Powder mixed electrochemical discharge process for micro machining of C103 niobium alloy

This work demonstrates the viability of the powder-mixed micro-electrochemical discharge machining(PMECDM) process to fabricate micro-holes on C103 niobium-based alloy for high temperature applications.Three processes are involved simultaneously i.e.spark erosion,chemical etching,and abrasive grinding for removal of material while the classical electrochemical discharge machining process involves double actions i.e.spark erosion,and chemical etching.The powder-mixed electrolyte process resulted in rapid material removal along with a better surface finish as compared to the classical microelectrochemical discharge machining(MECDM).Further,the results are optimized through a multiobjective optimization approach and study of the surface topography of the hole wall surface obtained at optimized parameters.In the selected range of experimental parameters,PMECDM shows a higher material removal rate(MRR) and lower surface roughness(R_(a))(MRR:2.8 mg/min and R_(a) of 0.61 μm) as compared to the MECDM process(MRR:2.01 mg/min and corresponding Raof 1.11 μm).A detailed analysis of the results is presented in this paper.

Machine-learning-assisted prediction of the mechanical properties of Cu–Al alloy

The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties.Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors.The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel(SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu–Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu–12Al–6Ni alloy with a tensile strength(390 MPa) and hardness(HB 139) that reached the expected values was developed.

Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laser powder bed fusion via a stacking model

Determining appropriate process parameters in large-scale laser powder bed fusion(LPBF)additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experimentation.This work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first time.This method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base models.Specifically,the stacking model utilized artificial neural network(ANN),gradient boosting regressor,kernel ridge regression,and elastic net as base models,with the Lasso model serving as the meta-model.Bayesian optimization and cross-validation were utilized for model optimization and training based on a limited data set,resulting in higher predictive accuracy compared to traditional artificial neural network model.The statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set,with a coefficient of determination value of 0.944,mean absolute percentage error of 2.51%,and root mean squared error of 27.64,surpassing that of the ANN model.All statistical metrics demonstrate superiority over those obtained from the ANN model.These results confirm that by integrating the base models,the stacking model exhibits superior predictive stability compared to individual base models alone,thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process.

Surface Modification Process by Electrical Discharge Machining with Ti Powder Green Compact Electrode

This paper describes a new method of surface modification by Electrical Discharge Machining (EDM). By using ordinary EDM machine tool and kerosene fluid, a hard ceramic layer can be created on the workpiece surface with Ti or other compressed powder electrode in a certain condition. This new revolutionary method is called Electrical Discharge Coating (EDC). The process of EDC begins with electrode wear during EDM,then a kind of hard carbide is created through the thermal and chemical reaction between the worn electrode material and the carbon particle decomposed from kerosene fluid under high temperature. The carbide is piled up on a workpiece quickly and becomes a hard layer of ceramic about 20 μm in several minutes. This paper studies the principle and process of EDC systemically by using Ti powder green compact electrode. In order to obtain a layer of compact ceramic film, it is very important to select proper electric pulse parameters, such as pulse width, pulse interval, peak current. Meantime, the electrode materials and its forming mode will effect the machining surface quality greatly. This paper presents a series of experiment results to study the EDC process by adopt different technology parameters. Experiments and analyses show that a compact TiC ceramic layer can be created on the surface of metal workpiece. The hardness of ceramic layer is more 3 times higher than the base body, and the hardness changes gradiently from surface to base body. The method will have a great future because many materials can be easily added to the electrode and then be coated on the workpiece surface. Gearing the parameters ceramic can be created with different thickness. The switch between deposition and removal process is carried out easily by changing the polarity, thus the gear to the thickness and shape of the composite ceramic layer is carried out easily. This kind of composite ceramic layer will be used to deal with the surface of the cutting tools or molds possibly, in order to lengthen their life. It also can be found wide application in the fields of surface repairing and strengthening of the ship or aircraft.

