Search Results (596)

In a steel continuous casting mold, argon bubbles injected through the submerged entry nozzle undergo transport, coalescence, and turbulent breakup, producing a polydisperse bubble swarm that affects flow stability and defect formation. In this study, an Euler–Lagrange model coupled with bubble collision coalescence and turbulence-induced breakup sub-models was established and validated using water model observations. Three daughter-bubble volume distribution models were compared in terms of bubble-cloud morphology, number-fraction distribution, and median-diameter evolution at different gas flow rates. For the median bubble diameter at different gas flow rates, the M-type model gives the lowest mean absolute error of 0.0349 mm. Large bubbles with diameters greater than 2.5 mm accounted for about 4% of the total number and were mainly concentrated near the SEN, whereas small bubbles with diameters of 1.0–1.5 mm accounted for about 60% and were dispersed throughout the upper recirculation region. Mechanism analysis further shows that bubble transport is drag-dominated in the high-velocity jet region, while buoyancy becomes more important in weaker flow regions; turbulent breakup is localized mainly in high-dissipation regions. Full article

Sulfur-containing medium-carbon microalloyed steel blooms are widely used for high-load automotive components, and reducing surface defects is important for improving product yield and lowering downstream processing costs. To address surface defects such as star cracks and microcracks in the continuous casting of these steel blooms, this study redesigned the mold flux on the basis of the steel’s solidification characteristics and crack susceptibility and carried out a twin-strand industrial comparative casting trial. Thermodynamic and thermophysical analyses indicated that the relatively high contents of S, Mn, and Ti/N in the steel promoted the precipitation of MnS and TiN–MnS complex inclusions along grain boundaries, severely weakening grain boundary cohesion. Meanwhile, the high specific heat capacity and low thermal conductivity further intensified thermal stress concentration in the solidifying shell, rendering the steel highly susceptible to cracking. Evaluation of the originally used mold flux (Flux A) revealed that its high melting temperature (1189 °C), long melting time (106 s), high break temperature (1170 °C), and poor crystallization behavior resulted in an excessively thin liquid slag layer (<5 mm) within the mold, making it difficult to provide adequate lubrication and stable heat transfer; these were key external factors inducing surface defects. Accordingly, the optimized mold flux (Flux B) was designed and prepared by increasing the basicity from 0.95 to 1.1, raising the Al₂O₃ content from 9.48% to 11.16%, increasing the F content from 4.93% to 5.58%, and reducing the carbon content from 13.85% to 6.97%. The rheological and crystallization properties of the flux were optimized in a coordinated manner, allowing uniform heat transfer through the crystalline slag layer while maintaining adequate lubrication. Industrial comparative trials demonstrated that Flux B stabilized the liquid slag layer at 8–10 mm, increased slag consumption to 0.56 kg/t, and significantly reduced surface defects such as star cracks and microcracks on blooms. The ultrasonic testing acceptance rate for rolled products increased to 98.6%, thereby meeting stringent quality requirements for the continuous casting of sulfur-containing, medium-carbon, microalloyed steel blooms. Full article

High-pressure casting technology is continuously evolving to achieve improved product quality. In the casting process using ADC12 alloy, defects such as porosity, shrinkage, cold shut, and others are frequently observed and may arise due to the complex interplay of heat and mass transfer, thermodynamic principles, and fluid flow rates. These types of defects can be predicted through computational simulation. By analyzing the simulation results of a given component, engineers can utilize them as a reference for establishing machine parameters. This approach enables the early identification of potential defects, allowing for the optimization of the relevant parameters. The integration of casting machines with process simulation thus serves as a complementary strategy for producing high-quality castings that meet customer requirements. Full article

