Aluminum profile surface defects can greatly affect the performance, safety, and reliability of products. Traditional human-based visual inspection has low accuracy and is time consuming, and machine vision-based methods depend on hand-crafted features that need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect-detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, and 43.3% average precision (AP) for the 10 defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

The interference fit can improve the fatigue performance of mechanical joints and is widely used in aircraft assembly. In this paper, specimens of lap plates and several interference fit sizes were designed, and then the interference fit hi-lock bolt insertion was carried out in an experimental test. Using the commercial finite element software ABAQUS, a two-dimensional axisymmetric finite element model was established to simulate the bolt insertion process. The finite element model was validated by comparison of experimental results and finite element prediction for insertion force and protuberance height. After the interference fitted bolt insertion, the changing characteristics of the non-uniform hole expansion and protuberance were presented with increases in interference fit size. Under low level of interference fit, the tensile hoop stress was produced mainly on the hole wall, and changed into compressive hoop stress when interference fit size is larger. The maximum tensile hoop stress point on faying surfaces went away from the hole wall with interference fit size increasing.

Interference-fit riveting is one of the most widely used mechanical joining ways in aircraft assembly. The fatigue performance of riveted joints has a significant impact on the service life and reliability of aircraft. In this paper, the fatigue performance of the riveted lap joints with various rivet diameters and pitches are studied based on stress distribution analysis under tensile load. First, a theoretical model of the riveted lap joint under tensile load is developed by using the spring-mass model. The rivet-load stress, bypass stress, and interference stress around the riveted hole are analyzed. Then, the finite element (FE) model of riveted lap joints are established. The influence of rivet diameter and pitch on stress distribution around the riveted hole are discussed. Finally, the fatigue tests are conducted with riveted lap joint specimens to verify the theoretical model and FE results, and a good agreement is observed. Based on the simulation and experimental results, a good combination of structural parameters of the riveted lap joint is found which can optimize the stress distribution around the riveted hole and improve the fatigue life of the riveted lap joint.

To predict the distortion of aerospace monolithic components, a model is established to simulate the numerical control (NC) milling process using 3D finite element method (FEM). In this model, the cutting layer is simplified firstly. Then, the models of cutting force and cutting temperature are established to gain the cutting loads, which are applied to the mesh model of the part. Finally, a prototype of machining simulation environment is developed to simulate the milling process of a spar. Key factors influencing the distortion, such as initial residual stress, cutting loads, fixture layout, cutting sequence, and tool path are considered all together. The total distortion of the spar is predicted and an experiment is conducted to validate the numerical results. It is found that the maximum discrepancy between the simulation results and experiment values is 19.0%