An integrated framework for mechatronics based product development in a fuzzy environment

New product development (NPD) is a term used to describe the complete process of bringing a new concept to a state of market readiness. Mechatronics based product requires a multidisciplinary approach for its modeling, design, development and implementation. An integrated and concurrent approach focusing on integrating the mechanical structure with basic three components namely sensors, controllers and actuators is required. This paper aims at developing a framework for a new Mechatronics product development. For conceptual design of Mechatronics system, various tools like Fuzzy Delphi Method (FDM), Fuzzy Interpretive Structural Modeling (FISM), Fuzzy Analytical Network Process (FANP) and Fuzzy Quality Function Deployment (FQFD) are used. Based on the prioritized design requirements, the functional specifications of the required components are developed. Then, Computer Aided Design and control system software are used to develop the detailed system design. Then, a prototype model is developed based on the integration of mechanical system with Sensor, Controller and Electrical units. Performance of the prototype model is monitored and Fuzzy failure mode and effect analysis (FMEA) is then used to rank the potential failures. Based on the results of fuzzy FMEA, the developed model is redesigned. The proposed framework is illustrated with a case study related to developing automatic power loom reed cleaning machine.     

A novel methodology of design for Additive Manufacturing applied to Additive Laser Manufacturing process

Nowadays, due to rapid prototyping processes improvements, a functional metal part can be built directly by Additive Manufacturing. It is now accepted that these new processes can increase productivity while enabling a mass and cost reduction and an increase of the parts functionality. However, the physical phenomena that occur during these processes have a strong impact on the quality of the produced parts. Especially, because the manufacturing paths used to produce the parts lead these physical phenomena, it is essential to considerate them right from the parts design stage. In this context, a new numerical chain based on a new design for Additive Manufacturing (DFAM) methodology is proposed in this paper, the new DFAM methodology being detailed; both design requirements and manufacturing specificities are taken into account. The corresponding numerical tools are detailed in the particular case of thin-walled metal parts manufactured by an Additive Laser Manufacturing (ALM) process.     

A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring

This paper proposes a Digital Twin approach for health monitoring. In this approach, a Digital Twin model based on nonparametric Bayesian network is constructed to denote the dynamic degradation process of health state and the propagation of epistemic uncertainty. Then, a real-time model updating strategy based on improved Gaussian particle filter (GPF) and Dirichlet process mixture model (DPMM) is presented to enhance the model adaptability. On one hand, for those parameters in the nonparametric Bayesian network with prior models, the improved GPF is used to update them in real time. On the other hand, for parameters lacking a prior model, DPMM is proposed to learn hidden variables, which adaptively update the model structure and greatly reduce uncertainty. Experiments on the electro-optical system are conducted to validate the feasibility of the Digital Twin approach and verify the effectiveness of the nonparametric Bayesian network. The results of comparative experiments prove that the Digital Twin approach based on nonparametric Bayesian Network has a good model self-learning ability, which improves the accuracy of health monitoring.     

基于SOM规则自动生成的模糊神经网络模型

1 引言模糊系统建模一般将经过系统结构辨识和系统参数估计两个阶段。在辨识阶段,主要决定输入变量及其相互关系、模糊规则数、输入输出空间划分和系统参数的初值;在估计阶段,主要用来调整系统参数以使得系统的输出与目标输出的差值尽可能小。对于系统参数估计阶段的参数调整,人们已提出一些自动方法。对于系统结构辨识阶段,也产生了如模板法、聚类法和决策树法等,但这些方法一般都需要人工干预。其中模糊规则的生成与调整以及隶属度函数的选取是系统结构辨识阶段的主要问题,文提出了用神经网络自动生成模糊规则并进行隶属度形状调整,从而构成模糊神经网络。Wang提出自动分割输入空间的方法,Lin提出三阶段学习算法的模糊神经网络。     

改进学习贝叶斯网络结构的MDL准则

文章针对用于BN结构学习的MDL准则在继承性方面的不足,通过扩充DL测度的组成要素,在其中增加一项旨在反映目标网络结构与当前网络结构拓扑差异度的描述长度指标,改进MDL准则,使其具备处理先验知识的能力。     

制造网格及其关键技术

实现网络化、全球化、虚拟化,达到资源共享、协同设计、快速制造是网络化制造追求的最终目标,而网格技术的发展满足了此需求。本文通过把网格技术引入到制造业,深入分析了制造网格的体系结构,详细解析了制造网格的工作过程。同时,对制造网格服务点建设、制造网格服务注册与发现、制造网格任务调度三大关键问题进行深入分析,解决了网格技术向制造业推广的瓶颈问题。     

