Development and validation of a digital twin of the human lower jaw under impact loading by using non-linear finite element analyses.

Mandibular fractures are one of the most frequently observed injuries within craniofacial region mostly due to tumor-related problems and traumatic events, often related to non-linear effects like impact loading. Therefore, a validated digital twin of the mandible is required to develop the best possible patient-specific treatment. However, there is a need to obtain a fully compatible numerical model that can reflect the patients' characteristics, be available and accessible quickly, require an acceptable level of modeling efforts and knowledge to provide accurate, robust and fast results at the same time under highly non-linear effects. In this study, a validated simulation methodology is suggested to develop a digital twin of mandible, capable of predicting the non-linear response of the biomechanical system under impact loading, which then can be utilized to design treatment strategies even for multiple fractures of the mandibular system. Using Computed Tomography data containing cranial (skull) images of a patient, a 3-dimensional mandibular model, which consists cortical and cancellous bones, disks and fossa is obtained with high accuracy that is compatible with anatomical boundaries. A Finite Element Model (FEM) of the biomechanical system is then developed for a three-level validation procedure including (A) modal analysis, (B) dynamic loading and (C) impact loading. For the modal analysis stage: Free-free vibration modes and frequencies of the system are validated against cadaver test results. For the dynamic loading stage: Two different regions of the mandible are loaded, and maximum stress levels of the system are validated against finite element analyses (FEA) results, where the first loading condition (i) transfers a 2000 N force acting on the symphysis region and, the second loading condition (ii) transfers a 2000 N force acting on the left body region. In both cases, equivalent muscle forces dependent on time are applied. For the impact loading stage: Thirteen different human mandibular models with various tooth deficiencies are used under the effects of traumatic impact forces that are generated by using an impact hammer with different initial velocities to transfer the impulse and momentum, where contact forces and fracture patterns are validated against cadaver tests. Five different anatomical regions are selected as the impact site. The results of the analyzes (modal, dynamic and impact) performed to validate the digital twin model are compared with the similar FEA and cadaver test results published in the literature and the results are found to be compatible. It has been evaluated that the digital twin model and numerical models are quite realistic and perform well in terms of predicting the biomechanical behavior of the mandible. The three-level validation methodology that is suggested in this research by utilizing non-linear FEA has provided a reliable road map to develop a digital twin of a biomechanical system with enough confidence that it can be utilized for similar structures to offer patient-specific treatments and can help develop custom or tailor-made implants or prosthesis for best compliance with the patient even considering the most catastrophic effects of impact related trauma.

Printability for additive manufacturing with machine learning: Hybrid intelligent Gaussian process surrogate-based neural network model for Co-Cr alloy.

AM has revolutionized the manufacturing industry, involving several operating parameters that may affect the properties of the final manufactured part. In AM, LPBF has proved its reliability in producing dense components; however, process development for every material necessitates extensive testing. Even the tiniest change can negate all the data for the same material. It is vital to have a P-P correlation that can train itself following a change in powder or machine to achieve defects-free parts and optimal properties. These goals cannot be met alone by multi-physics. One of the ways to address this issue is to apply ML, but it requires a huge data set for training and testing purposes. A framework has been developed for Co-Cr S-S curves to resolve this issue. Twenty-two experimental S-S curves have been generated to produce YS, TS, and EL data points. In combination with DNN, these data points have been applied to the validated and tested GPS-surrogate model to develop a smart processing window to achieve desired YS, TS, and EL. LP, LSS, HD, and PLT have been selected during the whole framework as inputs, while YS, TS, and EL have been classified as outputs. The output of the smart window was verified experimentally. It is found that the highest YS (1110.91 MPa) is attained using LP = 180 W, LSS = 600 mm/s and HD = 70 µm, while least YS (645.05 MPa) is identified using LP = 160 W, LSS = 900 mm/s and HD = 70 µm. For TS, the maximum (165.91 MPa) and minimum (689.73 MPa) values have been achieved using LP = 180 W, LSS = 900 mm/s and HD = 70 µm, and LP = 180 W, LSS = 1000 mm/s and HD = 70 µm, respectively. In the case of EL, LP = 180 W, LSS = 700 mm/s and HD = 70 µm, and LP = 180 W, LSS = 600 mm/s and HD = 70 µm, resulted 23.04% and 0.789% EL, respectively. Using CC, LP and HD did not significantly affect the TS, YS, and EL, while a negative relationship has been found for LSS with TS, YS, and EL. The smart processing window showed that the YS and TS could be achieved at low-high LP and low LSS at the cost of EL. This study provides a technique for framework development in the case of P-P relation based on the provided inputs and the corresponding outputs, leading toward process smartification.

