Minki Kim

Accurate prediction of startup screw speeds in rubber weatherstrip co-extrusion is challenging due to batch-dependent process variation, material variability, and target-specific geometric constraints. This study proposes a hybrid fusion framework that integrates an autoregressive integrated moving average with exogenous variables (ARIMAX) model and a physics-guided feature-based artificial neural network (PG-ANN) to predict startup screw speeds for the Ø120, Ø90, and Ø70 extruders. The temporal model captures batch-to-batch process variations, while the PG-ANN uses input features constructed from material rheological properties and cross-sectional geometry. A process-change-driven branch-selection mechanism determines which model output is used for each production batch. The framework was evaluated using industrial co-extrusion data collected from a mass-production environment. In the hold-out test, the best-performing model differed across targets: ARIMAX showed the highest R² and lowest root mean square error (RMSE) for Ø120, the hybrid model achieved the best performance across all evaluated metrics for Ø90, and the PG-ANN performed best for Ø70. Model transferability was further assessed by applying the A1-trained framework to the A2 product condition without refitting; the Ø120 target showed poor transfer performance, consistent with the weaker batch-to-batch correlation observed in A2. The limited number of independent production batches and changes in batch-to-batch relationships across products constrain model generalization. The proposed framework provides a rule-based mechanism for selecting between complementary prediction branches using measurable process changes.

Additive manufacturing (AM) of titanium alloys enables the production of complex, high-performance components, but the steep thermal gradients and rapid solidification involved make it challenging to control crystallographic texture and phase evolution. This review synthesizes the current understanding of how these thermal conditions influence grain morphology, texture intensity, and solid-state transformations in key alloys such as Ti-6Al-4V (Ti64), Ti-6Al-2Sn-4Zr-2Mo (Ti6242), Ti-5Al-5Mo-5V-3Cr (Ti5553), and metastable β-Ti systems processed by powder bed fusion-based processes (PBF) such as laser powder bed fusion (LPBF) and electron beam powder bed fusion (EBPBF/EBM). Emphasis is placed on mechanisms governing epitaxial columnar β-grain growth, α′ martensite formation, and the development of heterogeneous α/β distributions. The impact of processing variables on texture development and transformation kinetics is critically examined, alongside phase fractions. Across studies, AM-induced textures are consistently linked to mechanical anisotropy, with performance strongly dependent on build direction and alloy chemistry. Post-processing strategies, including tailored heat treatments and hot isostatic pressing (HIP), show clear potential to modify grain structure, reduce texture intensity, and stabilize desirable phase balances in titanium alloys. These insights highlight the emerging ability to deliberately engineer microstructures for reliable, application-specific properties in powder-based AM titanium alloys.

This study investigated defect formation and strain distribution in high-Mg-content Al-Mg alloys during normal rolling and cross-rolling processes. The finite element analysis (FEA) revealed the presence of wave defects and strain localization-induced zipper cracks in normal cold rolling, which were confirmed by the experimental results. The concentration of shear strain played a significant role in crack formation and propagation. However, the influence of wave defects was minimal in the cross-rolling process, which exhibited a relatively uniform strain distribution. Nonetheless, strain concentration at the edge and center regions led to the formation of zipper cracks and edge cracks, with more pronounced propagation observed in the experiments compared to FEA predictions. Furthermore, texture evolution was found to be a crucial factor affecting crack propagation, particularly with the development of the Goss texture component, which was observed via electron backscattered diffraction analysis at bending points. The Goss texture hindered crack propagation, while the Brass texture allowed cracks to pass through. This phenomenon was consistent with both FEA and experimental observations. To mitigate edge crack formation and propagation, potential strategies involve promoting the formation of the Goss texture at the edge through alloy and process conditions, as well as implementing intermediate annealing to alleviate stress accumulation. These measures can enhance the overall quality and reliability of Al-Mg alloys during cross-rolling processes. In summary, understanding the mechanisms of defect formation and strain distribution in Al-Mg alloys during rolling processes is crucial for optimizing their mechanical properties. The findings of this study provide insights into the challenges associated with wave defects, strain localization, and crack propagation. Future research and optimization efforts should focus on implementing strategies to minimize defects and improve the overall quality of Al-Mg alloys in industrial applications.

In conventional wear simulation, the geometry must be updated for succeeding iterations to predict the accumulated wear. However, repeating this procedure up to the desired iteration is rather time consuming. Thus, a wear simulation process capable of reasonable quantitative wear prediction in reduced computational time is needed. This study aimed to develop an efficient wear simulation method to predict quantitative wear reasonably in reduced computational time without updating the geometry for succeeding iterations. The wear resistance of a stamping tool was quantitatively evaluated for different punch shapes (R3.0 and R5.5) and coating conditions (physical vapor deposition of CrN and AlTiCrN coatings) by using a progressive die set. To capture the nonlinear wear behavior with respect to strokes, a nonlinear equation from a modified form of Archard’s wear model was proposed. By utilizing the scale factor representing the changes in wear properties with respect to wear depth as input, the simulation can predict the behavior of rapidly increasing wear depth with respect to strokes after failure initiation. Furthermore, the proposed simulation method is efficient in terms of computational time because it does not need to perform geometry updates.

This paper investigates the reduction of springback by an electrically single-pulsed current for an ultra-high-strength martensitic steel sheet, MART1470 1.2t. In order to evaluate the springback reduction by the electric current, V-bending tests were performed with various parameter-sets (current density and pulse duration). The amount of springback reduction was then calculated from the measured bent-angle of tested specimens. Experimental results show the springback is reduced with the increase in the current density, the pulse duration, and the electric energy density. In order to clarify thermal and athermal portions in the effect of electric current on the springback reduction, two ratios of force and isothermal flow stress were calculated based on bending theory. From the comparison of the ratios, it is noted that the athermal portion mainly contributes to the force relaxation, so the springback amount decreases. The athermal portion significantly increases as the electric energy density increases. Microstructures and micro-Vickers hardness were observed to confirm the applicability of the single-pulsed current to forming processes in practice. The springback reduction can be achieved up to 37.5% without severe changes in material properties when the electric energy density increases up to 281.3 mJ/mm³. Achievable reduction is 85.4% for the electric energy density of 500 mJ/mm³, but properties remarkably change.