基于模糊神经网络的脉冲噪声滤波器

针对一般模糊神经网络结构复杂、不利于硬件实现的问题,提出了一种基于Sugeno型模糊神经网络的新型脉冲噪声滤波器,该滤波器采用神经网络的结构设计,有利于噪声模式的检测,其内含于神经网络中的模糊推理机制不仅能够有效地滤除脉冲噪声,而且又不破坏图象的细节,该滤波器还采用能够获得全局解的遗传算法来对网络进行调整,初步研究表明,该模糊神经滤波器在滤除景物图象中的脉冲噪声方面,优于标准中值滤波器。     

用模糊神经网络预测蘑菇生长过程

文章探讨了模糊神经网络的基本构造和原理,结合蘑菇生长过程预测系统重点分析了FNNC摸型的推理和学习方法。并在此基础上提出了TPH学习方法。该方法吸收了梯度下降算法和随机搜索算法的优点,能够使生长过程预测系统的学习以很大概率快速收敛在系统误差的最优点附近。最后文章指出模糊神经网络以及TPH学习算法在农业生产过程的应用。     

Experimental design in soap manufacturing for optimization of fuzzified process capability index

In manufacturing industries, the quality of a product depends on the combined effect of multiple input variables working singly or together and therefore attention has been given on process capability indices to shift from single to multivariate domain. In case of multivariable domain the capability to incorporate uncertainties at the time of decision making becomes necessary. Fuzzy system is introduced to take care of this requirement. In this article the process parameters of soap manufacturing industries have been analyzed. The process capability is determined using Fuzzy Inference System rule editor based on a set of justified if then statements as applicable for the process. The data has been collected in linguistic form to derive its process capability, using a set of justified rules and the effect of each factor has been determined using Design of Experiments (DoE) and analysis of variance technique (ANOVA) for improving the soap quality from perspective of its softness. This article ventures to propose a new methodology by integrating Fuzzy with DoE providing better result followed by DoE and Fuzzy Inference system in isolation.     

基于结合自适应步长布谷鸟搜查算法的模糊神经网络的软件可靠性增长模型

针对现有的软件可靠性增长模型(SRGM)适用性较差、预测精度波动大的问题,使用自适应步长布谷鸟搜查(ASCS)算法对模糊神经网络（FNN）的权重和阈值进行寻优,利用得到了最优权重和阈值的FNN建立SRGM。在使用缺陷数据对FNN训练的过程中,利用ASCS来调整FNN的权重和阈值,以此提高在预测过程中的精度,同时采用多次预测结果取均值的方式来减小FNN预测的波动性,以此建立基于结合自适应步长布谷鸟搜查算法的模糊神经网络（ASCS-FNN）的软件可靠性增长模型。利用3组软件缺陷数据,以误差比均值和误差平方和作为衡量标准,对基于ASCS-FNN、结合模拟退火算法的动态模糊神经网络（SA-DFNN）、FNN、BP网络（BPN）建立的SRGM的一步向前预测能力进行比较。预测结果表明,在四组模型中,基于ASCS-FNN建立的SRGM相对于SA-DFNN、FNN、BPN建立的SRGM的平均预测精度相对提高率RI(AE)和RI(SSE)分别为-1.48%、54.8%、33.8%和14.4%、76%、35.9%,并且该模型比FNN、BPN建立的SRGM在相同缺陷数据下的预测波动性小,而且网络结构比SA-DFNN的网络结构简单。因此该模型具有预测精度较高、预测稳定和具有一定的适用性等优点。     

A knowledge-based process planning framework for wire arc additive manufacturing

Wire arc additive manufacturing (WAAM) provides a rapid and cost-effective solution for fabricating low-to-medium complexity and medium-to-large size metal parts. In WAAM, process settings are well-recognized as fundamental factors that determine the performance of the fabricated parts such as geometry accuracy and microstructure. However, decision-making on process variables for WAAM still heavily relies on knowledge from domain experts. For achieving reliable and automated production, process planning systems that can capture, store, and reuse knowledge are needed. This study proposes a process planning framework by integrating a WAAM knowledge base together with our in-house developed computer-aided tools. The knowledge base is construed with a data-knowledge-service structure to incorporate various data and knowledge including metamodels and planning rules. Process configurations are generated from the knowledge base and then used as inputs to computer-aided tools. Moreover, the process planning system also supports the early-stage design of products in the context of design for additive manufacturing. The proposed framework is demonstrated in a digital workflow of fabricating industrial-grade components with overhang features.     

