During the vaccine production through the chick embryo cultivation method, harmful cracks may occur from the perforation of a trocar on the eggshell, around the impact hole, leading to the failure of cultivation. To detect the perforative cracks, this study proposes a method based on acoustic responses. By stimulating the embryo eggs and collecting the acoustic signals, 7 characteristic values were extracted from the time and the frequency domains: The maximum value in the time domain; The difference in the time domain; The frequency-domain peaks, 870 Hz, 1250 Hz, 1470 Hz and 1770 Hz; The mean value of the waveform. Statistical difference analysis found that all the above-mentioned characteristic values revealed differences. Then, the support vector machine and confusion matrix were combined, and the differentiation was achieved. In the statistical difference analysis, the p values of the characteristic values were all less than 0.01, indicating differences. The demarcation lines of all characteristic values obtained via binary classification through the support vector machine were as follows: TM:0.4114, TD:0.7528, FP870:5.716, FP1250:1.18, FP1470:2.96, FP1770:9.206, WM:0.6564. The weighted composite scores of the characteristic values obtained through the confusion matrix were as follows: TM: 99.18 %, TD: 99.16 %, FP870:90.56 %, FP1250:96.73 %, FP1470:98.32 %, FP1770:92.56 %, WM: 99.16 %. Experimental results demonstrated that this study could effectively distinguish qualified perforated embryo eggs from cracked ones, with the experimental prototype possessing the potential to be upgraded into an online detecting system, which comes with great significance in theory and practice for the development of the vaccine cultivation industry.

During vaccine production using the chicken embryo cultivation method, viruses such as influenza need to be inoculated into 10-day-old chicken embryos for cultivation. Prior to virus injection, perforation on the eggshell may induce cracks, leading to potential cultivation failure. This necessitates identifying and removing unqualified cracked perforated embryo eggs. Traditional two-dimensional methods for egg eligibility judgment, limited by precision and analytical dimensionality, only consider crack presence while ignoring crack acceptability, resulting in erroneous rejection or missing detection. This study proposed a global-local three-dimensional(3D) quantitative characterization framework based on principal component analysis(PCA). Through image processing and 3D reconstruction, global and local point clouds of the perforation-induced cracks were generated, fully preserving their spatial information. PCA was employed to perform global quantitative characterization of the crack geometric information, ultimately yielding 7 global feature parameters. A crack shape identification model was constructed based on the random forest algorithm, achieving a mean accuracy of 99.19%(standard deviation 0.87%) and a macro F1-Score of 98.91%(standard deviation 1.19%). For cracks requiring further evaluation, local quantitative characterization was conducted to extract perforative cracks' local geometric features parameterically. This framework achieved precise 3D reconstruction and quantitative characterization of perforative cracks, providing quantitative parameters that could support crack acceptability evaluation. It has the potential to promote perforated egg eligibility judgment from 'crack presence' to 'crack acceptability,' thereby facilitating potential automated screening of perforated embryo eggs under different virus cultivation conditions with varying crack acceptability criteria.

Single-point incremental forming (SPIF) produces localized plastic deformation, resulting in thickness reduction, geometrical deviation, and microstructural evolution. To establish the relationship between forming quality and microstructural response, this study develops a cross-scale analysis framework integrating finite element simulation, Kocks–Mecking (K–M)-based statistically stored dislocation (SSD) density inference, metallographic observation and electron backscatter diffraction (EBSD) characterization. Applied to an Al 1060 truncated-cone part, the framework converts the simulated equivalent plastic strain (PEEQ) into SSD density via the K–M model calibrated using the Voce hardening model and the Taylor relation. The inferred SSD density distribution is then spatially correlated with thinning rate, geometrical deviation, grain size, grain-boundary misorientation, kernel average misorientation (KAM), and geometrically necessary dislocation (GND) density across different forming regions. The inferred SSD density rapidly approached a saturation level of 1.55 × 10¹³ m⁻², while the high-SSD density region progressively expanded during forming. This regional evolution was qualitatively consistent with the EBSD observations. The average grain size decreased from 30.4 μm to 21.9 μm, and the medium-angle grain-boundary fraction increased from 10.3% to 34.5%. Regionally, thickness reduction correlates strongly with PEEQ accumulation, SSD storage, and grain refinement, whereas geometrical deviation is more closely related to early-stage deformation heterogeneity. These findings provide a physically based route for predicting and controlling SPIF accuracy.