PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

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Nondestructive infrared detection of solder defects in solenoid coil connectors was investigated. A detailed experimental setup and a meticulous and repeatable experimental procedure were developed. The experimental methods were firmly based on the application of heat transfer science. Time averaged excess temperature ([Formula: see text]) was the sole discrimination parameter, and a difference larger than 2°C between good and defective solders was conservatively considered for good discrimination. Analysis of variance determined the minimum differences in [Formula: see text] between good and defective solder joints. The effects in discriminability of heating time ( th), time integration interval length ( ti), region of interest (ROI) geometry, and solder radiative properties were investigated. A rectangular ROI tightly enclosing the good solder was found as the best discrimination ROI. Matte black paint increasing radiative emission of solder joints significantly reduced discriminability. The defects were discriminated from the good solder for a rectangular ROI, th = 78 s, and ti = 20 s. Moreover, an excellent total inspection time of 34 s per solenoid coil is obtained by considering a well-controlled inspection environment during manufacturing, severe defects detection, and a possible 1°C difference for discrimination. Several aspects of the novel inspection procedure developed can greatly benefit the thermal imaging scientific community.

Requirements of low energy consumption and material-volume reduction in the aerospace industry have spurred improvements of mechanical and tribological behaviors of TiAl (TA) alloys. TA-graphene (TAG) has poorer mechanical properties (6.02 ± 0.42 GPa nano-hardness, 150 ± 12.32 GPa elasticity modulus, and 802 ± 21 MPa yield strength) than (6.25 ± 0.52 GPa nano-hardness, 159 ± 14.21 GPa elasticity modulus, and 850 ± 19 MPa yield strength) of TA-graphene-silver (TAGS). Multilayer graphene nanosheets were curled into small loops to resist the applied forces, and helped to improve the mechanical properties of the TAGS. Subsequently, the graphene nanosheets enhanced the tribological performances as observed by the ball-on-disk tribometer. The following factors were primarily responsible for more excellent tribological behaviors (approximately 0.27 friction coefficient, 2.82 × 10−4 mm3 N−1 m−1 wear rate) of TAGS than those of the TAG: intra-lamellar separation of graphene, graphene-enhanced capacity of wear scar, plastic deformation of silver, the excellent cooperation lubrication of graphene-silver, the low-hardness lubrication film on the grain-refined layer, the well-distributed film grain, and low grain orientation angles.

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