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Thin-Walled Parts (TWP) are widely used in aerospace, whose service performance is significantly affected by the machining accuracy. Considering the poor stiffness of TWP, multi-pass machining is used to machine TWP for better surface accuracy. However, it is difficult to accurately predict the final machining accuracy due to the surface topography error’s propagation and accumulation in multi-pass machining. Therefore, this paper proposes a multi-pass machining accuracy prediction method for TWP based on dynamic factors (cutting force and stiffness). Firstly, a flexible cutting force prediction model, which considers the axial errors determined by the initial surface topography and part deflection, is proposed. Secondly, a Position-Pass-Dependent Stiffness (PPDS) model is established considering position-dependent of stiffness and multi-pass machining material removal. Finally, combining the two models above, a multi-pass machining accuracy prediction method based on Genetic Algorithm - Back Propagation (GA-BP) neural network is proposed. The experiments under various conditions have been carried out to validate the proposed method. The machining accuracy (flatness as an example) is as high as 90.8% using the method in this paper, while it is only 73.9% when the accumulative error is neglected. The proposed method can significantly improve the performance of machining accuracy prediction by analyzing the error propagation mechanism and the effect of dynamic factors between multi-pass machining. Furthermore, this also provides a theoretical basis for process parameters optimization and machining accuracy improvement in TWP machining.

As an important cash crop, soybeans are often used to squeeze oil and as high-quality livestock feed, providing residents with high-quality vegetable oil and meat supplies, and play an important role in improving the health of the national diet. At present, the yield of soybeans in China is low, and many demand loopholes rely on high-quality soybeans imported from abroad to fill, so safe storage is particularly important. Based on this, this paper excavates and analyzes the problems existing in the traditional soybean storage method, and takes targeted measures according to the problems exposed by the traditional soybean storage, and constructs a real-time monitoring system for the soybean storage environment in the warehouse by using a variety of new storage technology means to improve the safe storage capacity and national living standards of soybeans in China.

As a large agricultural country, ensuring the steady development of agriculture is the most important task concerning the national economy and people's livelihood. In the context of rural revitalization, achieving rural prosperity is the primary goal. Agricultural product logistics not only affects the development of rural economy, but also plays a vital role in the development of agriculture. Therefore, this paper takes Guangxi as an example, studies and analyzes the current situation of agricultural product logistics under the background of rural revitalization, puts forward problems such as backward agricultural product logistics infrastructure and complex circulation links, and puts forward corresponding countermeasures and suggestions to strengthen infrastructure construction and reduce circulation links. It is of great significance to improve the logistics development of agricultural products in Guangxi and promote the local economic development.

As a special form of land circulation, land joint-stock cooperatives have become a trend of land development in the future, and have attracted widespread attention in the academic community. Many scholars have conducted in-depth research on the research of the land share cooperation system, the reasons for its emergence, its role and the influencing factors of participation willingness. On this basis, this paper combs the land share cooperatives in order to provide some reference value for the comprehensive research and future development direction of land share cooperatives.

Tool condition monitoring is vital to maintain the quality of workpieces during machining. Recently, data-driven methods based on multi-sensory data have been applied to tool condition monitoring. The quality of extracted features is a key to realizing a successful data-driven tool condition monitoring. However, the extracted features in the previous study are focused on the multi-collinearity of multi-sensory data which is incapable of identifying the informative and discriminative information in the long time period aspect. This paper proposed a novel method for tool condition monitoring using deep spatial-temporal feature extraction and lightweight feature fusion techniques. A key to the proposed method is the extraction of multi-collinearity as spatial features, and the capture of long-range dependencies and nonlinear dynamics as temporal features, to fully characterize tool wear change using multi-sensory data. Then, a lightweight feature fusion method is used to fuse spatial features, temporal features and statistical features for further removing redundant information employing the Kernel- Principal Component Analysis (KPCA). Finally, support vector machines (SVM) is used to predict the tool conditions using the fusion feature. Experiments on a milling machine and a gear hobbing machine are carried out to verify the effectiveness and generalization of the proposed method respectively.

Multi-part Dedicated Flow-line (MDF) is widely used to produce discrete products for high volumes with low varieties. Since frequent changes occur in customers’ demand and rapid upgrading of machines, the line needs to reconfigure timely to remain competitive. A lot of researchers investigate how to reconfigure the line when facing these changes, and most of them adopts small adjustments such as machine resettlement or buffer size rearrangements to react. However, when great changes occur in product’ machining process or machine’s performance, it’s time to reconfigure the line in a higher level by reselecting machining process of products and/or reselecting machines composition of the line. To fill this research gap, the paper proposes an effective Multi-part Dedicated Flow-line Reconfiguration Model (MDFRM) which can effectively react to great changes occur in products and machines. By optimal selection of Machining Process Path (MPP) for every product and machines composition for the new line, MDFRM can reconfigure MDF with objective of minimizing the products’ cost and capital investment. To solve this problem, an Enhanced Equilibrium Optimizer (EEO) with three layers of code and three steps to handling boundaries and constraints based on Equilibrium Optimizer (EO) is presented. The proposed new model and the designed method are validated by numerical experiments and real case study. This paper will significantly help decision-makers make a scientific decision to optimally reconfigure MDF.