灰铸铁的化学成分与抗拉强度的关系

灰铸铁的化学成分与抗拉强度的关系杭州汽车发动机厂芜湖分厂邹崇新利用灰铸铁的化学成分与抗拉强度之间的关系，用共晶度ＳＣ和相对强度ＲＧ值为监控铸件的生产过程，以期达到稳定铸件生产和提高铸件质量的目的。灰铸铁的化学成分与抗拉强度存在着一定的关系，大致满足如...     

灰铸铁化学成分对抗拉强度综合影响的排序

研究灰铸铁化学成分对抗拉强度Rm的影响,应该侧重于参数排序的综合影响方面。采用灰色理论详细分析了灰铸铁化学成分对抗拉强度的影响及其排序,通过大量计算,得到了灰铸铁化学成分对抗拉强度的影响排序结果。研究结果表明:在这些化学元素中,碳当量CE对抗拉强度影响最大,锡Sn的含量影响最小,而C,Si/C,Si,Mn/S,Mn和Cu依次介于它们之间,研究结果为进一步研究灰铸铁抗拉强度和新性能灰铸铁提供了重要的依据。     

发动机轴瓦钢背用低Si冷轧带钢性能稳定性分析

介绍了过程能力指数Cpk的计算方法,低Si冷轧带钢的抗拉强度的Cpk为1.28,伸长率的Cpk为2.12,两者均高于过程能力指数下限1.0,Cpk的高低反映了一个钢种在某一段时期生产的性能稳定性,Cpk值越大,表明其性能越稳定。     

灰铸铁的化学成分与抗拉强度的关系

通过灰铸铁的化学成分与抗拉强度之间的关系，利用共晶度ＳＧ值和相对强度ＲＧ值来监控铸件的生产过程，有利于稳定铸件生产和提高铸件质量。     

高韧性球墨铸铁化学成分对抗拉强度的综合影响的排序

采用灰色理论详细分析了高韧性球墨铸铁化学成分对抗拉强度的影响.通过大量计算,得到了高韧性球墨铸铁化学成分对抗拉强度的影响排序结果.研究结果表明,在这些化学元素中,碳元素C含量对抗拉强度影响最大,镍的影响最小,而Mg、Si、RE、P、Mn、S依次介于它们之间.本文研究结果为进一步研究球墨铸铁抗拉强度和新性能球墨铸铁提供了重要的依据.     

模式识别—人工神经网络在改善汽车发动机缸体铸件力学性能方面的 …

利用模式识别技术对影响汽车发动机缸体铸件抗拉强度和硬度的铁水化学成分以及浇注工艺参数进行了分析给出了影响铸件抗拉强度和硬度的主要因素的优化范围,利用经已知样本集训练的人工神经网络对铸件抗拉强度和硬度进行了预报,预报结果与实测值符合较好。     

模式识别─人工神经网络在改善汽车发动机缸体铸件力学性能方面的应用

利用模式识别技术对影响汽车发动机缸体铸件抗拉强度和硬度的铁水化学成分以及浇注工艺参数进行了分析,给出了影响铸件抗拉强度和硬度的主要因素的优化范围．利用经已知样本集训练的人工神经网络对铸件抗拉强度和硬度进行了预报,预报结果与实测值符合较好．     

基于多变异分析方法检验常用的过程能力指数

介绍了多变异分析方法，进行了多变异方差分析，结合7种典型生产情况对比了经过MVA计算的过程能力指数与没有MVA计算的过程能力指数，对过程不合格品率也做出了准确的计算，分析了在过程能力指数尚可的情况下，相应的不合格品却高出很多的真正原因，阐明了在进行计算过程能力指数之前，进行多变异分析的重要性，对生产现场分析质量变异原因和质量控制具有重要意义。     

