The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network is the most accurate. This research provides scientific and technical support for measuring and predicting small leaks.

Machining of No. 45 steel (AISI 1045) becomes more vital due to its widespread use. In this study, machining performances of abrasive water jet machining (AWJM) of No. 45 steel, including material removal rate, notch depth, and nozzle wear rate, were obtained by experimental and computational results. The Taguchi L16 orthogonal array design was used to study the influence of process parameters on machining performance. The optimal material removal rate and notch depth were achieved when abrasive particle size, operating pressure, and abrasive feed rate were 80 #, 400 MPa, and 840 g/min, respectively. The optimal nozzle wear rate was achieved when abrasive particle size, operating pressure, and abrasive feed rate were 80 #, 400 MPa, and 260 g/min, respectively. When the abrasive particle size is 80 # (namely the mesh number is 80), the particle diameter is usually between 0.18 and 0.25 mm according to the corresponding relationship between the international standard mesh number and particle diameter. Analysis of Variance was conducted to evaluate the statistical significance of the experimental results. Using regression analysis, an empirical model was developed to predict the response values of the AWJM process. Multi-response optimization was then carried out using the Decision Engineering Analysis and Resolution method. The optimal parameter solution for a higher material removal rate, a bigger notch depth, and a smaller nozzle wear rate was achieved when abrasive particle size, operating pressure, and abrasive feed rate were 120 #, 400 MPa, and 870 g/min, respectively.

High-pressure abrasive water jet (HP-AWJ) cutting is a prominent technology for processing a wide variety of materials. The structural parameters of the nozzle are important for the cutting performance of the HP-AWJ. This paper combines an abrasive particle kinetic energy model and a wall wear model of the nozzle to determine the multi-phase flow of a HP-AWJ nozzle. The flow field structure of the nozzle was optimized using a parametric multi-objective structure optimization design method. A Multi-Objective Heat Transfer Search (MOHTS) was utilized to generate the corresponding mathematical regression model for multiple response results, and the optimal solution sets of Pareto values were further obtained. The optimal HP-AWJ structural parameters could be selected according to the weight influence of multiple response indicators.