Application Research on Powder Mixed EDM in Rough Machining

Powder Mixed Electric Discharge Machining (PMEDM) has different mechanism from conventional EDM, which can improve the surface roughness and surface quality distinctly and to obtain nearly mirror surface effects. It is a useful finish machining method and is researched and applied by many countries. However there are little research on rough machining of PMEDM. Experiments show that PMEDM machining makes discharge breakdown easier, enlarges the discharge gaps and widens discharge passage, and at last forms even distributed and "large and shadow" shaped etched cavities. Because of much loss of discharge energy in the discharge gaps and reduction of ejecting force on the melted material, the machining efficiency gets lower and the surface roughness gets small in PMEDM machining in comparison with conventional EDM machining. This paper performs experimental research on the machining efficiency and surface roughness of PMEDM in rough machining. The machining efficiency of PMEDM can be highly increased by selecting proper discharge parameters (increasing peak current, reducing pulse width) with approximate surface roughness in comparison with conventional EDM machining. Although PMEDM can improve machining efficiency in rough efficiency, but a series of problems like electrode wear, efficiently separation of machined scraps from the powder mixed working fluid, should be solved before PMEDM machining is really applied in rough machining. Experiments result shows that powder mixed EDM machining can obviously improve machining efficiency at the same surface roughness by selecting proper discharging parameters, and can provide reference accordingly for the application of PMEDM machining technology in rough machining.

利用自动粉剂分包机提高儿童药物分剂量的准确度

目的:总结自动粉剂分包机的使用经验,探讨其应用价值。方法:选取3种常需分剂量的药品分别进行人工和机器(自动粉剂分包机)分剂量至1/5、1/8、1/10各3批,每批各取30包精密称量并记录,计算并比较各批的相对标准偏差(RSD)与合格率。结果:粉剂分包机分剂量合格率高于人工分剂量(P<0.05)。其中,分包总量>3 g的1/5粒熊去氧胆酸胶囊的分包机分剂量RSD低至3.26%~4.21%,合格率达到100%。结论:自动粉剂分包机分剂量大大提高了分剂量的准确度,用好自动粉末分包机对推动儿童精准分剂量调剂工作的发展至关重要。

粉剂固定式灌装机机械结构设计

通过对江、浙部分软包装企业的调研发现,便携尿袋里面的吸水防臭粉目前均由人工灌装,灌装次品率高、生产效率低。针对这一现象,提出了一种粉剂固定式自动灌装机。首先确定了灌装机技术方案,接着分别对进给组件、移动组件、尿袋撑口组件、料斗装料组件进行了机械结构设计,并且研制了粉末材料自动灌装机的样机。样机实际使用表明,该设备运行性能稳定,可显著提高生产效率,降低人工成本,提升企业经济效益。

粉末饮片自动包装机的研究与设计

目前市场上针对粉末饮片、颗粒等物料包装时,存在难以有效剔除不合格原料、存有破碎的细小残渣和产品结块受潮等现象,严重影响产品的包装质量。团队结合多年实践经验,设计了一种粉末饮片自动包装机,集上料机构、送料机构、送风机构、收集组件及驱动机构等于一体,可有效解决以上痛点问题。具体针对该装置的工作原理和各个组成部分的结构设计进行了介绍。实践证明,该装置在工作时,各个组成部件相互配合,能够有效剔除不合格的原料、避免原料结块受潮,具有结构简单、成本低、效率高等优点。

生料辊压机终粉磨系统节能减碳实践中存在的问题与对策

因原材料水分大、系统烘干能力不足、设备阻力大、高效选粉机结构缺陷等原因造成生料辊压机终粉磨系统运行不稳定且循环负荷大、停机次数较多、系统阻力大、循环风机拉风过大、细度跑粗现象,导致生料工段电耗偏高、成品细度合格率低。对物料特性、工艺流程、设备选型及运行、成品细度进行热工诊断和研究分析,并实施相应的设备、工艺及系统操作优化措施。优化后,生料辊压机终粉磨系统工段电耗降低了2.2kWh/t,选粉效率提升,成品细度明显改善,实现提质增效,节能减碳的目的。

