Unveiling the Re,Cr,and I diffusion in saturated compacted bentonite using machine-learning methods

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry densities.The machine-learning models were trained using two datasets.One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 publications.The other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input features.The results indicate that the former dataset yielded a higher predictive accuracy than the latter.Light gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning algorithms.In addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.

High-throughput studies and machine learning for design of β titanium alloys with optimum properties

Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.

Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.

Research on steel-fibber polymer concrete machine tool structure

Researched on the design and manufacturing of machine tool bed made by Steel-fibber Polymer Concrete(SFPC),which analyzed the static,dynamic and thermal performances of the bed.The results of study prove that machine tool bed made with SFPC is much more superiority than made in cast iron in dynamic and thermal perform- ances,and is more superiority then made in Polymer Concrete (PC) in static perform- ances.It can be concluded that the static,dynamic and thermal properties of machine tool can be improved by manufacturing machine tool bed with SFPC.Also SFPC machine tool bed posses some other advantages in the following: short development time,simple pro- duction process,reducing cost cost,saving energy,iron and steel.

Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

Solid solution strengthening(SSS)is one of the main contributions to the desired tensile properties of nickel-based superalloys for turbine blades and disks.The value of SSS can be calculated by using Fleischer’s and Labusch’s theories,while the model parameters are incorporated without fitting to experimental data of complex alloys.In thiswork,four diffusionmultiples consisting of multicomponent alloys and pure Niare prepared and characterized.The composition and microhardness of singleγphase regions in samples are used to quantify the SSS.Then,Fleischer’s and Labusch’s theories are examined based on high-throughput experiments,respectively.The fitted solid solution coefficients are obtained based on Labusch’s theory and experimental data,indicating higher accuracy.Furthermore,six machine learning algorithms are established,providing a more accurate prediction compared with traditional physical models and fitted physical models.The results show that the coupling of highthroughput experiments and machine learning has great potential in the field of performance prediction and alloy design.

LoRA模型微调Stable Diffusion的设计师风格服装生成方法

为控制Stable Diffusion模型准确生成特定设计师风格的服装图像,应用LoRA模型微调Stable Diffusion生成过程的方法实现风格化服装图像生成。以中国十佳时装服装设计师孙林的设计风格为例,首先对设计师的发布会服装进行系统分析,提取设计师的风格特征因子,根据设计师风格因子分别训练3个风格化LoRA模型,分别验证每个风格因子的表达效果。最后,通过3个LoRA模型叠加使用的方式,生成具有设计师风格特征的服装设计图像。结果表明:同时调用多个风格化LoRA模型微调Stable Diffusion的生成过程,能够使生成的服装符合设计师的服装风格,实现设计师风格的迁移设计,提高设计师进行服装系列设计和产品开发的效率。

Elements inter-diffusion in the turning of wear-resistance aluminum bronze

Inter-diffusion of elements between the tool and the workpiece during theturning of aluminum bronze using high-speed steel and cemented carbide tools have been studied. Thetool wear samples were prepared by using M2 high-speed steel and YW1 cemented carbide tools to turna novel high strength, wear-resistance aluminum bronze without coolant and lubricant. Adhesion ofworkpiece materials was found on all tools' surface. The diffusion couples made of tool materialsand aluminum bronze were prepared to simulate the inter-diffusion during the machining. The resultsobtained from tool wear samples were compared with those obtained from diffusion couples. Stronginter-diffusion between the tool materials and the aluminum bronze was observed in all samples. Itis concluded mat diffusion plays a significant role in the tool wear mechanism.

