Re-engineering Manufacturing for Sustainability

This edited volume presents the proceedings of the 20th CIRP LCE Conference, which cover various areas in life cycle engineering such as life cycle design, end-of-life management, manufacturing processes, manufacturing systems, methods and ...

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.

Microspheres and microcapsules, a survey of manufacturing techniques: Part III: Solvent evaporation

A methodological survey of microsphere formation and microencapsulation techniques based on solvent extraction/evaporation techniques is presented. Thus, basic features of solvent extraction and solvent evaporation processes, including droplet formation, droplet/particle stabilization, and solvent removal, are outlined. Preparation of a wide range of microspherical and microcapsular products based on biodegradable polyesters, polysaccharides, and nonbiodegradable polymers are discussed. Dependence of microcapsule characteristics on manufacturing parameters, as well as performance evaluation of microspherical and microcapsular products, are also briefly covered.

an empirical study on bug assignment automation using chinese bug data

Univ SE Calif, Ctr Syst & Software Engn, ABB, Microsoft Res, IEEE, ACMSIGSOFT, N Carolina State Univ Comp Sci

自动化制造系统的产生与发展

探讨了自动化与制造业的产生和互促发展过程 ,论述了自动化制造系统的形成和不同发展阶段的内涵及重大作用 ,指出自动化技术与制造技术是驱动自动化制造系统发展的两个轮子 ,不能忽视任何一个侧面 ,自动化研究与制造技术研究相结合是发展先进制造技术的必由之路

制造系统控制结构现状和发展——从集中结构到动态结构

系统的决策控制活动依赖于系统内部的控制结构，制造系统的控制结构是先进制造与自动化领域的重要研究课题。制造系统的控制结构与计算机技术、生产设备自动化水平及制造模式等因素有关。早期的制造系统采用集中控制结构，而柔性制造系统和计算机集成制造系统多采用递阶控制结构。目前制造业已进入一个新时代，传统生产模式和企业结构无法适应不可预测的激烈的环境变化。因此，未来制造系统需要具备快速变化能力的动态控制结构

A Scheduling Algorithm for Flexible Manufacturing Cell

In this paper, a new scheduling algorithm for the flexible manufacturing cell is presented, which is a discrete time control method with fixed length control period combining with event interruption. At the flow control level we determine simultaneously the production mix and the proportion of parts to be processed through each route. The simulation results for a hypothetical manufacturing cell are presented.

机器人及机器人学中的控制问题

机器人已成为进一步实现制造业、非制造业、军事活动以至家庭服务自动化的重要工具.机器人学则是更综合地研究如何使机器或系统具有“思想、感知或动作功能的一门交叉性新学科”.机器人的发展是波浪式的,经历了多次的曲折.目前工业机器人中除装配机器人外,其他点焊、弧焊、喷漆及搬运机器人,由于应用范围限制,以及工业生产装备更新速度和投资规模的影响,市场日趋饱和.以美国为例.制造商订单1984年达到高峰6046台.至1986年下降至5713台,不过对机器人未来市场大都持乐观态度.但进一步开拓市场.有待于推出适用于各种环境作业的各式各样的机器人.日本东京大学教授认为机器人当前处于又一次高潮的前夜(Nightmare),正是需要大力加强研究开发的时期.我国正在着手建立工业机器人产业,这一情况值得我们注意.本文着重从研究角度来介绍当前机器人及机器人学中的控制问题的现状及主要问题.

An analysis of manufacturing simulation software.

As a management tool Similation Software deserves greater analysis from both an academic and industrial viewpoint. A comparative study of three packages was carried out from a 'first time' use approach. This allowed the ease of use and package features to be assessed using a simple theoretical benchmark manufacturing process. To back the use of these packages an objective survey on simulation use and package features was carried out within the manufacturing industry.This identified the use of simulation software, its' applicability and preception of user requirements thereby proposing an ideal package.

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35

Intelligent software for laboratory automation

Whelan, K. E. and King, R. D. (2004) Intelligent software for laboratory automation. Trends in Biotechnology 22 (9): 440-445

Networks in small manufacturing firms

Fuller-Love, Nerys, and Thomas, Esyllt, 'Networks in small manufacturing firms', Journal of Small Business and Enterprise Development (2005) 11(2) pp.244-253 RAE2008