Advances in Laser Additive Manufacturing of Cobalt-Chromium Alloy Multi-Layer Mesoscopic Analytical Modelling with Experimental Correlations: From Micro-Dendrite Grains to Bulk Objects.

This study presents two analytical models for the laser powder bed fusion (LPBF) process. To begin, the single layer's dimensions were measured using principal operating conditions, including laser power, laser scanning speed, powder layer thickness, and hatch distance. The single-layer printing dimensions were transformed into multi-layer printing using the hatch distance. The thermal history of the printed layers was used as an input to the Johnson-Mehl-Avrami-Kolmogorov model to estimate the average dendrite grain size. LPBF experiments were conducted for a Cobalt-chromium (Co-Cr) alloy to validate the developed model. The average dendrite grain size was estimated using a scanning electron microscope (SEM) combined with "Image J" software. The Vickers hardness test was performed to correlate the average dendrite grain size and operating conditions. A 10-15% mean absolute deviation was presented between experiments and simulation results. In all samples, a Co-based γ-FCC structure was identified. An inverse correlation was established between the laser power and smaller average dendrite grain, while a direct relationship has been determined between laser scanning speed and average dendrite grain size. A similar trend was identified between hatch distance and average dendrite grain size. A direct link has been determined between the average dendrite grain size and hardness value. Furthermore, a direct relationship has connected the laser volume energy density and hardness value. This study will help experimentalists to design operating conditions based on the required grain size and corresponding mechanical characteristics.

Influence of Silicon Carbide on Direct Powder Bed Selective Laser Process (Sintering/Melting) of Alumina.

The powder bed selective laser process (sintering/melting) has revolutionised many industries, including aerospace and biomedicine. However, PBSLP of ceramic remains a formidable challenge. Here, we present a unique slurry-based approach for fabricating high-strength ceramic components instead of traditional PBSLP. A special PBSLP platform capable of 1000 °C pre-heating was designed for this purpose. In this paper, PBSLP of Al2O3 was accomplished at different SiC loads up to 20 wt%. Several specimens on different laser powers (120 W to 225 W) were printed. When the SiC content was 10 wt% or more, the chemical interaction made it difficult to process. Severe melt pool disturbances led to poor sintering and melting. The structural analysis revealed that the micro-structure was significantly affected by the weight fraction of SiC. Interestingly, when the content was less than 2 wt%, it showed significant improvement in the microstructure during PBSLP and no effects of LPS or chemical interaction. Particularly, a crack pinning effect could be clearly seen at 0.5 wt%.

Laser Melting Deposition Additive Manufacturing of Ti6Al4V Biomedical Alloy: Mesoscopic In-Situ Flow Field Mapping via Computational Fluid Dynamics and Analytical Modelling with Empirical Testing.

Laser melting deposition (LMD) has recently gained attention from the industrial sectors due to producing near-net-shape parts and repairing worn-out components. However, LMD remained unexplored concerning the melt pool dynamics and fluid flow analysis. In this study, computational fluid dynamics (CFD) and analytical models have been developed. The concepts of the volume of fluid and discrete element modeling were used for computational fluid dynamics (CFD) simulations. Furthermore, a simplified mathematical model was devised for single-layer deposition with a laser beam attenuation ratio inherent to the LMD process. Both models were validated with the experimental results of Ti6Al4V alloy single track depositions on Ti6Al4V substrate. A close correlation has been found between experiments and modelling with a few deviations. In addition, a mechanism for tracking the melt flow and involved forces was devised. It was simulated that the LMD involves conduction-mode melt flow only due to the coaxial addition of powder particles. In front of the laser beam, the melt pool showed a clockwise vortex, while at the back of the laser spot location, it adopted an anti-clockwise vortex. During printing, a few partially melted particles tried to enter into the molten pool, causing splashing within the melt material. The melting regime, mushy area (solid + liquid mixture) and solidified region were determined after layer deposition. This research gives an in-depth insight into the melt flow dynamics in the context of LMD printing.

