International Journal of Material Forming最新文献

{"title":"Optimization of structural parameters for promoting formability of 3D tufted composite preforms","authors":"Zhangjie Xu,&nbsp;Jianxin Tang,&nbsp;Ruikang Yin,&nbsp;Yanfei Lyu,&nbsp;Hao Shen,&nbsp;Peng Wang","doi":"10.1007/s12289-026-02057-z","DOIUrl":"10.1007/s12289-026-02057-z","url":null,"abstract":"<div><p>The formability of tufted composite preforms is strongly dependent on the in-plane shear, out-of-plane bending and interlayer friction behaviors. However, owing to a lack of systematic studies concerning the influence of various tufting parameters on these forming behaviors, effective strategies for optimizing tufting parameters for double-curved forming process remain unavailable. In this paper, orthogonal experiments with range analysis and ANOVA are employed to investigate the relationships between the multiple tufting parameters and the corresponding forming behaviors. The results indicate that in-plane angle of tufting yarn dominates in-plane shear stiffness of preforms (contribution ratio is 99.69%), tufting space has the most significant effect on out-of-plane bending stiffness of preforms (contribution ratio is 45.20%), and loop length of tufting yarn is the key parameter governing interlayer friction (contribution ratio is 91.07%). An optimization strategy for these tufting parameters is proposed to reduce the wrinkles, and hemispherical forming tests have been applied for verification.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation of hot isostatic pressing densification in metal-injection-molded FD-0205","authors":"Shuo Chen,&nbsp;Yuqi Feng,&nbsp;Lida Che,&nbsp;Pengjie Zhang,&nbsp;Haofeng Li,&nbsp;Xiangyang Li","doi":"10.1007/s12289-026-02058-y","DOIUrl":"10.1007/s12289-026-02058-y","url":null,"abstract":"<div><p>Hot isostatic pressing (HIP) is an effective post-processing technique for eliminating internal porosity in metal injection molded (MIM) components; however, the optimization of process parameters still largely relies on empirical approaches, which limits process reliability and efficiency. In this study, a mechanism-oriented numerical framework is developed to investigate stress-assisted pore closure behavior in FD-0205 steel during HIP. Realistic pore morphologies extracted from metallographic images are reconstructed using image-based processing to establish representative two-dimensional microstructural models. A fully coupled transient finite element model incorporating temperature-dependent elastoplastic deformation and high-temperature creep is then implemented to simulate the evolution of pore closure throughout the complete HIP thermal cycle. The results show that densification is governed by the coupled interaction between plastic collapse and creep deformation, which is strongly dependent on pore size, morphology, and local stress redistribution. Larger pores tend to undergo earlier stress-driven collapse due to higher stress concentration, whereas smaller pores exhibit more gradual closure dominated by time-dependent creep deformation. The evolution of equivalent stress, plastic strain, and creep strain exhibits a non-monotonic behavior during heating, holding, and cooling stages, reflecting transitions in the dominant deformation mechanisms. Experimental HIP results validate the simulation predictions, achieving a maximum relative density of 97.82%, with good agreement between numerical and experimental densification trends. Overall, the proposed image-based coupled elastoplastic–creep model provides a reliable framework for predicting pore evolution during HIP and offers a physics-based approach for process parameter optimization of MIM components.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability-aware compressive-strength prediction and inverse mix design of fly ash–GGBFS geopolymer concrete via conformal prediction","authors":"Gaurav Jagad,&nbsp;Amit Thoriya","doi":"10.1007/s12289-026-02054-2","DOIUrl":"10.1007/s12289-026-02054-2","url":null,"abstract":"<div><p>This study develops a reliability-aware machine-learning framework for compressive-strength prediction and inverse mix design of fly ash–GGBFS geopolymer concrete using a structured database of 672 specimens. Eight raw mix and curing variables were used after removing constant dosage-related columns. Thirteen regression models were first screened using 10-fold cross-validation, and the top models were further tuned using Bayesian optimisation. To move beyond point prediction, split conformal prediction, K-fold