The forging press plays a crucial and indispensable role in industries such as metallurgy, automotive, and aerospace. Its stable and efficient operation directly impacts the productivity and product quality of the entire production line. Therefore, it is particularly important to carry out efficient fault diagnosis for forging presses. In this paper, a method combining Convolutional Neural Network (CNN), Principal Component Analysis (PCA) and Transmission Support Vector Machine (TSVM) is proposed, which is called Multi-modal Attention-based CNN with PCA and TSVM (MACNN-PTSVM). This method effectively integrates multi-sensor information fusion technology, attention mechanism, CNN, PCA technology and TSVM, fully utilizing data collected from various sensors of the forging press. The constructed network model takes smooth monitoring signal data from the die forging press as input samples. Fault features are extracted through CNN, and PCA is utilized for feature fusion. Applications of this model in forging press fault diagnosis demonstrate significant advantages in fault classification performance. Specifically, its semi-supervised learning technology exhibits higher generalization ability and diagnostic accuracy when dealing with unlabeled data.

With the engineering system becoming more and more complex, the uncertainty factors have a more and more profound influence on the reliability and security of the engineering system. In recent years, Reliability-Based Design Optimization (RBDO) has been studied extensively in complex engineering system design. The research on reliability optimization design considering stochastic uncertainty has been comprehensive and widely used. However, the reliability optimization design considering mixed uncertainty has the disadvantages of large computation and imprecise convergence. This study proposes an RBDO method based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to overcome this challenge. In the method, PSO is used to solve the most probable point, while SA has excellent global optimization capability to acquire the global optimal solution. Finally, three examples are given to illustrate the advantages of the proposed method.

Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.