On-line predictive model for disassembly process planning adaptation

This paper presents an on-line predictive model for disassembly process adaptation. The prediction enables a planner to adapt the process plan based on the condition of the product (e.g., degree of rustiness, deformation) during process execution. This model tries to correlate the product physical condition, used as an explanatory variable, with the component value and disassembly cost, the response variables. The core of the approach is based on an inference engine that used a kernel regression. A simple methodology for integrating the predictive planner in a disassembly system is presented and exemplified by a case study of the disassembly of a ratio.     

Preliminary design and manufacturing planning integration using web-based intelligent agents

Software agents have been increasingly used in the product and process development in industry over the past years due to the rapid evolvement of the Internet technology. This paper describes agents for the integration of conceptual design and process planning. Agents provide mechanisms to interact with each other. This mechanism is important since both of those processes involve negotiations for optimization. A set of design and planning software agents has been developed. These agents are used in a computer-based collaborative environment, called a multi-agent platform. The main purpose of developing such a platform is to support product preliminary design, optimize product form and structure, and reduce the manufacturing cost in the early design stage. The agents on the platform have access to a knowledge base that contains design and planning rules. These rules are derived from an analysis of design factors that influence process and resource planning, such as product material, form, shape complexity, features, dimension, tolerance, surface condition, production volume, and production rate. These rules are used by process planning agents to provide process planners with information regarding selecting preliminary manufacturing processes, determining manufacturing resources, and constructing feedback information to product designers. Additionally, the agents communicate with WEB servers, and they are accessible by users through Internet browsers. During performing design and planning tasks, agents access the data pertinent to design and manufacturing processes by the programming interfaces of existing computer-aided design (CAD) and manufacturing system. The agents are supported by a developed prototype agent platform. The agents and the platform enable the information exchange among agents, based on a previously developed integrated design and manufacturing process object model.     

A machining process planning activity model for systems integration

A key issue of integrating process planning systems with design systems and production planning systems is how to overcome barriers in data exchange and sharing amongst software systems. A machining process planning activity model was developed to address some of the barriers. This model represents functional components and data requirements in process planning systems. The purpose of the model is to create the context in which data requirements and data flow for numerically controlled machining process planning are defined. Furthermore, the model was developed as a unification of many previously developed process planning activity models.     

CASE planning and the software process

Automating a software process magnifies its strengths and aceentuates its weaknesses. Automation can make an effective process more effective, but it can also make a chaotic process even worse-and at considerable expense. Anyone who buys expensive tools to solve an ill-defined problem is likely to be disappointed. Unless such tools are obtained as part of a thoughtful software process improvement plan, the purchase could be an expensive mistake. This article discusses software process maturity and its relationship to planning and installing computer-aided software engineering (CASE) systems. Although process is not a magic answer (there isn't one), the key issues are discussed from a process perspective, and guidelines are given for avoiding the most common pitfalls. Because CASE systems can involve significant investment, an economic justification may be necessary. The relevant financial considerations are therefore discussed, and some basic steps for producing such justifications are outlined. Finally, some key considerations for introducing and using CASE systems are discussed.This material is based in part on material from, Watts S. Humphrey,Managing the Software Process, Addison-Wesley, Reading, MA, © 1989. Reprinted with permission.This work was sponsored by the U.S. Department of Defense. The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange.     

Case-based process planning using an object-oriented model representation

This research focuses on the development of a process-planning system. This system utilizes case-based techniques for process selection and sequencing to combine the advantages of the variant and generative approaches to process planning. The case-based process planner utilizes an object-oriented model representation to operate on general three-dimensional prismatic parts, which are represented by a collection of features (e.g. slots, pockets, holes, etc.). Each feature subplan is developed by the case-based planner. Then the feature subplans are combined into the global process plan for the part via a hierarchical plan-merging mechanism. Abstracted feature subplans correspond to cases, which are used in subsequent planning operations to solve new problems. The abstracting and storing of feature subplans as cases is the primary mechanism by which the planner learns from its previous experiences to become more effective and efficient.     