In the manufacturing process of high-grade non-oriented electrical steel, cast billets are subjected to hot rolling and normalizing treatments. These processes are implemented to optimize the microstructure and texture of steel sheets during production, mitigate corrugated defects, and enhance the magnetic properties of the final finished sheets. In this study, two types of high-strength non-oriented silicon steel test specimens were prepared via the incorporation of the trace alloying element Sn, namely one without Sn addition and the other with 0.045 wt% Sn. The test specimens were first hot-rolled to a thickness of 2.0 mm, followed by normalization treatment in the laboratory to simulate the continuous normalizing process employed by a domestic steel mill. The effects of Sn on the normalized microstructure, texture, and precipitates of non-oriented silicon steel tailored for new energy vehicles were investigated. The findings reveal that the alloying element Sn can increase the thickness of the recrystallized layer on the surface of hot-rolled sheets and refine the grain size of non-oriented silicon steel. After continuous normalizing treatment, a comparison between the two test specimens shows that as the normalizing temperature rises, the reduction in average grain size of the 0.045 wt% Sn specimen relative to the Sn-free specimen increases from 1.4% to 15.96%. Additionally, the incorporation of Sn reduces the fraction of the {111} texture component (detrimental to magnetic properties) while increasing the fraction of the {100} texture component (beneficial to magnetic properties) in the non-oriented silicon steel. Precipitates exhibited significant coarsening and a reduction in number with increasing temperature, while the addition of Sn exerted a certain inhibitory effect on precipitate growth. Furthermore, the 0.045 wt% Sn-containing test specimen achieved an optimal balance between magnetic and mechanical properties when subjected to normalization at 980 °C and annealing at 920 °C. Under these processing conditions, the magnetic induction B₅₀ reached 1.733 T, the iron loss P_1.5/50 was 2.01 W/kg, the yield strength was 410 MPa, and the tensile strength was 529 MPa. Full article

Automated defect detection in industrial casting processes is important for improving product quality while reducing the cost of manual inspection. In this work, two deep convolutional neural network (CNN) architectures, InceptionV3 and InceptionResNetV2, are evaluated for the binary classification of defects in submersible pump impellers. A genetic algorithm (GA) is used to optimize key hyperparameters, including dropout rate, learning rate, and dense layer configuration, while model complexity is assessed through Pareto-based analysis. Single-run optimization results show that InceptionV3 achieves high classification accuracy (99.0%) with lower model complexity than InceptionResNetV2 (98.75%). Repeated experiments using different random seeds demonstrate relatively stable performance across runs, with InceptionV3 achieving an accuracy of 0.9913 ± 0.003 and InceptionResNetV2 achieving 0.9860 ± 0.0076. Additional experiments were conducted using random-search baselines and classification-head ablation studies (Flatten vs. Global Average Pooling). These experiments showed that optimization strategy and architectural design choices influence both predictive performance and computational complexity. The environmental impact of the training process is evaluated using CodeCarbon, with energy consumption ranging from 0.083 to 0.098 kWh and carbon emissions ranging from 2.008 to 2.401 g CO₂eq for InceptionV3 and InceptionResNetV2, respectively. Overall, the results suggest that the most effective configuration depends on the evaluated architecture and experimental setting, highlighting the importance of balancing accuracy, model complexity, and computational efficiency in industrial defect detection systems. Full article

