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Porosity defects in high-pressure die-casting (HPDC) are a significant quality concern and are challenging to diagnose due to noisy raw data, nonlinear relationships, and data imbalance. Augmenting rare faults becomes essential for machine learning (ML) results, but it can be challenging, potentially leading to the generation of unrealistic or irrelevant fault signatures. This paper considers data augmentation in an explainable AI framework for robust porosity diagnosis. Specifically, we utilize an autoencoder with a Wasserstein generative adversarial network (WGAN-GP) to augment the latent space for rare-fault data and employ the Fréchet Inception Distance (FID) metric to assess the quality of the synthetic data. Demonstrated through a real-world case study. We applied a traditional XGBoost classifier to isolate defects and achieved a 95.90

PurposeThe purpose of this study is to explore novel rotor shapes of variable leakage flux interior permanent magnet synchronous motors (VLF-IPMSMs) via parameter and topology optimization (PTO) to enhance the variable flux characteristics, which are crucial for achieving high torque at low speeds and high efficiency at high speeds in electric vehicle drives.Design/methodology/approachThe PTO method combines the normalized Gaussian network ON/OFF method with covariance matrix adaptation evolution strategy, using an objective function that maximizes flux linkage variation and suppresses harmonic components under torque constraints, which are evaluated using the finite element method.FindingsFor an arc-shaped magnet model identical to the reference model, optimization limited to the end of the magnet region yielded a rotor shape that increased the flux linkage variation by 32.7% and reduced the harmonic index of the radial air gap flux density by 75.2% compared to those of the reference while maintaining the maximum torque. Following the shape sensitivity analysis, low contribution regions were retained in the optimal shape. Upon optimization of the magnet shape parameters, the number of high order harmonic components increased, suggesting convergence to local optima.Originality/valueThis study demonstrates the application of PTO to VLF-IPMSMs using a newly defined objective function. The results demonstrate the potential of the PTO to identify rotor shapes with enhanced flux variations while clarifying the challenges of low sensitivity regions and local optima.

To accurately predict the shape of an automobile product, such as a crankshaft, produced by hot die forging, a preliminary simulation of the forging process indicated the significant impacts of local and varying friction coefficients between the complex-shaped die and the material. This study identified the friction coefficients through ring compression and tapered plug penetration tests, focusing on regions with high pressure or large contact areas. The results revealed variations in the friction coefficients across different regions. Consequently, the study suggests implementing locally appropriate friction coefficients on specific die surfaces exhibiting conditions akin to those observed in the friction tests. Specifically, a Coulomb’s friction coefficient of 0.14 was assigned to the product shape region of the crankshaft die. Additionally, a friction model transitioning from a Coulomb’s friction coefficient of 0.5 to a shear friction coefficient of 0.6 was applied in the flash region with significant sliding distances. By incorporating these tailored friction conditions into the simulation of hot die forging for crankshaft manufacturing, the study achieves more accurate material flow, die filling, and underfill replication.

An improvement in the performance of gears in the drive train is required due to the recent shift to electric vehicles. In this study, directed energy deposition (DED), a type of additive manufacturing, was utilized as a coating technology to get higher-performance gears. However, the mechanical properties of coating layers and heat-affected zone (HAZ) depend on the thermal histories. Therefore, a liquid cooling system consisting of a heatsink and a chiller which can adjust water temperature and flow rate was developed to improve the hardness of gear teeth by coating with DED. The performance of the liquid cooling system was evaluated by the temperature transition during the coating process and by measuring the hardness of the coating layer. The combination of the developed liquid cooling system and thermal conduction grease reduced the maximum temperature and exhibited a higher cooling rate during the process. As a result, the hardness of the gear teeth was successfully increased.

This study examines the buckling behavior of a non-pneumatic tire (NPT) with a linked zigzag structure under longitudinal loading. Finite element (FE) simulations were performed to clarify the instability mechanism and evaluate the structural response under combined compressive and bending loads. The numerical results confirm that the theoretical buckling formula for a circular arch-shaped plate agrees well with the ultimate compressive strength obtained by FE analysis under pure compression. In the NPT, buckling occurs along the interaction curve for combined axial force and bending moment, indicating that instability is governed by their coupled effect rather than by a single loading mode. Buckling is found to occur primarily in the circumferential intermediate ring, which is subjected simultaneously to compression and bending. The magnitude of these internal forces is largely controlled by the spoke inclination angle. Parametric analyses further show that increasing the thickness of the intermediate ring or the spokes effectively enhances the buckling load while maintaining the structural advantages of the airless tire. The proposed evaluation formula enables practical prediction of buckling onset based on the critical compressive load of the intermediate ring, providing a rational design criterion for linked zigzag NPT structures.