Keyhole Formation by Laser Drilling in Laser Powder Bed Fusion of Ti6Al4V Biomedical Alloy: Mesoscopic Computational Fluid Dynamics Simulation versus Mathematical Modelling Using Empirical Validation.

In the laser powder bed fusion (LPBF) process, the operating conditions are essential in determining laser-induced keyhole regimes based on the thermal distribution. These regimes, classified into shallow and deep keyholes, control the probability and defects formation intensity in the LPBF process. To study and control the keyhole in the LPBF process, mathematical and computational fluid dynamics (CFD) models are presented. For CFD, the volume of fluid method with the discrete element modeling technique was used, while a mathematical model was developed by including the laser beam absorption by the powder bed voids and surface. The dynamic melt pool behavior is explored in detail. Quantitative comparisons are made among experimental, CFD simulation and analytical computing results leading to a good correspondence. In LPBF, the temperature around the laser irradiation zone rises rapidly compared to the surroundings in the powder layer due to the high thermal resistance and the air between the powder particles, resulting in a slow travel of laser transverse heat waves. In LPBF, the keyhole can be classified into shallow and deep keyhole mode, controlled by the energy density. Increasing the energy density, the shallow keyhole mode transforms into the deep keyhole mode. The energy density in a deep keyhole is higher due to the multiple reflections and concentrations of secondary reflected beams within the keyhole, causing the material to vaporize quickly. Due to an elevated temperature distribution in deep keyhole mode, the probability of pores forming is much higher than in a shallow keyhole as the liquid material is close to the vaporization temperature. When the temperature increases rapidly, the material density drops quickly, thus, raising the fluid volume due to the specific heat and fusion latent heat. In return, this lowers the surface tension and affects the melt pool uniformity.

Laser Powder Bed Fusion (LPBF) of In718 and the Impact of Pre-Heating at 500 and 1000 °C: Operando Study.

The morphology of a melt pool has a critical role in laser powder bed fusion (LPBF). Nevertheless, directly characterizing the melt pool during LPBF is incredibly hard. Here, we present the melt pool flow of the entire melt pool in 3D using mesoscopic simulation models. The physical processes occurring within the melt pool are pinpointed. The flow patterns throughout the same are exposed and measured. Moreover, the impact of pre-heating at 500 and 1000 °C has been described. The study findings offer insights into LPBF. The findings presented here are critical for comprehending the LPBF and directing the establishment of improved metrics for process parameters optimization.

Full-Field Mapping and Flow Quantification of Melt Pool Dynamics in Laser Powder Bed Fusion of SS316L.

Laser powder bed fusion (LPBF) has a wide range of uses in high-tech industries, including the aerospace and biomedical fields. For LPBF, the flow of molten metal is crucial; until now, however, the flow in the melt pool has not been described thoroughly in 3D. Here, we provide full-field mapping and flow measurement of melt pool dynamics in laser powder bed fusion, through a high-fidelity numerical model using the finite volume method. The influence of Marangoni flow, evaporation, as well as recoil pressure have been included in the model. Single-track experiments were conducted for validation. The temperature profiles at different power and speed parameters were simulated, and results were compared with experimental temperature recordings. The flow dynamics in a single track were exposed. The numerical and experimental findings revealed that even in the same melting track, the melt pool's height and width can vary due to the strong Marangoni force. The model showed that the variation in density and volume for the same melting track was one of the critical reasons for defects. The acquired findings shed important light on laser additive manufacturing processes and pave the way for the development of robust, computational models with a high degree of reliability.