DEVELOPMENT OF Sr_（0．69）La_（0．31）F_（2．31）＋SrSSOLID ELECTROLYTE SENSOR AND ITS APPLICATION

ＤＥＶＥＬＯＰＭＥＮＴＯＦＳｒ_（０．６９）Ｌａ_（０．３１）Ｆ_（２．３１）＋ＳｒＳＳＯＬＩＤＥＬＥＣＴＲＯＬＹＴＥＳＥＮＳＯＲＡＮＤＩＴＳＡＰＰＬＩＣＡＴＩＯＮＺｈａｉ；ＹｕｃｈｕｎＭａ；ＰｉｎｇＴｉａｎ；ＹａｎｗｅｎＸｉａｏ；ＦｅｉＪｉ；Ｃｈｕｎ?..     

一种改进的BP神经网络在故障诊断中的应用研究

人工神经网络作为一个具有高度非线性映射能力的计算模型,在工程中具有广泛的应用前景.它不需要预选确定样本的数学模型,仅通过学习样本数据即可以进行故障诊断.本文结合计算机综合业务的一些实际问题,探讨了利用改进的BP神经网络进行故障诊断的方法和应该注意的问题,在分析神经网络的基础上提出了基于改进的BP模型神经网络的故障诊断推理方法.结果表明,基于BP神经网络的故障诊断方法是行之有效的.     

熔滴过渡小型光谱传感器

光谱信息是目前所发现的反映焊接熔滴过渡的最佳信息源，但是基于光谱仪的熔滴过渡信号检测系统不能用于在线实时控制。基于光谱信息检测思想，开发了小型实用的光谱传感器。章详细介绍了该熔滴过渡小型光谱传感器的设计思想和具体设计结构，并进行了理论探索，给出了小型光谱传感器的实际结构和在线应用情况。     

微小电偶腐蚀电流的准确测定

为了更多地获得电偶腐蚀的信息并揭示其本质规律，提出了电偶腐蚀试验实时监测模型。通过选用不同设备对试样的电偶腐蚀微小电流的测量，结果发现：选用现成设备测量试样的电偶腐蚀微小电流时，测量电流波动非常大，噪声明显超过实际信号输入——电偶腐蚀电流。经自行开发研制的MCD01电偶腐蚀计可以准确的测量腐蚀电流，特别是微小电偶腐蚀电流。     

工业链条实验台转速转矩检测

介绍一种用于工业链条实验台的动态转速转矩检测装置及其检测原理和方法。该装置采用单片机、动态转速转矩传感器及其信号检测电路等组成，具有结构简单，抗干扰能力强，检测数据准确可靠，成本低等特点。     

提高超声波探伤轧辊裂纹深度的准确度的方法

运用正交试验法，找出了超声波探伤轧辊裂纹深度最佳参数组合，提高了超声波探伤的准确度。     

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.     

Prediction of jominy hardness profiles of steels using artificial neural networks

Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.     

氮势控制技术及其应用

简要介绍了渗氮机理及氮势控制技术，并在GN70／120型井式渗氮炉内进行了可控渗氮试验。结果表明，采用HydroNit氢探头能精确测量和控制炉内氮势，有效降低渗层氮浓度，减少表面白视层厚度，满足高质量渗氮要求。     

变切深加工过程中的信息熵控制

不确定性信息的处理是目前制造业的关键和难点,"信息熵"的应用为解决此类问题提供一种新途径,但目前国内用于实际并不多.文章以变切削深度加工过程为例,用神经网络控制,发现系统响应速度慢;改用基于信息熵的目标函数,其中选用均匀分布函数作为不确定系统的输出分布概率函数,发现响应速度提高,但震荡次数增多;后用最大熵原理来取得概率函数,取得很好的控制效果.这为熵的优化理论提供实践证明.