基于AI图像生成技术的家具设计研究

简述了AI图像生成技术及其在家具设计中的应用优势,提出AI图像生成技术在家具设计中的一般流程。基于Stable Diffusion AI设计平台,依据“文本指令”进行家具设计尝试,以包豪斯风格家具设计实践来验证AI图像生成技术辅助生成家具设计效果图的高效性。研究表明:AI图像生成技术能合理运用于家具设计创意阶段,对缩短家具设计周期、提升家具设计质量具有实际意义。

生成式人工智能在面料外观仿真上的研究

数字经济对纺织服装产品的数字孪生仿真有更高的要求,服装数字孪生的品质关键在于纺织面料数字化的质量与效率。本文提出了一种基于机器学习的织物仿真方法。以潜在扩散模型为基础,采用LoRA的微调模型方法,以标签化的织物外观图片集为训练集,训练一个织物外观仿真的模型。与数字服装领域通过扫描面料获得其外观图片的方法相比,该方法速度快、效果好。与成熟的商用图片生成程序生成的图片相比,该模型生成的图片更具有针对性,仿真效果更加逼真。该模型生成的织物外观图片丰富多样,能够根据不同的文本提示词生成不同的织物外观图片,提高了织物外观的设计效率,降低了产品的研发成本,为服装行业的数字化发展和企业的智能制造提供了新的思路和参考。

开裂混凝土中氯离子等效扩散系数计算方法

氯离子侵蚀对钢筋混凝土结构有严重的劣化作用,混凝土中的裂缝为氯离子的快速扩散提供了通道,进而加快了结构力学性能的失效速度。现有研究多集中于单一裂缝特征因素对氯离子扩散系数的影响,根据试验结果给出的拟合公式具有局限性。结合机器视觉技术提取和量化多个裂缝特征因素,构建考虑裂缝密度、裂缝内界面粗糙度和裂缝取向度叠加影响的代表性单元体积(REV)模型,并计算其等效扩散系数,将已有研究的试验结果与基于REV模型的计算结果进行对比。结果表明:不同盐冻条件下的平均相对误差为1.6%,完成28次盐冻后不同初始裂缝条件下的平均相对误差为2.9%,计算结果与试验结果较为吻合,表明提出的开裂混凝土中氯离子等效扩散系数具有较好的可靠性。

Electrocatalytic CO_(2) reduction to C_(2)H_(4): From lab to fab

The global concerns of energy crisis and climate change,primarily caused by carbon dioxide(CO_(2)),are of utmost importance.Recently,the electrocatalytic CO_(2) reduction reaction(CO_(2)RR) to high value-added multi-carbon(C_(2+)) products driven by renewable electricity has emerged as a highly promising solution to alleviate energy shortages and achieve carbon neutrality.Among these C_(2+) products,ethylene(C_(2)H_(4))holds particular importance in the petrochemical industry.Accordingly,this review aims to establish a connection between the fundamentals of electrocatalytic CO_(2) reduction reaction to ethylene(CO_(2)RRto-C_(2)H_(4)) in laboratory-scale research(lab) and its potential applications in industrial-level fabrication(fab).The review begins by summarizing the fundamental aspects,including the design strategies of high-performance Cu-based electrocatalysts and advanced electrolyzer devices.Subsequently,innovative and value-added techniques are presented to address the inherent challenges encountered during the implementations of CO_(2)RR-to-C_(2)H_(4) in industrial scenarios.Additionally,case studies of the technoeconomic analysis of the CO_(2)RR-to-C_(2)H_(4) process are discussed,taking into factors such as costeffectiveness,scalability,and market potential.The review concludes by outlining the perspectives and challenges associated with scaling up the CO_(2)RR-to-C_(2)H_(4) process.The insights presented in this review are expected to make a valuable contribution in advancing the CO_(2)RR-to-C_(2)H_(4) process from lab to fab.

