Predictions of frame compliance and apex morphology in sharp nanoindentations

If all components in a nanoindentation system are well calibrated and a reference material has unique hardness, H and reduced modulus, E_r independent of the indentation depths, the load, L and the penetration depth, h in the indentation loading curve of the reference material can be correlated by L=Kh². Here the constant K is expressed by H, E_r and indenter geometry constants. By using H and E_r of a fused silica and the Berkovich geometry, an analytical expression for the indentation loading curve could be derived. To compare with this analytical loading curve, experimental indentation data were measured with two commercial nanoindenters. The experimental loading curves shifted leftward or rightward from the analytical loading curve and this depth deviation was attributed to improper calibration of the nanoindenters. Quantitative calibrations of frame compliance and indenter bluntness were tried for the raw nanoindentation data and this resulted in consistent nanoindentation data regardless of the used nanoindenters.     

Damage accumulation modeling under uniaxial low cycle fatigue at elevated temperatures

The paper presents a fatigue damage accumulation model, which allows us to predict fatigue life under low cycle uniaxial loadings at elevated temperatures. The structure of the model has been based on the stress–strain curves obtained during the experimental study. The model has been verified experimentally by applying experimental studies carried out on ENAW-2024T3 aluminum alloy and 2Cr–2WVTa steel. Moreover, a comparison between the results of fatigue life prediction using the proposed damage accumulation model was done with the results obtained on the basis of various generally applied models, based on the Manson–Coffin dependency. Furthermore this paper presents the results of experimental studies carried out on the aluminum alloy ENAW 2024 T3 under uniaxial low cycle fatigue loadings in the conditions of elevated temperatures. In the course of the study, material constants and the parameters of the stress–strain curve in the range of low cycle fatigue for four levels of temperatures (20, 100, 200 and 300 °C) were set.     

Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

Performance curve available for non-steady flow through Venturi-like reverse flow diverter

A reverse flow diverter (RFD) consists of a driving nozzle, a diffuser, and a suction gap that separates the nozzle and diffuser. Thus, the RFD is a Venturi-like fluidic component with three ports. The jet flow emanating from the driving nozzle exit can entrain the ambient fluid and transport it to a high elevation. During this time, the flow through the RFD is non-steady, which makes it difficult to measure the flow depending on the pressure drop. In this study, a series of tests was carried out to evaluate this fluid flow with different contraction ratios, suction gap lengths, fluid properties, inlet flow rates, and inlet pressures. A performance curve was formulated that can be expressed as an exponential equation correlating the non-dimensional Euler number, pressure ratio, and suction factor. The performance curve is not affected by the driving nozzle exit diameter and suction length of the RFD. The performance curve makes it possible to measure the flow out of a RFD depending on the pressure drop.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

基于功能参数的渐进成形件表面质量表征研究

目的 以圆锥台成形件为例，基于粗糙表面的区域支承率曲线，进行了用功能参数表征表面几何形貌的研究，以全面、准确、定量地表征渐进成形件的表面质量，预测表面性能。方法 用渐进成形工艺加工成形角分别为35°、45°和55°的圆锥台件，将各圆锥台的成形面分为顶部、中部和底部三个区域，并使用三维表面轮廓仪，在上述三个区域中随机测量各4个样本。根据ISO 25178-2:2012，将各样本表面的区域支承率曲线分为峰区、核心区和谷区，选用部分功能参数——核心区高度Sk、峰区材料体积Vmp、核心区材料体积Vmc和谷区空隙体积Vvv表征表面，并与常用的高度表征参数——Sa、Sq、Ssk和Sku进行比较。结果 成形件表面的Ssk和Sku的计算值极不稳定，变异系数（标准偏差与均值之比）最高值分别可达127.67%和39.11%，Sa、Sq的最大变异系数分别为4.41%和4.26%，虽然它们的参数计算值较为稳定，但常无法体现表面形貌的实际差异。功能参数独立表征不同功能区，Sk和Vmc的最大变异系数分别为9.32%和7.73%，说明同一表面各处，在长期工作阶段具有较为一致的表面性能，Vmp和Vvv的最大变异系数分别为60.53%和31.25%，说明表面各处峰、谷区的形貌有所不同，因而在磨合时间、磨粒存储等方面会有不同的表现。结论 粗糙表面的三维本质要求用三维表征参数才能全面表征其几何形貌。高度参数虽然计算简单，但常难以解读它们的物理意义，无法定量评价表面性能；功能参数具有明确的物理意义，可为表征表面质量、预测表面性能提供定量、实用的方法。     

Environmental Engel curves in Italy: A spatial econometric investigation

This paper adopts a spatial econometric methodology to investigate the relationship between income and direct household emissions in Italy, as posited by the environmental Engel curve. The spatial approach is motivated by an expanding strand of studies that have detected significant spatial interactions in household-polluting activities responsible for the generation of direct household emissions. Our results suggest the presence of significant spatial dependence for household emissions emerging among regions. At the same time, increases in the level of income do not seem to be coupled with a higher demand for environmental quality by Italian households.     

双机器人系统运动协调仿真与实验研究

针对具有重量大、形状不规则特征的物品,采用双机器人协同作业是该技术领域的研究热点问题之一。运用ADAMS软件对双机器人虚拟样机进行运动学仿真分析,模拟空间搬运轨迹,对DREE末端点的运动轨迹曲线进行同步误差分析。搭建了实验平台,对双机器人同步搬运箱体进行实验验证。实验结果表明:双机器人协作搬运箱体时,在水平方向和垂直方向的轨迹误差均小于±2.8mm,较好地满足同步搬运作业工艺要求。     

355 MPa级转向架构架用钢连续冷却转变行为

高速动车组转向架构架对高速动车组的运行品质、行车安全起到至关重要的作用。为向355 MPa级转向架构架用钢的工业试制提供指导，在Gleeble-3800热模拟试验机上进行了热模拟试验，结合热膨胀法和金相-硬度法，绘制了355 MPa级转向架构架用钢动态连续冷却转变曲线，研究了试验钢在连续冷却条件下的显微组织演变规律。结果表明，冷速小于1 ℃/s时，其组织主要为多边形铁素体和珠光体；冷速大于5 ℃/s时，贝氏体类组织快速增加；冷速达到50 ℃/s时，为板条贝氏体组织。因此，对于355 MPa级转向架构架用钢，冷速控制在1~5 ℃/s较为适宜，其组织主要由比例适中软硬性相结合的铁素体、珠光体和贝氏体构成，可以获得优良的强韧性匹配。