Development of a new cluster index for wafer defects

Defect number and defect clustering are two key determinants of wafer yield. Preventing and detecting wafer defects thus is an important issue in integrated circuit manufacturing. Defect clustering tends to grow with increasing wafer size. Methods have been developed for assessing defect clustering on wafers. However, these methods require either statistical assumptions regarding defect distribution or complex computations. This study develops a new cluster index, utilizing the rotating axis technique from multivariate analysis to accurately quantify defect clusters on a wafer. The developed defect-clustering index does not require making assumptions regarding defect distribution. Thus, the proposed method can be efficiently used by engineers with little statistical background. A simulation experiment is conducted to demonstrate the effectiveness of the proposed defect-clustering index.     

3轴联动数控焊接机设计研究

对圆管和圆管相贯线焊缝的数控焊接方法、工艺和专用设备进行有针对性的研究,力图提高劳动生产率,减少人为因素的影响,提高散热器焊接质量。     

全自动钉排涂胶机的设计

主要介绍用于建筑装璜业的钉排加工的自动涂胶机的结构设计。该涂胶机可以完成自动上料、双面刷胶、自动输送、切断的全部功能,是一个自动化程度较高的生产设备。该设备的应用可以提高生产效率、提高产品质量,减少污染,对于该行业生产水平的提高有很大的实际意义,也填补了该技术领域的缺欠。     

一种SMT生产缺陷自动检测系统的设计与实现

为了控制PCB成品质量,设计和实现了对SMT生产缺陷的自动检测。采用定位、摄像和图像处理等技术,精确检测和标识PCB产品的缺陷。系统运行可靠,降低了SMT生产成本。     

已建建筑物地基础缺陷的处理

论述已建建筑物地基基础缺陷的主要形式及地基基础的正确使用方法,提出了地基基础缺陷的处理措施。     

灯丝预处理仪的研制

报道团体同位素质谱计离子源灯丝的烧带处理仪的研制，该仪器一次可处理２０条灯丝，工作稳定可靠，性能良好，成本和价格远低于进口同类型仪器。     

大气碳黑气溶胶检测仪的研制

根据大气中碳黑气溶胶在可见光及近红外波段具有较强的吸光特性,研制了可检 测碳黑气溶胶的专用仪器.本文介绍了该仪器的检测原理、硬件系统设计和软件系统设计.仪器采用单片机控制信号的采集、处理和显示,能实时快捷地检测大气中碳黑气溶胶的浓度.对比实验表明,该仪器设计合理,可满足碳黑气溶胶在线检测的要求.     

Application of an envelope technique in the detection of ball bearing defects in a laboratory experiment

To judge the diagnostic capabilities of an envelope technique, based on enveloping the resonance frequency of a transducer,

1 Bearcon Signature™, Carl Schenck AG, Germany

artificially damaged ball bearings (SKF 6207) were run under oil lubrication at different radial loads and speeds in a laboratory experiment. Damage to the outer race, inner race and ball of different magnitudes was introduced by spark erosion and the response of the measurement system was analysed. The results showed that vibration spectrum components of outer race defects showed up in the envelope spectra of undamaged bearings, and that the detectability of defects was primarily limited by the speed of the bearing; on occasion the automatic scaling facility of the analyser was a limiting factor. Inner race and ball defects showed the well-known effects of load modulation in the defect contact zone, which can become dominant in the envelope spectra at high loads and speeds. This phenomenon could be confirmed by computer simulation. The detection of ball damage was hampered by the fact that overrolling of the defect in purely radially loaded bearings takes place incidentally. This drawback could be mitigated by applying a trigger technique to the envelope signal.     

Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

The purpose of this study is to develop an automated visual inspection system for analysis of the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). Known image patterns of the six types of ring varistors are used in a training process to establish Sugeno FIS rules, and the input-output data are then set to train the ANFIS to tune the membership function. Feature extraction reduces image complexity using two-dimensional edge detection, calculated within divided rectangular region. The ANFIS combines the neural network adaptive capabilities and fuzzy logic qualitative to train a classification system for six different types of components. The performance of the ANFIS is evaluated in terms of training performance and classification accuracy. The results confirm that the proposed ANFIS is capable of classifying the six types of ring varistors with an accuracy of 98.67%. This paper has not been published elsewhere nor has it been submitted for publication elsewhere.     

Development of an automatic process control system for the Severomuiskii tunnel

Implementation of an automatic process control system for the Severomuiskii tunnel is presented. Its subsystems are considered, which can be used for design of other similar control systems.     

Development of an international guide for measurement software

The aim of this paper is to outline the purpose, the rationale, and the need for a new international guide for measurement software. The method of constructing the guide including the risk-based approach and the progress is described. The work is being done with the collaboration of the National Measurement Institutes: Physikalisch-Technische Bundesanstalt (PTB) and National Physical Laboratory (NPL).     

