30CrMnSiA thin-wall cup-shaped part is a key basic component widely used in the flexible gear of harmonic reducer. Aiming at the problem of the low bearing capacity and short service life of harmonic reducer caused by insufficient strength and toughness of 30CrMnSiA thin-wall cup-shaped parts manufactured by traditional turning method, this paper proposed a plastic deformation-heat treatment process which consists of spinning, quenching, tempering, spinning and aging to manufacture 30CrMnSiA thin-wall cup-shaped parts with less or no cutting and excellent mechanical properties. Through the tensile and impact experiment, by comparing the mechanical properties of each process part, the microorganization of each process part was analyzed. The results show that tempered sorbite microstructure with high strength can be obtained by spinning-quenching and tempering process, but the plasticity was reduced significantly. The fibrous microstructure of spun parts can be further refined by aging heat treatment, and the fine carbides precipitated and uniformly distributed on the ferrite matrix. High strength and good plasticity can be obtained by subsequent aging at 300 ℃ for 6 h. As compared with parts obtained by turning forming after quenching and tempering heat treatment, the yield strength and tensile strength of 30CrMnSiA thin-wall cup-shaped parts manufactured by plastic deformation and heat treatment are improved by 93.65% and 47.88%, respectively. The hardness is increased by 26.87%, and the impact strength is increased by 12.01%. Meanwhile, the elongation and percentage reduction of area are 11.60% and 24.64%, respectively. The thin-wall cup-shaped parts with high strength and toughness can be manufactured by the plastic deformation and heat treatment process of spinning-quenching-tempering-spinning-aging, which provides a new method for manufacturing the thin-wall cupshaped parts with high-strength and toughness.

In view of the scheduling problem of die electrode in CNC and EDM stages, a mathematical model with batch processing and correlation characteristics was established. To minimize the tardiness of die parts, the solving process of die electrode scheduling problem was divided into two stages: batch processing and batch scheduling. In the first stage, the batch processing problem was solved according to the principle of correlation, and the correlation priority batch algorithm was designed. In the second stage, the batch scheduling problem was solved by genetic algorithm, and a strategy based on animal breeding was proposed to improve the traditional genetic algorithm. The die electrode scheduling program was developed based on MATLAB software to realize the above two stages of solving process, and the test was carried out under 24 kinds of simulation examples. The results show that, the designed die electrode scheduling algorithm is effective for solving the batch processing and batch scheduling problem of die electrode; the proposed strategy of breeding can significantly improve the quality of traditional genetic algorithm solution; and the tardiness of die parts in the examples can be reduced by 16.71% at most.

Based on the fitting relationship between residual compressive stress and overlap ratio, the laser spot path was designed under non-uniform overlap ratio. A laser shock peening finite element (FE) model for laser shock peening damaged 7075 aluminum alloy component was established based on ABAQUS software. The non-uniform overlap ratio laser shock peening simulation was realized and the distribution of residual stress was obtained. The results show that the non-uniform overlap ratio laser shock peening can keep the surface of the specimen in a state of uniform stress after grinding or under tensile load. By increasing the overlap ratio to control residual stress, the surface of the repaired component can be kept in a state of uniform compressive stress under tensile load, so as to inhibit the initiation and propagation of fatigue cracks. The experimental results are consistent with the simulation results, which verifies the reliability of the model.

Exploring the material flow behavior during the closed die forging is the theoretical basis for controlling forming defects such as insufficient filling and obtaining high-precision die forgings. In response to the problem of insufficient filling during hot die forging of gear blanks with deep spoke, this study selected the SCr420H gear blank with deep spoke as the research object and designed a multi-station closed hot die forging process of“cutting—heating—upsetting—preforging Ⅰ—preforging Ⅱ—final forging”based on structure analysis of the gear blank. Based on Deform, it established a finite element simulation model for the entire process of multi-station closed hot die forging. The reliability of the finite element model was verified by the experimental results, and the material flow law during the multistation closed hot die forging process was studied. The results show that a circular cake shaped blank can be obtained when the upsetting ratio is 3.7. The material flow along radial direction is uneven due to the friction effect between the upper and lower surfaces, resulting in a bulge shape at the waist of the billet during upsetting. A concave structure was formed on the bottom surface for positioning during pre-forging station Ⅰ, and it reduced the difficulty of material filling in subsequent stations. The material flow law during pre-forging Ⅱ and final forging are similar. The material mainly flows to the wheel flange part in the early stage of forming. It mainly flows to the wheel hub part in the middle stage, and flows to the rounded corner in the late stage. There is no defect of insufficient material filling during final forging. In the late forming stage, a small amount of metal material flows out of the guide gap of the upper and lower die of the final forging, forming a longitudinal flying edge. Through the production test, the well-filled gear blank with deep spoke was formed. The maximum error between the simulated value and the actual production value is not more than 3.15%, the longitudinal flying edge height is less than 0.5 mm, and the size deviation of each part is less than 0.2 mm, which verifies the rationality of the designed multi-stage closed die forging process.

Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.