X90钢级管线钢制管前后性能变化研究

介绍了X90钢级管线钢制管前后屈服强度、抗拉强度、屈强比、冲击韧性的变化,分析了产生这些变化的原因,并给出X90钢级卷板的性能控制值。试验结果表明:制管后X90钢级管线钢的屈服强度和抗拉强度均上升,且抗拉强度平均升幅大于屈服强度;冲击韧性下降。在制管过程中,因卷板受形变强化的影响,造成制管后屈服强度和抗拉强度升高而韧性下降;而形变强化对制管前后强韧性的影响由卷板的化学成分及轧制工艺决定。     

35CrMo钢螺栓断裂原因分析

一批35CrMo钢螺栓中有一部分使用了2个月后断裂。为揭示其断裂的原因，对断裂的螺栓进行了宏观检查、化学成分分析、抗拉强度和硬度检测、金相检验及断口分析。结果表明：螺栓有极不均匀的脱碳层，且抗拉强度低于要求值，导致使用中螺栓因螺纹根部形成细微的应力裂纹并逐步扩展而断裂。     

Effects of chemical composition on the microstructure and mechanical properties of FCAW-S weld metal containing 2% Ni

In this study the effects of alloying elements on the microstructure and mechanical properties of 600MPa grade FCAW-S weld metals containing 2% Ni were examined. Carbon, manganese and aluminum contents were varied in the ranges of 0.075%–0.101%, 1.19%–1.69%, and 0.66%–1.49% respectively. Regardless of the Al content, all of the weld metals showed a bainite dominant microstructure with no δ-ferrite. This indicates that when a weld metal contains 2% Ni, the Al content can be increased up to around 1.5% without concern about the deterioration of impact toughness due to the presence of δ-ferrite. The tensile strength of the weld metals varied from 595 MPa to 702 MPa dependent upon the chemical composition. Multiple regression analysis showed that while C and Mn have strong influences on the tensile strength, Al has little influence. Therefore, the Pcm index of weld metals could be used as an indication of their tensile strength. Impact toughness of the weld metals was influenced most by tensile strength and showed that the 50J transition temperature increased by 36 °C when the tensile strength was increased by around 100 MPa. Therefore, an excessive increase of the tensile strength should be avoided to attain higher impact toughness. Even when inclusion mean diameters were increased from 0.588 μm to 0.708 μm with an increase of the Al content from 0.66% to 1.49%, the size difference showed little influence on the impact toughness of the weld metals in this experiment.     

钢铁材料抗拉强度与硬度关系综述

讨论了钢铁材料的抗拉强度和硬度之间的关系.研究表明:钢铁材料的抗拉强度和硬度之间基本呈正相关关系,也存在线性关系.受化学成分和制造工艺(铸造、锻造、轧制、热处理)不同的影响,钢铁材料抗拉强度和硬度之间的换算关系存在着较大的分散性;但是,在一定的生产条件下,用硬度来监控产品质量是一种很好的选择.     

应用回归和神经网络方法预测热轧带钢性能

针对Q235B热轧带钢性能预测系统，提出一种回归分析和神经网络相结合的方法来预测其力学性能。首先，测量材料最终相的组成与铁素体的晶粒度，应用多重回归分析的方法，建立成分、相体积分数、晶粒尺寸与抗拉强度、屈服强度、延伸率的对应关系，另一方面，采用BP神经网络方法，结合相变动力学模型的计算数据，通过大量数据的自学习训练，完成神经网络模型对抗拉强度、屈服强度、延伸率的预测，预测结果表明，应用神经网络和回归分析方法，具有较高的预测精度。     

Evaluation of the effects of variations in chemical composition on the quality of Al-Si-Mg,Al-Cu,and Al-Zn-Mg cast aluminum alloys

The effect of slight variations in chemical composition on the quality of cast aluminum alloys from three different major alloy systems was evaluated. For the evaluation of the alloy quality, an index Q _D adjusted to damage tolerance requirements that are currently involved for the design of advanced lightweight structures is used. The quality index Q _D accounts for tensile strength and ductility as well as for material failure through yielding or fracture. For this investigation, experimental results obtained for variations in chemical composition of the alloy systems Al-Si-Mg, Al-Cu, and Al-Zn-Mg were exploited. In total, castings from 37 different batches from 10 aluminum alloys, varying in chemical composition, were evaluated. Quality characterization and alloy quality ranking were made by evaluating results of 512 tensile tests using the index Q _D as well as, for comparison, the quality index Q, which is currently used by the industry. The results obtained involving the index Q _D seem to be more realistic, from the viewpoint of damage tolerance design requirements.     