residual conformal prediction, and jackknife-after-bootstrap intervals were evaluated at 95%, 90%, and 80% confidence levels. GradientBoost achieved the highest cross-validated accuracy and was retained for residual diagnostics, Mondrian conformal analysis, convergent interpretability, and inverse design. LightGBM achieved the lowest held-out test error and, when paired with K-fold residual conformal prediction, provided the recommended 90% reliability-aware prediction configuration, with PICP = 0.9703, MPIW = 1.186 MPa, and ES = 0.9739. Nearest-neighbour applicability-domain analysis showed that the high R² values mainly reflect dense interpolation within the structured FA–GGBFS experimental envelope rather than unrestricted extrapolation. SHAP, Morris, and PAWN analyses consistently identified curing time, recycled aggregate content, and aggregate composition as influential variables. Finally, local adaptive conformal thresholds were coupled with NSGA-II to identify six deduplicated strength–uncertainty design regions. The framework is useful for in-envelope reliability-aware screening of FA–GGBFS mixes, while transfer to new geopolymer chemistries requires external validation and recalibration.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of surrogate models to FE simulation for a sizing press in a hot rolling mill","authors":"Hiroto Goto,&nbsp;Peter Eberhard","doi":"10.1007/s12289-026-02042-6","DOIUrl":"10.1007/s12289-026-02042-6","url":null,"abstract":"<div><p>In steel production plants, finite element (FE) simulations are widely used to develop plastic deformation processes such as rolling and forging. However, these simulations are large-scale and highly nonlinear, and therefore require long computation times, which makes systematic optimization of process conditions difficult. In this study, a POD-based non-intrusive reduced order model (NIROM), which is used here as a surrogate model, is applied to FE simulations of a sizing press in a hot rolling mill to address this challenge. The method combines Proper Orthogonal Decomposition (POD) with machine-learning-based regression to learn from existing simulation results and predict unknown process conditions. Gaussian process regression (GPR) is employed as the regression model. This approach accelerates computations while preserving the relevant physical characteristics of the solution. For slab displacement, POD-based NIROM with Gaussian process regression as developed in this study achieves a relative root mean square error (RMSE) of approximately 5 % across all nodes at each time step, although periodic error growth is observed during initial slab–die contact. Excluding the initial contact phase, relative RMSE values in all directions remain under 1 % relative to the characteristic lengths of the slab in the conveyance, thickness, and width directions. While one FE simulation requires about 20 hours on the target computational environment, the surrogate model produces predictions within a few seconds, resulting in a significant reduction in computation time. The results demonstrate that POD-based NIROM with Gaussian process regression is effective for predicting displacement even in simulations involving complex plastic deformation such as sizing press operations. Furthermore, the method provides sufficient accuracy and speed for process development in steel production, indicating its practical applicability. To the best of the authors’ knowledge, this is the first reported application of a POD-based NIROM to FE simulation models involving large-scale plastic deformation in industrial steel production processes.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12289-026-02042-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of cryorolling and subsequent annealing on microstructural evolution, mechanical response, and deep-drawability of 1060 aluminum alloy","authors":"Wenbo zhang,&nbsp;Lijuan Zhu,&nbsp;Zhiqiang Zhang,&nbsp;Hongjie Jia,&nbsp;Mingwen Ren","doi":"10.1007/s12289-026-02053-3","DOIUrl":"10.1007/s12289-026-02053-3","url":null,"abstract":"<div><p>Balancing strength and formability remains a key challenge for commercially pure 1060 aluminum alloy used in deep-drawing applications. In this study, cryorolling with an 80% thickness reduction was combined with subsequent annealing to regulate the microstructure, texture, and deformation behavior of 1060 aluminum alloy, with particular emphasis on deep-drawability. Cryorolling effectively suppressed dynamic recovery and promoted the accumulation