The use of metallographic images to predict the mechanical properties of materials and their corrosion behavior is helpful in achieving nondestructive detection and quality control. However, after a long-term attempt, the traditional methods cannot accurately correlate the mechanical properties and corrosion behavior of materials with the corresponding microstructure images. In this study, we propose a deep learning strategy to predict the mechanical property and corrosion behavior of large-scale extruded aluminum profiles using surface optical microstructure images. The proposed models with remarkable properties were established through experimental dataset collection, dataset preprocessing, deep learning network modification, and key parameter screening. Taking extruded Al-Zn-Mg alloys with different surface microstructures as example materials, 4,800 sets of ''metallographic image -hardness (HV) -corrosion potential (E corr )'' data were experimentally collected to establish the HV and E corr models with prediction accuracies of 90% and 82%, respectively. The proposed HV and E corr models exhibit great generalization ability with mean average errors of 1.8 HV and 7.0 mV on experimental validation sets, respectively. The proposed model can accurately correlate the metallographic images, mechanical property, and corrosion behavior, which can provide theoretical support for intelligent and nondestructive testing methods to further prevent unexpected material failure.

In the present study, an investigation is conducted into the effect of different aging treatments on the hardening properties of the 6151 aluminum alloy sheet. According to the research results, the artificial aging hardening response of the pre-aging sheet is significantly stronger compared to natural aging after the solution treatment. In the stage of artificial aging, 120 oC pre-aging treatment produces a more significant strengthening effect than 80 oC and 100 oC pre-aging treatment. The longer the artificial time, the higher the hardness value. When the artificial aging temperature reaches 200 oC, the time taken to achieve peak aging is the shortest, and the occurrence of softening is evident in the over-aged state. When the artificial aging temperature is 200 oC, the softening effect becomes more significant. Natural aging can inhibit the strengthening effect of artificial aging. With the extension of natural aging, the hardening effect of artificial aging diminishes gradually.