Ti8LC合金热处理力学性能的回归分析

采用正交试验设计了Ti8LC合金的热处理制度,并测试了不同热处理制度下Ti8LC合金的室温抗拉强度,利用多元回归模型对Ti8LC合金的热处理制度与抗拉强度进行了回归分析,建立了热处理制度与抗拉强度之间的回归方程,通过方差分析验证了该回归方程具有较高的可信度。同时分析了Ti8LC合金抗拉强度与合金相的体积分数及晶粒尺寸的关系,得到了合金室温抗拉强度与合金相的体积分数及晶粒尺寸近似呈线性关系,并从微观组织结构分析了合金相的体积分数、晶粒尺寸与热处理温度、时间之间的关系。     

FTSR产线低屈强比高强钢的开发实践

针对市场对500 MPa以上、屈强比低于0.9的冷加工成形用热轧带钢的需求,唐钢在FTSR产线上对其进行了开发。对其化学成分、加热制度、轧制制度、卷取工艺进行了优化设计,形成了一整套具有FTSR产线特色的低屈强比薄规格高强钢的生产工艺。生产实践表明,采用碳锰钢+Nb微合金化化学成分设计和合理的TMCP工艺,带钢组织为典型的铁素体组织,抗拉强度不小于500 MPa、屈强比不大于0.9,提升了Nb微合金化高强薄规格带钢性能和轧制的稳定性,并解决了卷取后易扁卷的问题。     

铁素体基体球铁力学性能与化学成分的关系分析

为有效控制铁液成分,利用趋势图分析以及一元线性回归分析铁素体基体球铁的生产数据,得出了抗拉强度Rm、布氏硬度HB、伸长率A以及w(Mg残)量之间的一些定性和定量关系,对指导有效生产具有一定的价值。     

Comparison of Some Neural Network and Multivariate Regression for Predicting Mechanical Properties of Investment Casting

Investment casting enables producing complex shapes with good accuracy and surface finish. A key goal for investment castings used in automobile, aerospace, chemical, biomedical and other critical applications is to be free of internal defects and to possess mechanical properties within the desired range. At present, casting quality is ascertained by destructive testing at the end of production cycle, leading to the possibility of scrapping the entire batch. In this work, the mechanical properties of investment castings have been predicted based on process parameters and chemical composition, by employing artificial neural network (ANN) and multivariate regression (MVR). The data of related process parameters (wax making, shell making, dewaxing, melting etc.), chemical composition of the alloy, and the resulting mechanical properties (ultimate tensile strength, yield strength, and percentage elongation) for 800 heats were collected in an industrial investment casting foundry. Three different ANN models: back propagation, momentum and adaptive, and Levenberg-Marquardt, with varying number of neurons in the hidden layer (from 20 to 45 in steps of 5) were trained using a portion of the data and tested with remaining data. A prediction penalty index (PPI) was developed to compare the relative predictive capability of various neural network and MVR models. It is observed that both ANN and MVR could predict the mechanical properties well, though MVR gave slightly better results. For the ANN model, better results were produced when the number of neurons in the hidden layer was equal or slightly higher than the number of input parameters.     

微合金化磁极钢板化学成分与磁性能的关系分析

采用多元线性回归分析方法分析微合金化磁极钢板化学成分与磁感应强度K、B100的关系，并用剩余碳含量的概念，得出显著性较高的磁极钢板磁性能与硫及剩余碳含量关系的回归式。