The present work exhibits the effects of Sn addition on the magnetic properties and microstructure of FeCoNi(CuAl)_0.8Sn_x (0 ≤ x ≤ 0.10) high-entropy alloys (HEAs). The results show all the samples consist of a mixed structure of face-centered-cubic (FCC) phase and body-centered-cubic (BCC) phase. The addition of Sn promotes the formation of BCC phase, and it also affects the shape of Cu-rich nano-precipitates in BCC matrix. It also shows that the Curie temperatures (T_c) of the FCC phase and the saturation magnetization (M_s) of the FeCoNi(CuAl)_0.8Sn_x (0 ≤ x ≤ 0.10) HEAs increase greatly while the remanence (B_r) decreases after the addition of Sn into FeCoNi(CuAl)_0.8 HEA. The thermomagnetic curves indicate that the phases of the FeCoNi(CuAl)_0.8Sn_x (0 ≤ x ≤ 0.10) HEAs will transform from FCC with low T_c to BCC phase with high T_c at temperature of 600–700 K. This work provides a new idea for FeCoNi(CuAl)_0.8Sn_x (0 ≤ x ≤ 0.10) HEAs for their potential application as soft magnets to be used at high temperatures.

High-entropy alloys (HEAs) with soft magnetic properties are one of the new candidate soft magnetic materials which are usually used under an alternating current (AC) magnetic field. In this work, the AC soft magnetic properties are investigated for FeCoNi_xCuAl (1.0 ≤ x ≤ 1.75) HEAs. The X-ray diffraction (XRD) and scanning electron microscope (SEM) show that the alloy consists of two phases, namely a face-centred cubic (FCC) phase and a body-centred cubic (BCC) phase. With increasing Ni content, the FCC phase content increased. Further research shows that the AC soft magnetic properties of these alloys are closely related to their phase constitution. Increasing the FCC phase content contributes to a decrease in the values of AC remanence (AC B_r), AC coercivity (AC H_c) and AC total loss (P_s), while it is harmful to the AC maximum magnetic flux density (AC B_m). P_s can be divided into two parts: AC hysteresis loss (P_h) and eddy current loss (P_e). With increasing frequency f, the ratio of P_h/P_s decreases for all samples. When f ≤ 150 Hz, P_h/P_s > 70%, which means that P_h mainly contributes to P_s. When f ≥ 800 Hz, P_h/P_s < 40% (except for the x = 1.0 sample), which means that P_e mainly contributes to P_s. At the same frequency, the ratio of P_h/P_s decreases gradually with increasing FCC phase content. The values of P_e and P_h are mainly related to the electrical resistivity (ρ) and the AC H_c, respectively. This provides a direction to reduce P_s.

Incremental sheet metal forming characterized as increased flexibility and local plastic deformation is well suitable for low-production-run manufacturing and a new sample trial production of complex shapes. Thickness thinning is still an obstacle to the application of incremental forming. In this study, a novel mathematical algorithm based on a non-uniform rational B-spline (NURBS) surface was proposed and implemented which focuses on predicting and calculating the final thickness for arbitrary parts in incremental forming. In order to evaluate the validity of the proposed model, the finite element simulation and forming experiments of three kinds of parts, such as truncated cones, truncated pyramids and ellipsoid parts, were conducted. The thickness of theoretical prediction was compared with that of finite element simulation and experiment, and good agreements were obtained. The results show that the proposed model and the method are effective and robust for predicting the thickness of the formed parts in incremental sheet metal forming.

In recent years, steel surface defect detection has emerged as a significant area of focus within intelligent manufacturing research. Existing approaches often exhibit insufficient accuracy and limited generalization capability, constraining their practical implementation in industrial environments. To overcome these shortcomings, this study presents IMTS-YOLO, an enhanced detection model based on the YOLOv11n architecture, incorporating several technical innovations designed to improve detection performance. The proposed framework introduces four key enhancements. First, an Intelligent Guidance Mechanism (IGM) refines the feature extraction process to address semantic ambiguity and enhance cross-scenario adaptability, particularly for detecting complex defect patterns. Second, a multi-scale convolution module (MulBk) captures and integrates defect features across varying receptive fields, thereby improving the characterization of intricate surface textures. Third, a triple-head adaptive feature fusion (TASFF) structure enables more effective detection of irregularly shaped defects while maintaining computational efficiency. Finally, a specialized bounding box regression loss function (Shape-IoU) optimizes localization precision and training stability. The model achieved a 5.0% improvement in mAP50 and a 3.2% improvement in mAP50-95 on the NEU-DET dataset, while also achieving a 4.4% improvement in mAP50 and a 3.1% improvement in mAP50-95 in the cross-dataset GC10-DET validation. These results confirm the model’s practical value for real-time industrial defect inspection applications.

The phase evolution, magnetic properties and microstructure of rod-shaped permanent magnets prepared by annealing the amorphous precursor Fe_70−xNd₇B₂₁Zr₂Nb_x (x = 0–3.0) were systematically studied. X-ray diffraction analysis, magnetometer, microstructure and δM-plots studies show that the good magnetic properties of the magnet are attributed to the uniform microstructure composed of exchange-coupled α-Fe and Nd₂Fe₁₄B phases. Nb addition to Fe_67.5Nd₇B₂₁Zr₂Nb_2.5 alloy led to an increase in the volume fraction of the soft magnetic phase, reinforced exchange coupling and improved magnetic properties. The magnetic properties of the optimized annealed Fe_67.5Nd₇B₂₁Zr₂Nb_2.5 rod are: coercivity (H_ci) = 513.92 kA/m, remanence (B_r) = 0.58 T, squareness (H_k/H_ci) = 0.24 and magnetic energy product ((BH)_max) = 37.59 kJ/m³.