羟基磷灰石混粉电火花铣削加工316L的效果研究

316L不锈钢是一种制作人体植入物的重要材料。经过调研提出一种羟基磷灰石混粉电火花铣削加工方式,通过因素和水平的变化探究羟基磷灰石混粉电火花铣削加工与混粉电火花成形加工两种加工工艺蚀除316L不锈钢的趋势与不同。试验结果发现,混粉电火花铣削加工能够提高材料去除率,降低电极损耗率、表面粗糙度和表面接触角。证明了该加工方式具有研究意义,并通过单因素试验进一步探究该加工方式正负极性下所呈现的不同加工特性及形成原因。极性试验结果显示,正极性加工能够实现更高的材料去除率,负极性加工则呈现更低的电极损耗率、表面粗糙度和表面接触角。

镍基粉末高温合金的室温和高温力学性能:机器学习建模与实验验证

本文采用机器学习方法实现了镍基粉末高温合金在不同温度下的力学性能精准预测。首先,通过文献评估构建了一个包含166条数据的镍基粉末高温合金数据集,经数据清洗和特征分析后,从合金成分、热处理工艺和测试温度出发,建立了预测镍基粉末高温合金不同温度下的屈服强度与延伸率的高精度预测模型。其次,利用构建的模型预测了未在训练数据集中使用的新型镍基粉末高温合金FGH4113A在不同热处理工艺和测试温度下的力学性能,并进行了实验验证。结果表明,所构建的模型对FGH4113A合金的室温屈服强度预测的平均绝对误差(Mean absolute error,MAE)为31.49 MPa,室温延伸率的MAE为2.45%,而高温屈服强度预测的MAE为76.14 MPa,高温延伸率的MAE为2.06%,表明模型具有良好的泛化能力。最后,通过分析模型的可解释性,提出在一定范围内提高二级时效温度并降低一级时效温度可以同时提高镍基粉末高温合金的屈服强度和延伸率,这为合金的热处理工艺设计提供了新思路。

机制砂替代河砂对高性能混凝土性能的影响研究

为探究机制砂取代天然河砂作为细骨料的混凝土性能特点,文中从抗氯离子渗透性能、干燥收缩性能、强度性能和工作性能等方面,开展机制砂替代河砂对高性能混凝土性能的影响研究。结果表明,采用机制砂作为细骨料的混凝土在抗氯离子渗透性能、抗压强度和工作性能等方面与河砂混凝土之间没有明显差异;机制砂混凝土中掺加5%石粉后,低标号混凝土在抗氯离子渗透性能、抗压强度和和易性皆有所提升;应用机制砂有利于改善混凝土稳定性并减少干燥收缩。

蛋壳粉泡沫混凝土性能分析及预测

为了探究蛋壳粉部分替代水泥制备绿色低碳型泡沫混凝土的可行性,提出一种预测蛋壳粉泡沫混凝土性能的方法。首先通过室内试验测试,分析蛋壳粉替代率对泡沫混凝土吸水率、力学强度以及孔隙结构的影响,然后基于试验数据采用机器学习的方法建立模型,预测蛋壳粉泡沫混凝土的性能。研究结果表明:掺入蛋壳粉会降低泡沫混凝土试样的初始表面吸收率、吸水率;掺入蛋壳粉有助于提升泡沫混凝土的抗压、抗折强度,蛋壳粉替代率为15%的泡沫混凝土强度最高;泡沫体积的增大使孔隙尺寸扩大,大孔隙体积占比增加;蛋壳粉填充效果较好,掺加15%蛋壳粉致使泡沫混凝土中小孔隙体积占比增大而大孔隙体积占比减小;所建立的机器学习模型可有效预测蛋壳粉泡沫混凝土的性能;与决策树模型相比,随机森林模型预测结果的精确度更高。

机制砂高质量加工与混凝土应用研究

机制砂具有料源稳定、氯离子含量低等特点,在混凝土中应用较为广泛。依托某公路工程,开展粉砂岩机制砂高质量加工与应用研究。重点介绍了粉砂岩机制砂高质量加工技术,分析了石粉含量对高性能混凝土工作性能的影响,提出了“六区两室一池”的机制砂场区布局以及人工与机械相结合的三级分拣技术。

工业硅粉磁性物质组成特征分析及去除

采用ICP及XRF等方法分析了本公司工业硅粉中磁性物质的成分。研究结果表明:工业硅粉磁性物质中主要构成为铁、铝、钙3种元素,其中铁元素含量最高。据此在冷氢化下料口增设磁选机设备可减少75%左右的磁性物质,有效提升了工业硅粉的使用效率,保障了生产工艺安全平稳运行。