复杂背景下退役圆柱锂电池轮廓精确提取与位姿检测方法

针对退役圆柱动力锂电池自动化拆解过程中存在的成像环境复杂、电池不规则形变和金属表面不均匀漫反射等复杂情形,现有视觉识别方法无法准确提取轮廓与位姿信息问题,提出基于弗雷歇距离相似函数的轮廓精确提取和基于矩形度与边缘形态特征的位姿检测方法。通过建立圆柱锂电池Lambert漫反射模型和运用形态学运算方法得到锂电池粗定位轮廓,并根据弗雷歇距离定义的相似度函数,对粗定位图像内各像素带归类完成轮廓精确提取。随后根据圆柱锂电池正负极端特征,通过自适应阈值分割算法提取正负极端ROI区域特征轮廓,最后对比两端区域矩形度数值计算出锂电池位姿信息。实验结果显示:在自建包含形变、腐蚀锈斑和光照不均情形下的退役圆柱锂电池图像数据集中,所提方法对不同型号和位姿下的锂电池识别均有较高精度,其直径长度检测误差小于3%,位姿检测正确率高于94%,能够满足实际自动化拆解检测需求。

机器学习辅助相场模拟预测锂离子输运参数对电池枝晶最大生长高度和空间利用率的影响

在锂基电池反复充放电的过程中,锂离子在负极表面的不均匀沉积会导致不可控的枝晶生长,进而严重影响电池的安全性能。相场模拟方法是描述和预测枝晶生长的有力手段,然而求解描述场变量演化的偏微分方程组对计算资源的要求较高。机器学习因能快速拟合历史数据中的潜在规律以实现材料性能的预测,已被广泛用于电池材料性能预测与筛选、电池健康状况评估等方面。本文以锂离子输运参数对电池枝晶形貌的影响为例,通过相场模拟收集不同锂离子扩散系数与离子电导率对应的枝晶图像,基于这些数据训练机器学习模型,进而预测给定离子输运参数所对应的枝晶描述因子(枝晶最大生长高度和空间利用率)。结果表明K-最邻近(K-nearest neighbors)模型可以较为精准地刻画离子输运参数与两种枝晶描述因子之间的联系(R^(2)为0.995和0.992),同时机器学习模型对锂离子输运参数与枝晶描述因子间构效关系的挖掘方式及枝晶描述因子的区间范围都会影响预测结果的准确性。本文能够有效降低计算成本,有助于指导高效地设计具有枝晶抑制性能的电池材料体系。

基于天气分型的水平面总辐射及散射辐射建模分析

为获得无太阳辐射观测地区的水平面总辐射及散射辐射量提出一种组合模型。首先通过日照百分率将天气情况分成3类,在此基础上,探究总云量、气溶胶等气象环境因子对水平面总辐射的影响,构建水平面总辐射线性模型;其次,考虑不同天气类型下气象环境因子的特征,建立基于高斯过程回归(GPR)的散射比和散射系数模型,进而获得散射辐射量;最后,得到每种天气类型下最优水平面总辐射模型与散射辐射模型构成的组合模型。结果表明,所提组合模型可有效提高水平面总辐射与散射辐射的预测精度。

手机用烧结钕铁硼材料的发展

本文主要分析了目前高端手机市场用烧结钕铁硼材料的发展状况,从摄像头音圈马达用烧结钕铁硼、振动马达用烧结钕铁硼、磁吸及TWS耳机用烧结钕铁硼三个主要方面对目前市场的应用进行了详细的分析和说明。并对烧结钕铁硼材料的需求性能、材料制备工艺、加工技术、电镀技术、自动充磁和检验技术的现状进行了详细的分析。并指出高性能材料制备,高精度加工方法,低磁衰电镀工艺及自动充磁检验一体化,是未来一段时期高端手机用烧结钕铁硼材料的发展方向。