of dense deformation substructures, including dislocation walls, dislocation cells, and shear bands, leading to a pronounced increase in strength but a severe loss of ductility. Subsequent annealing at 290 °C for 50 min enabled substantial recovery of the heavily deformed structure while retaining a refined substructure. As a result, the elongation recovered to 21.1%, while the tensile strength remained at 116.4 MPa and the yield strength was maintained at 79.9 MPa. EBSD and texture analyses showed that cryorolling produced strong deformation textures dominated by the γ-fiber and Brass/Copper components, whereas annealing weakened texture intensity and continuity without developing a pronounced recrystallization texture. Despite a relatively low measured r-value of 0.32, the cryogenically rolled and annealed sheet exhibited a limiting drawing ratio of approximately 2.04, higher than that of the fully annealed condition. The improved drawability is therefore more plausibly associated with improved deformation uniformity and delayed thinning/strain localization than with conventional anisotropy optimization alone. These results demonstrate that a cryorolling-annealing route can be used to achieve a favorable strength-ductility-formability balance in 1060 aluminum alloy.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical and thermal properties prediction and optimization of multiscale high performance hybrid biopolymer nanocomposites","authors":"Vaishali Mendhe,&nbsp;Chetan D. Karekar,&nbsp;Shradhesh Marve,&nbsp;Lowlesh N. Yadav,&nbsp;Hirkani Padwad,&nbsp;Nischal Puri,&nbsp;Latika Pinjarkar,&nbsp;Rakesh Patel,&nbsp;Aseel Smerat,&nbsp;P. Jagadesh,&nbsp;Karan Kumar Chaudhari,&nbsp;Vikrant S. Vairagade","doi":"10.1007/s12289-026-02051-5","DOIUrl":"10.1007/s12289-026-02051-5","url":null,"abstract":"<div><p>The motivation for advanced multiscale hybrid bio-polymer nanocomposites stems from the growing and current need to develop superior thermal, mechanical, dielectric, and viscoelastic materials across industries such as automotive, electronics, and biomedicine. Research on bio-polymer nanocomposites, such as those integrating polylactic acid (PLA), graphene nanoplatelets (GNPs), carbon nanotubes (CNTs), and cellulose nanofibers, has so far demonstrated encouraging improvements in material properties. However, current models often struggle to accurately predict and optimize properties, mainly because of the complex interplay among various nanoscale constituents and processing conditions. A multicriteria methodology proposed here will be able to address this limitation using contemporary machine learning techniques in conjunction with a wide variety of optimization methodologies. The use of the proposed multicriteria methodology is dependent upon its ability to model complex behaviors of materials as well as estimate uncertainties associated with these behaviors. The model is useful for predicting thermal, mechanical, dielectric, and viscoelastic properties. Based on data captured through experiments and simulations, Random Forest Regression (RFR) shall analyze the data to identify the parameters relevant to determining the values of these properties. To find multi-objective trade-offs, paper adopt the particle swarm optimization (PSO) framework for search and also perform efficient fine-tuning of processing parameters via Bayesian optimization. The respect of physical laws is ensured through physics informed neural networks (PINNs), thereby improving accuracy in multi-scale modeling. Research carried out an analysis at three distinct levels: single effects of each nanocomponent, binary combinations of any of the four nanocomponents, and simultaneous effects of all four nanocomponents. The results reveal a 10–15% improvement in mechanical and thermal properties, along with a 30–40% reduction in simulation time. Hence, these results are beneficial towards further optimization of high durability and enhanced functional properties for bio-polymer nanocomposites.