Process parameter control is critical for reducing casting defects in ZTA2 alloy pump body investment casting. However, there exists a complex nonlinear relationship between parameters such as pouring temperature, pouring time, and shell preheating temperature, and defects including total defect volume, shrinkage porosity, and shrinkage cavities, posing significant challenges to accurate prediction and optimization. To address this issue, this study proposes an integrated strategy for defect prediction and process optimization that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Backpropagation Neural Network (BP neural network). First, an L₂₅(5³) orthogonal experiment was designed, and a dataset consisting of 25 orthogonal samples and 97 random samples was constructed by combining ProCAST simulations, covering the entire parameter domain of pouring temperature, pouring time, and shell preheating temperature. Subsequently, the PSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and Bayesian regularization and 5-fold cross-validation were introduced to build a high-precision defect prediction model. The SHapley Additive exPlanations (SHAP) analysis was employed to clarify parameter sensitivity and interaction mechanisms, and the NSGA-II was combined to realize multi-objective process optimization. The results show that: compared with the traditional BP neural network, the optimized PSO-BP model improves the coefficient of determination (R²) of the test set for total defect volume prediction by 20.82% and reduces the root mean square error (RMSE) by 33.34%; for shrinkage porosity volume prediction, the R² is increased by 7.93% and the RMSE is reduced by 22.71%, which effectively solves the problems of local optimization and weak generalization ability. Pouring time is the most sensitive parameter affecting defects, and the coupling effect between pouring temperature and pouring time is the strongest. Considering actual production conditions, the superior process parameters are determined as follows: pouring temperature of 1800 °C, pouring time of 4 s, and shell preheating temperature of 475 °C. Compared with the pre-optimization results, this parameter combination reduces the total defect volume by 38.92% and the shrinkage porosity volume by 51.62%. The intelligent optimization framework constructed in this study provides reliable technical support for the accurate control of defects in ZTA2 titanium alloy pump body investment casting, and has important engineering value for improving the quality of castings in industrial production and reducing costs. Full article

Manufacturing high-performance turbomachinery blades remains one of the most demanding challenges in aerospace and energy engineering, requiring tight control over microstructure, geometry, and cooling architectures. Despite rapid progress in casting, machining, and additive manufacturing, the field lacks a structured classification that links process capabilities with blade functional requirements and future design trends. This review addresses that gap by introducing a new classification scheme for turbomachinery blade manufacturing technologies, organized into three complementary domains: (i) foundational fabrication routes (casting, forging, precision machining); (ii) advanced and hybrid processes (powder-bed fusion, directed-energy deposition, additive–subtractive systems, laser repair); and (iii) digital and intelligent manufacturing enablers (in situ monitoring, AI-driven process control, digital twins, and automated inspection). Within each class, the review maps process parameters to resulting structural performance, defect modes, cost drivers, and certification challenges. Special emphasis is placed on the manufacturing implications of emerging blade architectures, such as intricate internal cooling channels, gradient materials, and bio-inspired aerodynamic profiles. By consolidating disparate techniques into a structured taxonomy, this paper clarifies current limitations, identifies cross-technology synergies, and outlines priority research directions for achieving next-generation turbomachinery blade manufacturing. Full article

The article presents the calculation of temperature fields for a casting (a cylinder 20 mm in diameter) made of Al–5%wt.Cu alloy, poured into sand (sand–clay) and metal (steel) molds at a temperature of 1123 K (with a metal mold temperature of 523 K). Many existing analytical approaches do not explicitly account for key features such as the time-dependent temperature evolution at the casting surface and center, as well as the variable temperature gradient within the casting. In this paper, the parameters calculated for the sand mold include the surface temperature change over time, as do the dynamics of the solidification front progression, and ultimately, the overall thermal field of the casting. For the metal mold, the process first determines the change in the center temperature over time, followed by the surface temperature dynamics, and finally, the complete thermal field of the casting. Particular attention is paid to determining the position of the mushy zone, namely the zero fluidity and feeding temperatures (the point at which the liquid phase loses mobility upon cooling). These temperatures are critical for casting structure formation and the initiation of shrinkage defects. To perform the calculations, the authors developed original mathematical models and provided solutions to the resulting differential equations. The study demonstrates the differences between the thermal fields in sand and metal molds: the maximum temperature difference is 195 K in the sand mold, compared to 90 K in the metal mold. Therefore, the solidification conditions for this casting in the metal mold are more favorable. The metal mold provides more favorable thermal conditions and a lower analytically predicted tendency toward shrinkage defects, but it does not guarantee their complete absence. Full article