异纤分拣机输棉通道结构对气流稳定性的影响

针对异纤分拣机输棉通道内部出现气流波动导致后续棉花异纤的检测与剔除效果不佳等问题,对异纤分拣机输棉通道结构与气流稳定性之间的关系进行了研究。利用流体仿真软件Fluent分别对原始输棉通道结构、不同弯管角度与通道入口段长度以及在通道入口增设不同类型面扩散器进行模拟分析,探讨输棉通道在不同方案改进下其内部气流速度分布、压力分布及直通道中心线位置的速度衰减曲线的规律。仿真结果表明:原始输棉通道由于弯管处产生流动方向突变和离心力,导致内外壁面存在压力差使得通道内部流速波动较大;减小弯管角度、增大入口段长度有利于减小压力不均衡分布区域,提升内部气流稳定性;在输棉通道入口增设局部阻力系数最小的双圆弧相切流线型面的缩扩型扩散器,不仅可以减小因型面突变产生的局部阻力,同时可将气流集中在管道中间位置,相较于原始输棉通道气流的波动距离缩短0.3 m。

乳腺癌精准诊疗的重要工具——MRI定量分析技术

乳腺癌严重威胁女性的生命健康和生活质量,MRI是乳腺疾病诊断中重要的工具。随着近年来软硬件技术的提高,越来越多的MRI定量特征被挖掘出来,并较形态判读等非定量特征展现出来更大的优势。本文简要论述了包括常规MRI序列、MRI新技术新方法、MRI影像组学和深度学习等MRI定量分析方法在乳腺病灶良恶性鉴别、新辅助治疗疗效和预后等临床问题中的应用,同时提出了亟待解决的几个问题。乳腺癌的精准诊疗时代对影像学研究提出了更高的要求,希望本文可以启发研究者未来深入挖掘MRI的定量特征和定量分析方法,结合非定量特征,更好地推动MRI在乳腺癌诊疗中的应用,推动临床转化,提升乳腺癌患者的生存时间和生活质量。

超塑成形工艺及设备浅析

超塑成形/扩散焊接(SPF/DB)技术是利用材料在超塑性状态下良好的固态粘合性能而发展起来的一种组合工艺技术。本文对超塑成形/扩散焊接(SPF/DB)成形工艺、热成形机和真空氩气成形系统进行了详细介绍分析。

薄壁小直径柱/板扩散焊界面超声信号特征分析与缺陷智能识别

为解决薄壁小直径柱/板扩散焊质量超声检测时波幅信号中缺陷与界面信号混叠,难以判断焊接接头是否存在微小缺陷的问题,采用基于粒子群优化的支持向量机技术(PSO-SVM),以不同界面类型的多特征参量为输入,对扩散焊界面进行缺陷识别。首先,使用水浸超声检测系统采集试样的C扫描数据,以金相试验得到的焊接截面为参照,运用快速傅里叶变换、经验模态分解等方法提取无缺陷、焊瘤、未焊合3种界面类型的时域、频域特征值;然后使用主成分分析法(PCA)对多特征参量进行融合得到融合特征值;最后输入到PSO-SVM模型中进行缺陷智能识别,并且与未经过多特征融合的预测结果进行对比分析。结果表明:经过PCA处理后,测试结果中3种类型界面的识别准确率为100%,比未经过PCA处理的测试结果准确率提高4.5%。

Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Flamelet Generated Manifold(FGM)is an example of a chemistry tabulation or a flamelet method that is under attention because of its accuracy and speed in predicting combustion characteristics.However,the main problem in applying the model is a large amount of memory required.One way to solve this problem is to apply machine learning(ML)to replace the stored tabulated data.Four different machine learning methods,including two Artificial Neural Networks(ANNs),a Random Forest(RF),and a Gradient Boosted Trees(GBT),are trained,validated,and compared in terms of various performance measures.The progress variable source term and transport properties are replaced with the ML models.Particular attention was paid to the progress variable source term due to its high gradient and wide range of its value in the control variables space.Data preprocessing is shown to play an essential role in improving the performance of the models.Two ensemble models,namely RF and GBT,exhibit high training efficiency and acceptable accuracy.On the other hand,the ANN models have lower training errors and take longer to train.The four models are then combined with a one-dimensional combustion code to simulate a counterflow non-premixed diffusion flame in engine-relevant conditions.The predictions of the ML-FGM models are compared with detailed chemical simulations and the original FGM model for key combustion properties and representative species profiles.