of Cawood & Howell Class V/VI (severely resorbed) ridges are innovative techniques for the construction of removable prosthesis with a profound effect in denture stability and retention, particularly in mandible. NZ implies the principle of synergistic activity of Oro-muscular function in denture stability by arranging the prosthetic teeth within the neutral zone area, hence preventing their interfere with the oro-muscular function, while AGI aimed at achieving prosthesis retention through functional moulding and the viscosity of impression material helps disseminate the forces transmitted to the denture-bearing area reducing the potential for discomfort arise from the soft tissue folds over atrophic ridge. This case report attempts to present a combination of modified impression techniques utilizing all green impression techniques and neutral zone techniques in a 66-year-old patient for C&H Class V mandibular ridge. The goal of this technique is to provide optimal stability covering the maximum denture bearing area and selectively distributing the pressure and to produce a denture molded by oro-muscular function in harmony with its surrounding structures thereby enhancing stability and retention.

Effects of Er,Cr:YSGG Laser Surface Treatments and Composites with Different Viscosities on the Repair Bond Strength of CAD/CAM Resin Nanoceramic.

This study aims to evaluate the repair micro-shear bond strength of the CAD/CAM resin nanoceramic block treated using four different surface treatments and composite resins of different viscosities. For the current study, 96 samples with dimensions of 14 × 12 × 2 mm were obtained from a CAD/CAM resin nanoceramic block (Cerasmart) with a low-speed precision cutting saw under water cooling. The relevant samples were randomly divided into four groups according to the surface treatment processes: grinding with diamond bur, aluminum oxide airborne-particle abrasion, long-pulse laser, and short-pulse laser. Following silane application, universal adhesive was applied to all surface-treated samples and cured with an LED for 10 s. The samples prepared for the repair procedure were divided into two subgroups (microhybrid composite and injectable composite) according to the viscosity of the repair material to be used (n = 12). After the repair procedure, care was taken to keep the samples in distilled water in an incubator at 37 °C for 24 h. The repair micro-shear bond strength values (μSBSs) of CAD/CAM resin nanoceramic-composite resin complexes were tested. In addition, randomly selected samples from each group were examined with a scanning electron microscope to evaluate the surface topography after both surface treatments and the micro-shear bond strength test. Data were analyzed by two-way ANOVA and Bonferroni test. It was determined that the surface treatment preferred in the repair protocol significantly affected the μSBS value (p < 0.001). While the highest μSBS value was obtained with the short-pulse laser airradiation group, the lowest μSBS values were found in samples with long pulse laser irradiation. However, samples grinded with a bur and airborne-particle abrasion showed similar μSBS values (p > 0.05). The preferred composite viscosity in the repair procedure has a significant effect on the μSBS value (p < 0.001). However, the interaction between the surface treatment and the viscosity of the repair composite does not affect the μSBS values in a statistically significant way (p = 0.193). It may be recommended to clinicians to repair CAD/CAM resin nanoceramic restoration surfaces with injectable composites or after treatment with short-pulse lasers.

Influence of viscosity and fiber reinforcement of resin composite on fracture strength and failure mode of restored molars.

To evaluate the fracture behavior of human molars with extensive MOD restorations using short-fiber-reinforced resin composite of varying viscosities. Human molars were randomly divided into seven groups (n = 12): intact teeth (control); restoration using conventional high-viscosity resin composite without (Filtek Z350XT, 3M) or with fibers (everX Posterior, GC); conventional low-viscosity resin composite without (Filtek Supreme Flowable, 3M) or with fibers (everX Flow Dentin Shade, GC); bulk-fill low-viscosity resin composite (Filtek Bulk Fill Flow, 3M) or with fibers (everX Flow Bulk Shade, GC). Restorations were performed on extensive MOD preparations, following the manufacturers' recommendations for each material. Specimens underwent fracture strength testing (N) and fracture pattern (%) categorized as repairable, possibly repairable, or non-repairable. Results were analyzed using a generalized linear model (N) and Fisher's exact test (%), with α = 0.05. Restorations performed with high-viscosity materials showed fracture strength values similar to the control and higher than those of restorations using low-viscosity resin composites (p < 0.0001), except for the bulk-fill low-viscosity resin composite with fibers (p > 0.05). Teeth restored using low-viscosity resin composite with fibers showed a higher % of repairable and possibly repairable fractures than the control (p = 0.0091). The viscosity of materials mediated the fracture strength, with restorations using high-viscosity resin composites promoting values similar to the intact tooth; however, the presence of fibers influenced the fracture pattern. Teeth with MOD cavities restored with high-viscosity resin composites showed similar fracture strength to intact teeth. Fiber-reinforced low-viscosity resin composite for the base of restoration resulted in a more repairable/possibly repairable fracture pattern.