一种气动挤压装置的研制

介绍了一种气动挤压装置的研制过程。该装置是一种根据气压原理，通过多工位夹紧挤压，实现零件产生塑性变形，满足产品加工需要的，且能够实现手动、半自动、自动操作的机、电、气一体化设备。     

卷烟周长测量仪的研制

介绍的卷烟周长测量仪以光学投影的原理测量卷烟的直径,它以线阵CCD为光电传感器,以MCS51系列单片机为核心组成控制系统。文中详细介绍了测试系统的构成,线阵CCD驱动电路,对CCD信号的采集、处理电路,并对测量误差进行了详细分析。本仪器所采用的原理和电路对柔软物体的非接触性测量有参考意义。     

Development of an intelligent process model for EDM

This paper reports the development of an intelligent model for the electric discharge machining (EDM) process using finite-element method (FEM) and artificial neural network (ANN). A two-dimensional axisymmetric thermal (FEM) model of single-spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time- and energy-dependent spark radius, etc. to predict the shape of crater cavity, material removal rate, and tool wear rate. The model is validated using the reported analytical and experimental results. A neural-network-based process model is proposed to establish relation between input process conditions (discharge power, spark on time, and duty factor) and the process responses (crater geometry, material removal rate, and tool wear rate) for various work—tool work materials. The ANN model was trained, tested, and tuned using the data generated from the numerical (FEM) simulations. The ANN model was found to accurately predict EDM process responses for chosen process conditions. It can be used for the selection of optimum process conditions for EDM process.     

创新——企业发展的动力和源泉

创新是一个民族的灵魂,是一个国家兴旺发达的不竭动力。对于企业来说,创新意识同样具有举足轻重的作用。只有积极创新,才能不断进步,从而在激烈的市场竞争中占得先机。因此,如何深刻地认识创新,成为企业必须解决的一个重要问题。     

基于虚拟装配技术的射钉枪设计

研究了在Pro/E环境下,自顶向下虚拟装配技术,并以射钉枪虚拟设计为例介绍了Pro/E中自顶向下装配设计的具体方法.不仅实现了射钉枪的三维实体建模,而且实现了射钉枪虚拟装配及运动仿真,有利于射钉枪快速设计与优化,提高工作效率,可以更加直接、准确地达到设计者的意图,对新产品开发可以大大缩短设计周期.     

Development of an authoring framework for the simplified customization of PDM systems

Given that the amount of product data in firms is explosively increasing, a PDM system for effective data management is considered indispensable for product development. However, considerable time and specialized human resources are needed to customize a generic PDM system for satisfying the specific requirements of individual firms. To overcome this problem, we propose the use of UML object models in a PDM authoring framework. A PDM authoring framework, which provides authoring functions for the effective customization of PDM systems, will reduce the need for the intervention of PDM specialists in the design of the object models of the PDM system. We describe how a PDM authoring framework may be designed by using UML object models, and show how model-oriented application development (MOAD), in conjunction with the PDM authoring framework, can be used to build object models into a PDM system. Furthermore, we confirm the value of the framework by evaluating its performance under several conditions. This paper was recommended for publication in revised form by Associate Editor Dae-Eun Kim Inho Song is a postdoctoral associate in the Department of Mechanical Engineering, Carnegie Mellon University, USA. He received the Ph.D. degree in Mechanical Engineering from Hanyang University, Seoul, Korea in 2007. From 2002 to 2007, he served as a CAx team leader of the INOPS Company (CIES R&D Center), Seoul, Korea. He has developed the sketch-based CAD system for an automotive company. His research interests include collaborative design, sketch-based CAD, geometry translation, geometry compression, product data exchange, PDM/PLM, digital manufacturing, and virtual reality. Jeongsam Yang is an assistant professor in the Department of Industrial & Information Systems Engineering and is leading the CAD laboratory() at Ajou University. He worked at Clausthal University of Technology (Germany) as a visiting researcher and the University of Wisconsin-Madison (USA) as a postdoctoral associate. He obtained his Ph.D. in mechanical engineering in 2004 at KAIST. His current research interests are product data quality (PDQ), VR application in product design, product data management (PDM), knowledge-based design system, and STEP. Peom Park is a professor in the Department of Industrial & Information Systems Engineering and is leading the Human Technology Research Center and Human Factors/HCI laboratory () at Ajou University. He worked on HCI and Telecommunication system at ETRI as a senior researcher. He obtained his Ph.D. in Industrial and Manufacturing Systems Engineering at Iowa State University on 1992. His current research interests are uT applications, u-Healthcare/Telemedicine, Telematics and Ergonomic/Safety Design.     

Development of an acoustoelectric method for determining the porosity of dielectric materials

The regularities of acoustoelectric transformations in cement stones with different water-to-cement ratios are studied. A physically substantiated acoustoelectric method for determining the porosity of dielectric materials is proposed.     

Using neural networks for the diagnosis of localized defects in ball bearings

Two neural network based approaches, a multilayered feed forward neural network trained with supervised Error Back Propagation technique and an unsupervised Adaptive Resonance Theory-2 (ART2) based neural network were used for automatic detection/diagnosis of localized defects in ball bearings. Vibration acceleration signals were collected from a normal bearing and two different defective bearings under various load and speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, and these inputs were used to train the neural network and the output represented the ball bearing states. The trained neural networks were used for the recognition of ball bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 100% reliability. Moreover, the networks were able to classify the ball bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.