Accurate thermal analysis of steel solidification and heat transfer in the continuous casting mold is essential for understanding and controlling solidification, shell thickness uniformity, interfacial gap phenomena, and defects such as cracks and breakouts. This study investigates heat transfer in a funnel mold slab caster using the in-house thermal model, Con1D. A new methodology is introduced to predict the slag layer roughness, and its effect on interface resistance. To account for the multidimensional thermal behavior near water channels and thermocouples, finite-element models are developed in Abaqus to calibrate Con1D to match three-dimensional calculations of mold heat transfer. After calibration to match plant measurements for one set of casting conditions, Con1D predictions are validated with plant measurements at different casting speeds and mold plate thicknesses. Key outputs analyzed include the heat flux profile, mold and shell temperatures, shell thickness, shell shrinkage, and interfacial parameters such as slag layer thickness. Increasing casting speed causes higher heat flux, higher shell surface and mold temperatures, and decreased shell and slag layer thicknesses. Decreasing mold plate thickness increases heat flux slightly due to reduced thermal resistance of both the mold and interfacial gap. The modeling approach presented here is a powerful methodology to gain quantitative fundamental understanding of mold heat transfer in continuous casting, especially including phenomena in the interfacial gap. Full article

In this study, a Ti–48 at.% Al–2 at.% Nb–2 at.% Cr alloy was cast by bottom-pouring cold crucible induction melting (CCIM), and the shrinkage depressions formed in ingots during solidification were investigated. Ingots with different heights were produced, and shrinkage depression height and yield were evaluated based on longitudinal cross-sectional observations. The normalized ingot height ranged from 4 to 25, and the shrinkage depression height increased from 20 mm to 105 mm with increasing ingot height. The yield ranged from 77% to 97% and did not increase monotonically, exhibiting noticeable scatter even among ingots with similar heights. The casting rate ranged from 0.025 kg/s to 0.18 kg/s, and the shrinkage depression height increased with increasing casting rate, whereas no clear correlation was observed between the yield and the casting rate. When the nozzle inner diameter ranged from 2 mm to 5 mm, both the shrinkage depression height and the yield increased, accompanied by scatter. The Reynolds number was evaluated as a parameter representing the average flow condition of the pouring stream; however, shrinkage depression formation could not be uniquely explained by the Reynolds number alone, indicating that melt feeding behavior and heat extraction conditions must also be considered. Full article

The stability of mold level fluctuations (MLFs) is crucial for product quality and process efficiency in continuous casting. Abnormal mold level fluctuations, which are typically associated with multiple factors including stopper rod opening, casting speed, and mold width, are known to lead to slab quality defects. In this paper, an Informer-based prediction framework is proposed for the early detection of abnormal MLF. A threshold-based labeling method is developed to quantify the future likelihood and severity of anomalies across different time horizons. Considering the importance of frequency-domain features in mold level prediction, power spectral density (PSD) features are incorporated and smoothed using the exponential moving average (EMA) to enhance predictive performance. Through the integration of temporal and processed spectral features, early indicators of abnormality can be captured, and proactive warnings can be issued. The proposed architecture is validated using approximately 32.5 million data points from a real-world continuous casting process. This approach provides a robust and data-driven solution for predicting and diagnosing abnormal MLF events in continuous casting. Experimental results show that the mean ROC-AUC and PR-AUC reach 0.821 and 0.418, respectively. Full article

The growing demand for improved operational efficiency of cast components used in various types of equipment necessitates the development of advanced casting technologies. One of the key challenges currently faced by the foundry industry is enhancing the surface quality of castings and reducing rejection rates caused by casting defects. These requirements can be effectively met by castings produced using shell mold casting technology. Sand–resin mixtures are used for their production. Foundry molds made from such mixtures make it possible to obtain high-quality castings from various alloys. However, their widespread industrial application is limited by the relatively high cost of the binder, namely pulverbakelite. The influence of pulverbakelite content on the properties of sand–resin mixtures during shell mold formation under variational static pressure was investigated. It was established that pressure variation during the molding process increases mold strength and improves surface quality while maintaining the required level of gas permeability. The optimal binder content was determined to be 4–6%, which makes it possible to reduce binder consumption without deteriorating the mechanical and technological characteristics of the mold. With respect to novelty, it should be noted that previous studies addressed individual aspects of variable pressure application. In the present article: a wider range of pulverized bakelite content (3–9%) was investigated; the relationship between binder content, strength, and gas permeability was established; the optimal binder content range (4–6%) was determined; and microstructural analysis was extended to include composition and pressure regimes. Thus, the present work significantly extends previous findings and provides a more comprehensive investigation. Full article

