Mechanical properties, such as residual stress, micro-hardness and fatigue performance, of the Ti-5Al-4Mo-4Cr-2Sn-2Zr titanium alloy were improved via the laser peening without coating (LPwC) with a water-penetrable wavelength of 532 nm and pulse duration of 10 ns. In this paper, three kinds of laser energy, namely 85, 110 and 160 mJ were used to process the samples. The titanium alloy samples were also peened with different impact times (1, 3 or 5 impacts) at the energy of 85 mJ. The micro-hardness and residual stress distribution results provided that LPwC can introduce compressive residual stress (CRS) and also induce hardening of the target materials. Further, micro-hardness and CRS showed the increasing trends when the laser impact times increased. However, the CRS and micro-hardness decreased while the laser energy increased from 110 to 160 mJ, which was attributed to the dynamic equilibrium between the thermal and mechanical effects of LPwC. High cycle fatigue strength of the titanium alloy was significantly improved from 360 to 490.3 MPa after three impacts LPwC. The strengthening mechanism of fatigue strength subjected to LPwC was a combined effect between the laser-induced CRS and the high-density dislocations.

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies.

Ignoring real decision contexts in linguistic multi-criteria classification (MCC) causes unreasonable weights, rank reversal and unreliable classification. This paper develops a context-constrained MCC framework integrating case-based reasoning, grey target theory and cloud model. Unlike conventional data-only grey target methods, three practical contextual features structurally reshape all algorithm modules: linguistic dual fuzziness-randomness, labeled benchmark cases and expert weight preferences. First, cloud Hamming distance and cloud-based dual target centers are constructed to eliminate rank reversal, a property invalid without linguistic uncertainty. Second, benchmark case constraints build a target-score classification mechanism with case-calibrated grading thresholds. Third, expert weight bounds form a bi-objective optimization model fusing multi-source information to avoid data-biased weights. A riparian wetland ecological grading case validates the method. A rigorous mathematical proof, counterexample contrast and large-scale random simulation are further supplemented to verify that the proposed fixed dual cloud target center rule significantly mitigates rank-reversal phenomena triggered by adding new alternatives under the designed experimental settings. Comparisons show our framework yields domain-consistent weights, grading outputs with perfect agreement with independent blind expert judgments, and stable monotonic scoring performance (where larger target scores correspond to worse ecological conditions), which achieves more consistent grading results compared with cloud entropy weighting and virtual linguistic approaches in the tested wetland case.