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based rivet–die design framework for self-piercing riveting of GFRP composite and DQ steel sheets","authors":"Durga Rao Boddepalli,&nbsp;R. Ganesh Narayanan,&nbsp;B. R. Manoj,&nbsp;Brajesh Asati,&nbsp;Ishwar Kapoor","doi":"10.1007/s12289-026-02052-4","DOIUrl":"10.1007/s12289-026-02052-4","url":null,"abstract":"<div><p>Self-piercing riveting (SPR) is increasingly employed for joining lightweight hybrid structures, particularly when traditional fusion welding is unsuitable for materials such as fibre-reinforced composites. However, the rivet-and-die design in SPR remains challenging due to the complex deformation and failure mechanisms involved in composite-metal joining. This study develops a hybrid, data-driven SPR rivet-and-die design framework that integrates machine learning (ML) with numerical simulations. The framework is demonstrated using the SPR of Glass Fibre-Reinforced Polymer (GFRP) composite sheets and Drawing Quality (DQ) steel sheets, illustrating their potential as dissimilar materials for automotive applications. SPR experiments are performed to validate numerical results and establish a reliable numerical simulation approach. The validated simulation setup is then used to perform multiple simulations with variations in rivet length, diameter, hardness, die diameter, die depth, pip height, friction coefficient, and sheet thickness, generating a comprehensive dataset. Additionally, parameter variations are generated using the Conditional Tabular Generative Adversarial Network (CTGAN) to produce statistically balanced synthetic data for the simulation input. The framework leverages simulation data to train predictive ML models and is validated by using experimental data that determine optimal die dimensions for various GFRP/DQ steel thickness combinations. The developed ML framework achieved strong predictive capability, with FE–ML correlation coefficients of <i>r</i> = 0.90, 0.95, and 0.89 for ID, BRT, and CD, respectively. The optimized design predicted by the Random Forest–L-BFGS-B framework showed close agreement with FE validation results, with deviations of only 1.1% for ID and 5.8% for BRT. The ultimate goal is to reduce experimental effort and design iterations by enabling accurate die prediction through the combined use of simulation and data-driven modelling. This approach is expected to be applicable to other hybrid joining and forming processes, as well as to other dissimilar sheet combinations that require efficient, interpretable design development.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crystal plasticity modeling of the Bauschinger effect and asymmetric yielding in pressure-insensitive polycrystals: application to L-bending springback","authors":"Kengo Yoshida,&nbsp;Atsuya Chizuki","doi":"10.1007/s12289-026-02049-z","DOIUrl":"10.1007/s12289-026-02049-z","url":null,"abstract":"<div><p>This study experimentally investigated the plastic behavior of AA5083-O sheets under uniaxial tension and compression, tension–compression–tension, and loading–unloading tests. The flow stress under uniaxial compression was higher than that under uniaxial tension, indicating tension–compression asymmetry. In the tension–compression–tension test, the reversal of the loading direction decreased the flow stress, indicating a Bauschinger effect. After tensile prestraining, the unloading stress–strain curves exhibited a slight nonlinearity, and their slopes decreased below the Young’s modulus. A crystal plasticity model incorporating the non-Schmid effect was developed based on our previous model to describe the Bauschinger effect in aluminum alloy sheets (Yoshida K. 2024 International Journal of Solids and Structures, 291:112697). In addition to the resolved shear stress, plastic slip was influenced by the deviatoric stress component normal to the slip plane. The proposed model successfully reproduced the tension–compression asymmetry of flow stress without introducing pressure sensitivity. The reduction in the unloading slope was modeled by introducing a strain-dependent Young’s modulus. The model reasonably reproduced the tension–compression–tension response and approximately represented the overall unloading response. Finally, the model was applied to the springback simulation of L-bending to examine the effects of the crystal plasticity model, particularly its ability to describe the tension–compression asymmetry and unloading behavior. The simulation results showed that improving the accuracy of the crystal plasticity model improved the springback prediction. For the sheet examined in this study, the reduction in the approximated unloading slope had the largest influence among the modeling factors considered.