The industrial X-ray inspection of turbine blade castings requires reliable and auditable decision support, yet defect indications are subtle, and data availability is limited. This study quantitatively assesses the diagnostic potential of handcrafted image descriptors by evaluating class separability in feature space, independently of any trained classifier. The dataset comprises 1600 16-bit DICOM radiograms of 200 blades (eight views per blade), including 156 defective images with 207 localized defects. Standardized 32 × 32 ROI patches were sampled randomly in the vicinity of indications and from defect-free regions to reduce sample correlation and to emulate localization uncertainty. Feature vectors were extracted using five descriptor families—first-order statistics, GLCM/Haralick, FFT and wavelet (DWT) features, Gabor filters, and LBP—and the standardized z-score. Separability was ranked using complementary distribution-based and distance-based metrics grouped into three sets, and the results were min–max-normalized to enable TOP-5 comparisons. Spectral descriptors, particularly DWT wavelets and FFT combined with DWT, consistently achieved the highest scores in distributional metrics, supporting a lightweight screening profile. In contrast, richer combinations dominated multidimensional geometric metrics, indicating benefits from multi-perspective representations for offline analysis. The proposed metric-driven framework provides an interpretable basis for representation selection prior to classifier development under industrial constraints. Full article

Surface defects in high-temperature continuous cast billets are critical factors affecting the quality of steel products. Owing to high-temperature radiation, heavy dust contamination, varying billet specifications, and background interference from oxide scales and water stains, existing online surface defect detection technologies for high-temperature continuous cast billets still suffer from limitations including high false-positive rates, inefficient identification of pseudo-defects, and the inability to simultaneously detect three-dimensional (3D) depth information alongside two-dimensional (2D) features. To solve these problems, this paper proposes a multi-dimensional online detection technology for surface defects in high-temperature continuous cast billets based on multi-information fusion. A four-channel multispectral image sensor and a corresponding three-light-source imaging system were developed. Furthermore, a defect sample augmentation method, a deep learning-based 2D recognition method, and a photometric stereo-based 3D reconstruction method were designed to mitigate problems of low detection accuracy and poor robustness caused by sample imbalance among different defect types. Finally, industrial applications were conducted on large-section continuous cast billets, beam blanks, and billets during the grinding process. According to the surface defect detection requirements of different continuous cast billets, multispectral multi-information fusion and traditional 2D defect imaging methods were adopted respectively. The results demonstrate high-precision online detection of surface defects in continuous cast billets, with favorable practical application effects. Full article

Seamless Copper Tube Manufacturing (SCTM) is a multi-stage manufacturing chain, typically comprising billet casting, hot extrusion, cold drawing or pilgering, intermediate annealing, and finishing operations. Despite the fact that quality control (QC) practices are implemented at individual stages, many product deviations originated from cumulative thermomechanical and metallurgical interactions across multiple processes. Thus, although the current stage-wise QC schema ensures compliance with quality standards, it’s questionable whether it can identify root causes or implement proactive QC at the production level. This study presents a narrative review of QC approaches in SCTM, examining the production chain across key quality domains, including billet integrity, extrusion tooling condition, dimensional control, surface and internal defect detection, annealing atmosphere monitoring, and inner-surface cleanliness. The industrial practices are critically compared with research approaches in numerical modelling, advanced sensing technologies, and data-driven monitoring methods. Results confirmed that dimensional instability, defect formation, surface contamination, and microstructural variation in the tube are influenced by interactions among factors such as billet quality, thermomechanical conditions during extrusion and drawing, annealing conditions, tooling conditions, lubrication regimes, and handling between processing steps. Their analysis indicated that the main limitation of current QC frameworks is not the lack of monitoring or modelling technologies but the limited integration of process data across the manufacturing chain. Full article