Estimating software readiness using predictive models

In this study, defect tracking is used as a proxy method to predict software readiness. The number of remaining defects in an application under development is one of the most important factors that allow one to decide if a piece of software is ready to be released. By comparing predicted number of faults and number of faults discovered in testing, software manager can decide whether the software is likely ready to be released or not.The predictive model developed in this research can predict: (i) the number of faults (defects) likely to exist, (ii) the estimated number of code changes required to correct a fault and (iii) the estimated amount of time (in minutes) needed to make the changes in respective classes of the application. The model uses product metrics as independent variables to do predictions. These metrics are selected depending on the nature of source code with regards to architecture layers, types of faults and contribution factors of these metrics. The use of neural network model with genetic training strategy is introduced to improve prediction results for estimating software readiness in this study. This genetic-net combines a genetic algorithm with a statistical estimator to produce a model which also shows the usefulness of inputs.The model is divided into three parts: (1) prediction model for presentation logic tier (2) prediction model for business tier and (3) prediction model for data access tier. Existing object-oriented metrics and complexity software metrics are used in the business tier prediction model. New sets of metrics have been proposed for the presentation logic tier and data access tier. These metrics are validated using data extracted from real world applications. The trained models can be used as tools to assist software mangers in making software release decisions.     

A Bayesian predictive software reliability model withpseudo-failures

In our previous paper (2000), a Bayesian software reliability model with stochastically decreasing hazard rate was presented. Within any given failure time interval, the hazard rate is a function of both total testing time as well as number of encountered encountered failures. In this paper, to improve the predictive performance of our previously proposed model, a pseudo-failure is inserted whenever there is a period of failure-free execution equals (1-α)th percentile of the predictive distribution for time until the next failure has passed. We apply the enhanced model with pseudo-failures inserted to actual software failure data and show it gives better results under the sum of square errors criteria compared to previous Bayesian models and other existing times between failures models     

Rapid software integration using structured methods

In this paper we discuss an approach to organizing the integration of 110,000 lines of C, SQL, Assembler, and microcode distributed over a network of 36 processors of four types so that it could be accomplished in six months. The software runs on a test system architecture consisting of a LAN-based workstation group and a set of VME-based embedded processors. By using structured methodology, parallelism in the integration process was achieved. The necessary stub tools were identified and developed before integration began. The principles followed and experiences of integrating the system are discussed.     

Manufacturing features extraction and recognition in automated process planning

Features are considered the information carriers between design and manufacturing. Manufacturing feature here refers to a certain shape of volumes which is to be removed from the blank to produce the finish part. In this paper, an Overall Removable Volume is generated through the graphically comparing a finish part and a blank. A Removable Surface-based Concave Edge (RM-CV) feature extraction approach is introduced to extract manufacturing features. The recognition of manufacturing features are performed with the programs written in C and CLIPS expert system. The General Manufacturing Feature Information Scheme (GMF-IS) is also designed to maintain the information between the features, blank and finished part. This paper covers part of the research project founded by Naval Ordnance Station.     

A linearized polynomial mixed integer programming model for the integration of process planning and scheduling

This paper presents a linearized polynomial mixed-integer programming model (PMIPM) for the integration of process planning and scheduling problem. First, the integration problem is modeled as a PMIPM in which some of the terms are of products of up to three variables, of both binary and continuous in nature. Then, an equivalent linearized model is derived from the polynomial model by applying certain linearization techniques. Although the linearized models have more variables and constraints than their polynomial counterparts, they are potentially solvable to the optimum in comparison to their equivalent polynomial models. Experiments show that the linearized model possesses certain characteristics that are absent from other models in the literature, and provides a fundamental framework for further research in this area.     

面向任务的软件过程控制模型

提出了一种面向任务的软件过程控制模型,旨在提高软件过程控制能力、促进CMM/CMMI的实施。该控制模型采用面向任务的调度思想,将软件过程划分为一系列任务。采用有限域约束规划方法建立约束模型,生成任务调度方案。实验验证了该模型的可行性和有效性。     

XP-PSP集成软件过程模型

为了提高软件开发的效率,控制软件产品的质量,以及增强软件工程师自我管理的能力,提出了一个集成的软件过程模型——XP-PSP。XP-PSP首先采用极限编程(XP)迭代式的过程框架,然后在每次迭代中引入个人软件过程(PSP)和极限编程的具体实践,将极限编程和个人软件过程中的具体实践相结合,使得过程模型既具有极限编程所提供可适应性又具有个人软件过程所支持的可预测性。通过将这个过程模型应用到真实的项目环境中,验证了它的合理性和有效性,初步实现了两种过程的融合。     

采用预测控制的龙门架式冗余自由度机械臂轨迹规划算法

为解决近年来工业常用的复杂结构大型机械臂的运动控制问题,本文以龙门架式机械臂为研究对象,首先结合材料力学分析,对在双臂荷载下的横梁挠度公式进行推导,进而对机械臂系统建立精确的运动学模型.为了便于后期算法设计,又在此基础上进行了降阶、线性化等模型简化工作.针对这一具有关节限位等约束条件和冗余自由度的多输入多输出高维复杂系统,提出了一种基于预测控制的轨迹规划算法.该算法根据模型中不同关节的运动范围以及定位精度等特性,划定其不同的运动优先级,通过在优化问题中对各个关节控制量设置不同的权重,来解决冗余问题.同时通过实时更新与滚动优化,很好的保证了控制的及时陛.仿真结果表明了本文所推导的挠度公式的准确性和所提出算法的有效性,不仅很好地解决了冗余问题,而且充分利用不同关节的运动特性,针对不同类型的跟踪目标值给出相适应的轨迹规划方案.     