Pre-structural patterning approach for enhancing flow characteristics of B-stage film-type epoxy molding compound

Epoxy molding compound (EMC) compositions of silica powder and epoxy matrix have been utilized as encapsulation for semiconductor packaging over the past decades. Among various forms of EMC, film-type EMC offers untapped potential. Utilizing EMC film as an encapsulation material allows larger and more uniform encapsulation behavior especially for panel-level and wafer-level packaging applications. In material aspects, as increases silica contents in EMC film, it can encounter a trade-off relationship between the coefficient of thermal expansion (CTE) and the material's viscosity. As silica content increases, the CTE tends to decrease, which enhances warpage behavior. However, viscosity also tends to increase, which reduces flowability. To overcome these tradeoff relationships, in our study, we have demonstrated a pre-structural patterning approach to enhance flowability of B-stage EMC film without changing the raw compositions of material. By doing that, we can suppose increased flowability of film while having similar thermomechanical properties. For patterning while maintaining B-stage, patterned protection film was fabricated using a brass-based metallic mold and a PDMS/PUA-based soft mold. Subsequently, pre-structural patterned EMC was fabricated by transferring the patterned protection film onto a B-stage EMC film using a laminator. The effectiveness of patterning on EMC film was investigated in both numerical and experimental molding analysis according to rheological, chemical, and thermomechanical characterization. The pre-structural patterning approach demonstrated enhanced flow characteristics via a shortened flow path despite the increased viscosity caused by higher silica content.

Sequence programmable nucleic acid coacervates.

Nature uses bottom-up self-assembly to build structures with remarkable complexity and functionality. Understanding how molecular-scale interactions translate to macroscopic properties remains a major challenge and requires systems that effectively bridge these two scales. Here, we generate DNA and RNA liquids with exquisite programmability in their material properties. Nucleic acids are negatively charged, and in the presence of polycations, they may condense to a liquid-like state. Within these liquids, DNA and RNA retain sequence-specific hybridization abilities. We show that intermolecular hybridization in the condensed phase cross-links molecules and slows down chain dynamics. This reduced chain mobility is mirrored in the macroscopic properties of the condensates. Molecular diffusivity and material viscosity scale with the intermolecular hybridization energy, enabling precise sequence-based modulation of condensate properties over orders of magnitude. Our work offers a robust platform to create self-assembling programmable fluids and may help advance our understanding of liquid-like compartments in cells.

A thermal fluid dynamic model for the melt region during the laser powder bed fusion of polyamide 11 (PA11)

Studying the physical characteristics of the melt region in polymer-based laser powder bed fusion (PBF-LB/P) offers good comprehension that is essential for quality control, process optimization, understanding of material behavior, and preserving process consistency. Among the few numerical modelling investigations of PBF-LB/P, this study focuses on 3D multiphysics simulation of melt pool morphologies, beginning with powder bed preparation incorporating the actual particle size distribution using the discrete element method (DEM). A thermal fluid dynamic numerical model is then developed based on the finite volume method (FVM) using Flow-3D Weld to simulate PBF-LB while using polyamide 11 (PA11) as the primary material. The cooling after sintering plays a crucial role for the formation of the melt region due to the high viscosity of the polymer material effectively resulting in creeping flow. Here, we propose a model for both sintering as well as the subsequent cooling step, a two-step model capable of simulating the finished structure accurately with relatively low computational resources. The model's accuracy is verified through a mesh dependency analysis and its predictive power via validation against internal single-track experiments providing dimension data on the melt region width and depth. The simulation results allow for a detailed exploration of the effects of laser settings on melt region morphologies and the occurrence of defects. The model's scope is further extended to multi-track simulation, revealing the connection between surface roughness and laser settings. In general, this study provides significant contributions to the comprehension of PBF-LB/P methodologies which in turn can be used for further process improvement.