机制砂细粉含量对混凝土力学性能影响研究

设置5种不同机制砂细粉含量,在相同配合比条件下,研究混凝土强度及弹性模量受机制砂细粉含量影响规律。研究表明,机制砂细粉含量对机制砂混凝土强度和弹性模量均存在显著影响,且弹性模量对细粉含量的响应更为显著。随着机制砂细粉含量的增加,混凝土抗压强度和劈裂抗拉强度均呈先升后降的变化趋势,混凝土弹性模量则随细粉含量的增加呈持续降低趋势。细粉含量为5%时,抗压强度增幅4.91%,劈裂抗拉强度增幅7.91%,均达到最大值。细粉含量为20%时,混凝土弹性模量降幅高达14.7%。基于细粉含量对混凝土物理力学特性影响规律,机制砂细粉含量为5%时混凝土强度增益效果较佳。

基于网版铺粉及激光打标平台的金属锡3D打印研究

金属3D打印技术是目前增材制造研究的重点和热点。研究使用YLP-DF30激光打标机平台进行金属锡的立体打印,并利用合适网目数的网版完成铺粉工序,通过对3D打印各工艺参数的优化,得到了良好成型的金属锡3D打印成品,并利用显微镜分析观察了打印结果。结果表明,激光打标设备功率为25W,速度为5.00mm/s时打印出来的成品呈现出较好光泽,并且具有良好的致密程度及成型。

大型砂石骨料加工系统机制砂最优石粉含量试验研究

砂石骨料加工系统在生产机制砂时会产生石粉,石粉可有效调整胶凝材料的级配,补充水泥中不足的粒径为45~75μm的颗粒。但机制砂石粉含量的增加也会使混凝土的收缩增大,不利于混凝土塑性开裂的控制,从而影响混凝土质量。通过对砂石加工系统破碎、筛分设备选型和整套生产工艺的研究改进,确定使混凝土性能最优的石粉含量,为混凝土骨料生产提供技术依据,保证工程质量。

粉对再生机制砂混凝土收缩和强度性能的影响研究

废旧混凝土再生机制砂制备混凝土具有显著的经济、环保效益。本文从强度和收缩性能方面研究了石粉含量对废旧混凝土再生机制砂混凝土性能影响。结果表明:石粉含量<9%对混凝土工作性能有改善作用,并提高了混凝土抗压强度,9%石粉掺量提高混凝土抗压强度约10%。适量的石粉会降低混凝土早期自收缩,但3 d混凝土的自收缩与石粉含量之间没有明显的关联。石粉含量<6%时可抑制再生机制砂混凝土干燥收缩,研究结论对于废旧混凝土再生机制砂混凝土应用提供了借鉴。

高石粉含量对不同砂体系混凝土性能的影响研究

机制砂中较高的石粉含量会影响新拌混凝土的工作性能以及硬化混凝土的力学性能和耐久性能。本文研究不同砂体系中高石粉含量对不同强度等级混凝土和易性、含气量、抗压强度、碳化深度、抗渗以及收缩性能的影响。结果表明:当天然砂和机制砂混合比例为7∶3时,混合砂的孔隙率最低,为37%;高石粉含量显著增加了高强度等级混凝土的粘度,不利于混凝土的泵送浇筑;通过破碎得到的机制砂,颗粒边缘碎化,相对于天然砂体系混凝土的气泡保有性能差别较大,含气量经时损失较大;混合砂体系混凝土早期抗压强度优于机制砂体系,与天然砂体系相差不大,且混合砂体系混凝土的后期抗压强度较大,这也表明石粉能更好地填充至体系中,填充效应显著;高石粉含量增加了混凝土碳化的发生概率,机制砂体系混凝土不同龄期的碳化深度最大,混合砂体系混凝土的碳化深度与天然砂体系基本保持一致;混合砂体系混凝土的渗水高度最小,其抗渗性能最优;混合砂体系混凝土的早期收缩低于机制砂体系,与天然砂体系保持一致。