基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价

主要讨论了基于Fuzzy ARTMAP神经网络的高分辨率遥感图象土地覆盖分类方法及其实践．首先介绍了Fuzzy ARTMAP神经网络的原理，然后用SPOT XS图象试验数据进行土地覆盖分类．分类结果与传统的最大似然监督分类(MLC)、反馈式(Back Propagation，BP)神经网络的分类结果进行了比较．通过抽取500个样点对3种分类结果进行精度评价表明，Fuzzy ARTMAP神经网络相对其他两种方法，分类精度均有不同程度的改善，具有更好的分类结果，总分类精度比MLC和BP算法分别提高17．41％、7．32％．最后，对不同分类方法对于土地覆盖分类结果的影响进行了评价和分析．试验表明，Fuzzy ARTMAP神经网络用于高分辨图象土地覆盖分类研究可以获得相对较好的分类结果．     

内容分发网络中基于数据挖掘的影片热度预测

内容分发网络(CDN)系统对内容热度的估计主要依靠管理员的经验，所以主观性比较大，无法保证服务质量(QoS)。首先对数据进行预处理，得到预测影片的初始知识库，利用数据挖掘技术对已有知识进行学习，对新加入的影片热度进行预测，将影片合理部署到CDN系统中。比较基于贝叶斯网络的影片热度预测和基于决策树模型的影片热度预测，在正确分类率和其他分类参数相同的前提下，贝叶斯网络所用的时间更短，所以选择贝叶斯网络分类器，解决管理员部署时不准确的问题，提高CDN系统的效率。     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

Fuzzy association rule mining approaches for enhancing prediction performance

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM–MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM–MSapriori model predicted the future values effectively and outperformed the FCM–Apriori model and other models reported in the literature.     

Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation

In this paper the fault detection problem is solved using an alternative methodology based on a fuzzy/Bayesian strategy combining a Bayesian network and the fuzzy set theory. The new important issue in this proposed methodology is to address uncertainties in the input of the Bayesian Network. This contribution is possible since the fuzzy set theory is used as the knowledge representation. To illustrate the technique, the fault detection problem in induction machine stator-winding is considered. Specifically, the faults in the induction machine stator-winding are detected by a state change of stator current. Simulation results are presented to illustrate the advance of the proposed methodology when compared to standard Bayesian network.     

Application of Bayesian Network Learning Methods to Waste Water Treatment Plants

Bayesian Networks have been proposed as an alternative to rule-based systems in domains with uncertainty. Applications in monitoring and control can benefit from this form of knowledge representation. Following the work of Chong and Walley, we explore the possibilities of Bayesian Networks in the Waste Water Treatment Plants (WWTP) monitoring and control domain. We show the advantages of modelling knowledge in such a domain by means of Bayesian networks, put forth new methods for knowledge acquisition, describe their applications to a real waste water treatment plant and comment on the results. We also show how a Bayesian Network learning environment was used in the process and which characteristics of data in the domain suggested new ways of representing knowledge in network form but with uncertainty representations formalisms other than probability. The results of applying a possibilistic extension of current learning methods are also shown and compared.     

利用神经网络进行推理的模糊控制器

本文介绍了一种利用神经网络进行推理的模糊控制器。网络的输入和输出均为模糊集。训练后的网络能完成合成关系，即模糊推时。为了减少ＢＰ网络的高线训练时间，对模糊集进行了“编码”。最后给出了该控制器应用于曲线环节的实时控制结果。     

网络工程专业网络程序设计课程探讨

网络工程是近年来为满足社会对网络和信息化人才的迫切需求而设立的本科专业。网络程序设计是各高校网络工程及相关专业广泛开设的一门专业课。本文依据网络工程专业的培养目标,结合个人教学过程中的体会,对该课程的教学基本问题,包括课堂教学的内容设置与学时分配、实践环节内容与安排等进行初步探讨,阐述作者的理解和认识。     

Research on the Application of Bayesian Network in Agriculture Intelligent System

Bayesian network is a strong tool for uncertain knowledge representation and inference. This paper mainly introduces some technologies and methods about Bayesian network based on intelligent system. In the construction of Bayesian network, divorcing technology and noisy-or technology are used. In the inference of Bayesian network, VE algorithm and sampling algorithm are introduced. Finally, Bayesian network construction component and inference component are developed. Then an expert system about cow disease diagnosis is constructed based on the two components.