Rotor assembly is a core tache in the whole process of aero-engine manufacturing. Preventing out-of-tolerance of concentricity is one of the primary tasks. Conventional assembly approaches are based on a manual test with the dial indicator, depending on experience appraises, which lack systematic and quantitative precision design theory. As a result, two issues need to be solved: the modeling problem of complicated geometric variations in three-dimensions, as well as the abnormal distribution of ubiquitous actual deviations. This work attempts to propose a novel probabilistic approach for three-dimensional variation analysis in rotor assembly. Based on rotor’s revolving characteristics and multistage stacking process, Jacobian–Torsor model is adopted to establish the variation propagation, and Pearson distribution family is used to derive the probability density function, which can quickly determine the variation distribution pattern and efficiently perform statistical variation analysis. A real case of mechanical assemblies consisting of revolving axisymmetric components is concerned. The results show that the suggested method has a similar accuracy, but much higher efficiency than conventional methods. Calculations agree with the experimentations, and the probability distribution type of the part’s variation has an appreciable impact on the final assembly precision.

The rotation precision of rotors determines the efficiency and quality of the overall aero-engine, as well as its long-term and reliable operation ability. As the terminal link of aero-engine manufacturing, the assembly is the last guarantee of precision control. Rotor assembly relates to the accurate expression of the connection form and design optimization of the assembly scheme. The existing variation model cannot adequately handle the partial parallel chain problem, ignoring the bayonet circular connector between the rotor parts, and it is still deficient in multistage gyration error control. In this paper, the partial parallel connection and multistage revolving characteristics of rotors were discussed, and a novel modeling and optimizing method for a partial parallel dimension chain was proposed. On the one hand, the variation expression of the connection features for the revolving components considering the partial parallel structure was researched. Contact point-based torsors were represented, and a system for locating points was regarded as an assembly to describe the partial parallel chain. On the other hand, the variation propagation modeling and control for the stacking of the multistage revolving components was researched. A revolution joint was introduced in the unified Jacobian–Torsor model, and a novel assembly technique for concentricity control was proposed. Therefore, a unified variation analysis and control method for rotor assembly has been developed. Experimental results show that through this method, the final concentricity variation is 0.0539 mm, far less than the 0.1595 mm of the traditional model, and is closer to the true value range of 0.030–0.040 mm. Moreover, the optimum installed angles can be calculated as 3.153 rad, 6.025 rad, and 2.590 rad, to obtain the highest concentricity of 0.040 mm, which has strong practical guiding significance.

Weakly rigid drilling systems, such as the industrial robot, are widely used in aerospace, military, and other fields due to its good flexibility and large scope of operation. However, the weak rigidity can easily cause burrs, seriously affecting the precision of parts and product performance. To reduce the heavy deburring process and to improve continuous production and sustainable processing capacity, accurate prediction of burr quality is a prerequisite. Traditional burr forming theory cannot accurately predict the drilling defects. Data-driven approaches can be independent of prior knowledge and discover relationships between process parameters and machining precision directly from the data structure itself. Therefore, to take advantage of both approaches, a fusion model was established for burr classified prediction. On the one hand, the drilling and burr forming process was firstly modeled, and preliminary classification results for burrs were calculated. On the other hand, according to the measured data, the errors between initial calculation results and actual classification results were obtained and selected as the tag values of dataset, which served as inputs for the error compensation model of burrs. Finally, by training the network of TCN–DNN using the drilling data, the burr classified prediction in a weak rigid hole-making system was realized. Experimental results showed that compared with traditional drilling theory, the prediction accuracy of the proposed model improved by 25%, reaching 91.67%. The results can provide a basis for judging the process of burr post-treatment, which has practical guiding significance. This method is beneficial to reduce the heavy deburring process and to improve sustainable processing capacity.

It is of strategic significance to extract germanium (Ge) in an ecological way for sustainable development. Adsorbents that already adsorb Ge have disadvantages such as poor selectivity and low adsorption capacity. In this study, a novel adsorbent material based on rice husk functionalized with tannic acid was developed for the efficient extraction of Ge from simulated coal fly ash leachate. The adsorption capacity of tannic acid-functionalized rice husk (TA-EPI-ORH) for Ge was 19.9 times higher than that of untreated rice husk, demonstrating significantly improved performance. The results showed that the adsorption process of Ge by TA-EPI-ORH is consistent with pseudo-second-order kinetic and Freundlich isotherm model. TA-EPI-ORH had excellent selective adsorption properties, with adsorption of 1.40 mg L⁻¹ Ge exceeding 95% and solid-liquid partition coefficients of 4380 mL g⁻¹, even in the presence of nine impurity metal ions (average concentration: 479.08 mg L⁻¹). When compared with the two main coexistence ions—aluminum (Al) and calcium (Ca)—both of which have the relatively highest concentrations (Al: 1594.20 mg L⁻¹, Ca: 1740.13 mg L⁻¹), the separation factors for Ge still maintain relatively high level with SF(Ge/Al) = 42.57 and SF(Ge/Ca) = 39.93. Compared to existing studies, TA-EPI-ORH exhibits superior selective adsorption performance even with the presence of more interfering ions. After elution of the adsorbed Ge from TA-EPI-ORH, the extraction rate of Ge with low initial concentration (1.40 mg L⁻¹) reached 85.17%, while the extraction rates of Al and Ca were only 1.02% and 1.18%, respectively. Further research revealed that the catechol groups on the surface of TA-EPI-ORH formed stable complexes with Ge, whereas the complexes with coexisting ions (e.g., Ca and Al) were unstable, thereby ensuring high selectivity for Ge. This green chemistry-based functionalization of rice husk not only enables high-value utilization of agricultural waste but also provides a sustainable and eco-friendly strategy for efficient Ge separation and recovery.

Indoor positioning using commodity Wi-Fi infrastructure is attractive for smart buildings and Internet of Things applications, but the practical accuracy of Wi-Fi Fine Timing Measurement (FTM) remains limited by non-line-of-sight propagation, multipath delay, access-point-dependent ranging bias, and unstable anchor geometry. This paper proposes a measurement-reliability learning and geometry-constrained fusion framework, termed MRL-GCF, for robust horizontal Wi-Fi FTM indoor localization. MRL-GCF learns the reliability of each access-point observation from a multi-factor representation that includes Received Signal Strength Indicator (RSSI), logarithmic FTM range, short-window range stability, RSSI fluctuation, access-point visibility, abnormal-range tendency, and coarse anchor geometry. A lightweight heteroscedastic neural calibrator estimates both range bias and observation uncertainty. A supervised reliability-regime head is further trained from residual-regime soft targets, and its entropy is used as a propagation-ambiguity measure. The learned uncertainty is fused with propagation ambiguity, map obstruction, material-aware obstruction cues, and anchor geometry to select reliable anchors and construct a trust-weighted nonlinear least-squares localization objective. To avoid overestimating performance from repeated scans at identical survey points, both scan-level and point-held-out protocols were adopted. Experiments were conducted in a lobby, a classroom, and a dormitory using 4410 synchronized RSSI-FTM scans. On 882 scan-level test queries, MRL-GCF achieved mean absolute errors of 0.88 m, 0.55 m, and 1.20 m, with sub-3 m success rates of 98.0%, 99.0%, and 96.5%, respectively. Additional replay-based dynamic, temporal, cross-device, AP-density, uncertainty-calibration, map-availability, and coefficient-sensitivity analyses were included to examine deployment-oriented robustness. These results indicate that learning measurement reliability while preserving geometric constraints provides a practical and interpretable solution for robust Wi-Fi FTM indoor positioning.