Dynamic planning model for determining cutting parameters using neural networks in feature-based process planning

Although feature-based computer-aided process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prohibit the shop floor controllers from rapidly coping with unexpected production errors. The objective of the paper is to suggest a neural network-based dynamic planning model, by which the shop floor controllers determine cutting parameters in real-time based on shop floor status. At off-line is the dynamic planning model constructed as a neural network form, and then embedded into each removal feature. The dynamic planning model will be executed by the shop floor controllers to determine the cutting parameters. A prototype system is constructed to validate whether the dynamic planning model is capable of determining dynamically and efficiently the cutting parameters for a particular set of shop operating factors. Owing to the dynamic planning model, the shop floor controller will increase flexibility and robustness by rapidly and adaptively determining the cutting parameters in unexpected errors occurring.     

Optimal control of a nonlinear fed-batch fermentation process using model predictive approach

Bioprocesses are involved in producing different pharmaceutical products. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. The main control goal is to get a pure product with a high concentration, which commonly is achieved by regulating temperature or pH at certain levels. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The novel approach used here is to use the inverse of penicillin concentration as a cost function instead of a common quadratic regulating one in an optimization block. The result of applying the obtained controller has been displayed and compared with the results of an auto-tuned PID controller used in previous works. Moreover, to avoid high computational cost, the nonlinear model is substituted with neuro-fuzzy piecewise linear models obtained from a method called locally linear model tree (LoLiMoT).     

Rapid goal-oriented automated software testing using MEA-graph planning

With the rapid growth in the development of sophisticated modern software applications, the complexity of the software development process has increased enormously, posing an urgent need for the automation of some of the more time-consuming aspects of the development process. One of the key stages in the software development process is system testing. In this paper, we evaluate the potential application of AI planning techniques in automated software testing. The key contributions of this paper include the following: (1) A formal model of software systems from the perspective of software testing that is applicable to important classes of systems and is amenable to automation using AI planning methods. (2) The design of a framework for an automated planning system (APS) for applying AI planning techniques for testing software systems. (3) Assessment of the test automation framework and a specific AI Planning algorithm, namely, MEA-Graphplan (Means-Ends Analysis Graphplan), algorithm to automatically generate test data. (4) A case study is presented to evaluate the proposed automated testing method and compare the performance of MEA-Graphplan with that of Graphplan. The empirical results show that for software testing, the MEA-Graphplan algorithm can perform computationally more efficiently and effectively than the basic Graph Planning algorithm.

基于行为的软件测试过程模型及其应用研究

在深入研究各种综合测试技术的基础上,通过对比分析各种主流的测试模型,提出了一种基于行为的软件测试过程模型,包括了测试设计、测试计划、测试执行、测试结果分析和测试重用等活动,把这些测试活动嵌入到软件开发的整个生命周期当中。然后将这种测试模型应用到一个大型的应用程序测试项目(AMP)中,发现这种测试模型在尽早发现Bug和回归测试选择效率上具有较好的测试效果。     

CMM模型及对软件过程的改进

CMM提供了一个评估软件开发过程管理改进的框架和工程能力的标准，并给出了软件过程不断进化的科学途径，它标志着软件工程技术的一个突破性发展，其改进软件过程的目标取决于企业的商业目标和当前的技术水平。系统地介绍了产生CMM模型的来源、内容及对软件过程的改进，目的是进一步加深理解CMM框架及其对软件过程改进的指导，有效地达到软件过程持续改进的效果。     

Discriminating between disturbance and process model mismatch in model predictive control

A novel method for discriminating faults in model predictive control is presented. The proposed method monitors the Kalman filter innovations to detect the presence of autocorrelation, which is an indication of suboptimal state estimation. The cause of the suboptimal state estimation is diagnosed by the observability of this innovations process. This task involves determining the order of the autocorrelation present in the innovations. The proposed MPC fault discrimination method is demonstrated on a SISO process and a MIMO process.     

网络地址转换在网络安全中的应用

Internet网络中网络地址转换技术产生的背景、主要内容、内部转换机制、在网络安全中的应用以及与其它网络安全技术相比的优点。并介绍了基于网络地址转换技术的防火墙的特点和网络地址转换技术的最新发展。