To duly and correctly deliver parcels, both the capacity and the delivery route of a delivery vehicle need to be considered. Thus, the delivery process of a delivery vehicle can be characterized as a capacitated vehicle routing problem with three-dimensional loading constraints (3L-CVRP), which is an NP-hard problem. To solve the problem, a mathematical model is established in this paper to minimize the total delivery distance and maximize the loading rate, simultaneously. Additionally, a hybrid algorithm that combines a three-dimensional (3D) packing algorithm based on the residual space optimized (RSO) strategy and an improved genetic algorithm (IGA) is proposed. Initially, the proposed hybrid algorithm employs a modified Clarke–Wright savings algorithm to generate a feasible set of route solutions. Furthermore, building upon the traditional genetic algorithm, an elite retention strategy is introduced, and an enhanced order crossover method is utilized to improve the stability of the hybrid algorithm and its global search capability for optimal solutions. Finally, during each iteration of the algorithm, the RSO algorithm is integrated to verify the feasibility of 3D packing scheme. Two comparative experiments are conducted on 22 modified benchmark instances and actual logistics data of a university against two other algorithms, demonstrating that the proposed RSO-IGA algorithm achieves superior solutions in delivery efficiency.

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with “one-line knit to shape” capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry.

TRIP duplex stainless steels, characterized by high strength and high plasticity, can achieve light-weighting and contribute to reducing fuel consumption and emissions. To further promote the development and application of lightweight metastable duplex stainless steels, the martensitic transformation and the microscopic mechanism of Mn-N alloyed TRIP duplex stainless steel under cyclic loading were investigated. An in situ measurement platform for martensitic transformation under cyclic loading was constructed using an INSTRON 8801 series servo-hydraulic testing machine (Shanghai Instron Test Equipment Trading Co., Ltd., Shanghai, China) and an FMP30 ferrite measuring instrument (Nantong Fischer Testing Instrument Co., Ltd., Nantong, China). The volume fraction of martensitic transformation under symmetrical cyclic loading for different cycles, with strain amplitudes of 0.5%, 0.7%, 0.9%, 1.1%, and 1.3%, was measured. The transformation law of martensite under cyclic loading was analyzed, and a kinetics model for martensitic transformation under cyclic loading was established. Furthermore, the martensitic transformation law, the influence of austenite grain orientation on martensitic transformation, and the microscopic mechanism of martensitic transformation under cyclic loading were analyzed by means of electron back-scattering diffraction (EBSD) and transmission electron microscopy (TEM).