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic interpretable machine learning-based multi-scale modeling of nanomaterial reinforced concrete with carbon nanotubes and nano-silica for enhanced structural performance","authors":"Boskey V. Bahoria,&nbsp;Nilesh Mendhe,&nbsp;Shradhesh R. Marve,&nbsp;Prashant Hiwase,&nbsp;Lowlesh N. Yadav,&nbsp;Nilesh Shelke,&nbsp;Latika Pinjarkar,&nbsp;Rakesh Patel,&nbsp;P. Jagadesh,&nbsp;Aseel Smerat,&nbsp;Vikrant S. Vairagade","doi":"10.1007/s12289-026-02050-6","DOIUrl":"10.1007/s12289-026-02050-6","url":null,"abstract":"<div><p>Recent need for enhanced mechanical properties in cementitious composites poses a demand for high performance along with functional infrastructure, particularly hybrid concrete reinforced with carbon nanotubes (CNTs) and nano-SiO₂ particles. Such demands require improvements to be made in mechanical properties under modern requirements. This work develops a comprehensive multiscale hybrid model that is data-driven by incorporating stochasticity to grasp complex behavior in hybrid concrete materials under a wide array of loading conditions. As such, the model fuses Stochastic Kriging-based Hybrid Multiscale Model (SK-HMM) that encompass random field models of CNT and nano-SiO₂ dispersion coupled with finite element analysis, thereby making it Viable for the probabilistic predictions of the effective Young’s modulus and tensile as well as compressive strength accurately. Bayesian neural networks (BNNs) with Monte Carlo Dropout are exploited for uncertainty quantification so that the outputs are probabilistic with uncertainty bounds quantified. When it comes to finding solutions to problems and making judgments, it is an essential and essential component. As a further point of interest, the constitutive models of stress-strain behavior adhere to scientific laws, such as the conservation of momentum and energy in physics-informed neural networks (PINNs). Because of this, it is possible to generate fairly accurate predictions regarding stress and strain even when the behavior is not linear. At the end of the day, the nonlinear response surface for crack growth rate and compressive strength can be modeled using gaussian process regression (GPR) with heteroscedastic noise. This allows for input diversity. It is far simpler to make an educated prediction regarding the tensile strength, fracture toughness, and wear life of a material than it would be otherwise. The purpose of this study is to develop a robust framework for the enhancement of hybrid concrete composites by making careful use of randomization, physical laws, and uncertainty quantification respectively. It is possible that these composites will result in structures that are more durable and better building materials that are more resistant to damages.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale investigation of pore and crack formation during selective laser melting of pure tungsten: simulation and experimental validation","authors":"Meng Guo,&nbsp;Shilin Xia","doi":"10.1007/s12289-026-02048-0","DOIUrl":"10.1007/s12289-026-02048-0","url":null,"abstract":"<div><p>Selective laser melting (SLM) of pure tungsten remains challenging due to its intrinsic properties, including high melting point, high surface tension, and high dynamic viscosity. To reveal the physical phenomena involved in the SLM of pure tungsten, a transient mesoscale model was developed using the finite volume method (FVM). The temperature evolution and thermodynamic behavior within the molten pool were investigated. The simulation results demonstrated that both the peak temperature and the cooling rate increased with higher laser power. At a high laser power of 350 W, the peak temperature and cooling rate reached 5742 K and 4.66 × 10⁷ K/s, respectively. Correspondingly, a long liquid lifetime of 184 µs was obtained. At 350 W, the velocity vectors within the molten pool were significantly intensified, generating strong mass transfer. A regular molten pool with a large width of 62 µm was formed, which favored metallurgical bonding with adjacent scanning tracks. Laser power played a crucial role in determining the surface morphologies of SLM-processed tungsten parts. At a relatively low laser power of 200 W, the scanning track was discontinuous and contained numerous unmelted particles. Simultaneously, the corresponding SLM-processed parts exhibited large pores. However, when a high laser power of 350 W was applied, the top surface of the scanning track became continuous with regular liquid flow, and the corresponding part was nearly pore-free. Moreover, cracks were inevitable across all applied laser power levels, and its formation mechanism was revealed. Based on the simulation results, potential methods for reducing cracks were proposed.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}