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As a special kind of advanced aerospace sheet metal forging equipment, more and more attention has been paid to the design and development of isothermal forming hydraulic press. Failure Modes, Effects and Criticality Analysis (FMECA) mathod is an effective technique to improve the reliability of products. To improve the reliability of isothermal forming hydraulic press, this paper propose FMECA method to predict all potential failure modes, causes and effects in the design stage. Then improvement and preventive measures are accordingly put forward to alleviate or eliminate failure effects. Finally, the reliability of hydraulic press body is improved using the FMECA mathod, which also provides a basis for reliability analysis and design of other systems.

Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as without good hyper-parameter values the performance of these algorithms will be very poor. Unfortunately, for complex machine learning models like deep neural networks, it is very difficult to determine their hyper-parameters. Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization. In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning algorithms. This method is defined as the Tabu_Genetic Algorithm. In order to verify the performance of the proposed algorithm, two sets of contrast experiments are conducted. The Tabu_Genetic Algorithm and other four methods are simultaneously used to search for good values of hyper-parameters of deep convolutional neural networks. Experimental results show that, compared to Random Search and Bayesian optimization methods, the proposed Tabu_Genetic Algorithm finds a better model in less time. Whether in a low-dimensional or high-dimensional space, the Tabu_Genetic Algorithm has better search capabilities as an effective method for finding the hyper-parameters of learning algorithms. The presented method in this paper provides a new solution for solving the hyper-parameters optimization problem of complex machine learning models, which will provide machine learning algorithms with better performance when solving practical problems.

Ram is a very important component of super-heavy-duty computer numerical control (CNC) floor type boring-milling machine, and deformation of ram is a significant source causing errors in machining process. To compensate the deformation error of super-heavy-duty CNC floor type boring-milling machine, based on force analysis theory, the law and compensation measures of deformation of ram are researched. Based on the principle of torque (force) balance of the ram components, the formulas of compensation forces and compensation torques are derived, the relations between compensation forces (compensation torques) and the stroke distance of the ram are given. According to theoretical analysis results and the structural characteristics of super-heavy-duty CNC floor type boring and milling machine of TK6932, rods compensation, hydrostatic pressure compensation and wire rope compensation measures are taken to compensate the deformation error of ram. The experiments and computer simulation results show that the straightness of the ram at its overhanging end meets the national machinery industry standards.