制造物联网系统动力学建模与分析

动态因素影响生产系统的运作表现,而相应的流程再造和信息共享机制使得充满动态性的生产系统达到统计学意义上的运作最优化。随着系统动力学广泛应用于系统结构和参数的控制与仿真,物联网技术为广泛、实时、精确采集生产系统的动态性提供了使能手段,使得系统动力学可以在实时信息环境下对生产系统的运作表现进行全面、敏捷、精确的仿真和分析,使得制造物联网系统的运作达到最优。本文采用系统动力学构建仿真模型,对比分析物联网技术应用前后的生产系统运作差异,并通过投资回报率分析不同方案的经济可行性,为企业决策提供参考依据。     

Development of a model of barriers to environmentally conscious manufacturing implementation

Manufacturing firms consume energy and natural resources in highly unsustainable manner and release large amounts of green house gases leading to many economic, environmental and social problems from climate change to local waste disposal. Consciousness about these issues has led to a new manufacturing paradigm of environmentally conscious manufacturing (ECM). However, there are various barriers to the implementation of ECM. A proper understanding and analysis of these barriers will allow the management of the company and government to prioritise their focus to mitigate root barriers for effective implementation of ECM. This paper aims at identifying the barriers to ECM, developing a model of these barriers using statistical analysis and testing the model using structural equation modelling technique. The results provide three types of barriers – internal, policy and economic. The results show that internal barriers are the root barriers and cause policy and economic barriers. It reflects that the barriers which are internal to the organisation should be mitigated first for effective implementation of ECM.     

An empirical investigation into the barriers to postponement

The need for postponement is significantly driven by today's business and marketing environment. However, currently its applications are still not as widespread as expected. Therefore, this paper empirically investigates what factors hinder the adoption and implementation of postponement, and whether postponement applications are expected to increase in practice. Following a literature review on how to prompt and progress postponement applications, we present our research design. Namely, a questionnaire survey was conducted (by way of both post and e-mail) among 368 British manufacturing companies across four industrial sectors to help build statistical generalizations on the barriers to postponement. Then, we provide demographic data and response rate, and assess the reliability and validity of the survey instruments. The results from our questionnaire survey indicate that most of the highest ranked barriers were related to how a company manages its external networks (suppliers or customers), while those barriers related to distributors were put towards the bottom of the list. The respondents also expected postponement to be less used in three years. Finally, we draw some conclusions, determine limitations of this research and make suggestions for future research.     

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.     

RP－based Abrading Technique for Graphite EDM Electrode

Traditional processes for machining mold cavities are lengthy and costly.EDM(electro-discharge machining)is the most commonly used technique to obtain complex mold cavities.However,some electrodes are difficult to fabricate because of the complexity.Applying RP(rapid prototyping)technology to fabricate an abrading tool which is used to abrade graphite EDM electrodes,the cost and cycle time can greatly be reduced.The paper describes the work being conducted in this area by the authors.This technique will find widespread application in rapid steel mold manufacturing.     

An Efficient Scheme for Data Pattern Matching in IoT Networks

The Internet has become an unavoidable trend of all things due to the rapid growth of networking technology, smart home technology encompasses a variety of sectors, including intelligent transportation, allowing users to communicate with anybody or any device at any time and from anywhere. However, most things are different now. Background: Structured data is a form of separated storage that slows down the rate at which everything is connected. Data pattern matching is commonly used in data connectivity and can help with the issues mentioned above. Aim: The present pattern matching system is ineffective due to the heterogeneity and rapid expansion of large IoT data. The method requires a lot of manual work and has a poor match with real-world applications. In the modern IoT context, solving the challenge of automatic pattern matching is complex. Methodology: A three-layer mapping matching is proposed for heterogeneous data from the IoT, and a hierarchical pattern matching technique. The feature classification matching, relational feature clustering matching, and mixed element matching are all examples of feature classification matching. Through layer-by-layer matching, the algorithm gradually narrows the matching space, improving matching quality, reducing the number of matching between components and the degree of manual participation, and producing a better automatic mode matching. Results: The algorithm's efficiency and performance are tested using a large number of data samples, and the results show that the technique is practical and effective. Conclusion: the proposed algorithm utilizes the instance information of the data pattern. It deploys three-layer mapping matching approach and mixed element matching and realizes the automatic pattern matching of heterogeneous data which reduces the matching space between elements in complex patterns. It improves the efficiency and accuracy of automatic matching.     

Modelling of flexible manufacturing system: a review

A flexible manufacturing system (FMS) due to its ability of being flexible in nature is concerned with automatic production of different parts in medium range. In short, it can be regarded as an automated manufacturing system. In this paper, an insight on previous work in the area of FMS modelling has been provided with an overview of research and development for better understanding of FMS. It serves as a medium for investigation of work that are accomplished by using different modelling techniques in FMS like mathematical, artificial intelligence, hierarchical, multi criteria decision-making method, Petri Nets and simulation. This paper will help researchers who are keen to do research in the area of FMS modelling, by highlighting contribution of available techniques in the field of FMS. It will also help them in deciding which modelling techniques can be used for a given problem. At last, comparison of different parameters considered in the recent papers of modelling FMS has been provided in the form of Table, along with a clear vision related to those works that still need to be investigated.     

Hierarchical approach to modelling of liquid-crystalline polymers

Summary Liquid-crystalline polymers exhibit fascinating structure at many size scales. This paper describes how computer models at different size scales can be linked together by a hierarchical approach to give a better understanding of the properties and behaviour of these materials. The atomic scale is considered first, where semi-empirical molecular orbital techniques are used to calculate the torsional energy functions associated with rotating the backbone bonds of the polymer. Secondly, entire chains are simulated using a Monte Carlo technique based on the torsional energy functions to deduce the persistence length of the polymer of interest. A theoretical relationship enables the Frank elastic constants to be determined from the persistence length. The elastic constants may then be used as input parameters for models both to predict microstructure and to help understand the role of defects in the shear flow of these materials. The hierarchical approach provides a meaningful framework within which data obtained from small-scale models are used to parameterise models at a larger scale.     

Identified research directions for using manufacturing knowledge earlier in the product life cycle

Design for manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within computer-aided design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customisation on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is: How can manufacturing knowledge, depending on its definition, be used earlier in the product life cycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behaviour and context characteristics of a product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product life cycle.     

Measuring the manufacturing process yield based on fuzzy data

The process yield is the most basic and common criterion used in the manufacturing industry as the basis for measuring process performance. In the conventional case, the underlying data for a manufacturing process are obtained from the output responses of continuous quantities that are always assumed to be real numbers. However, measurement of the output process occasionally appears to be imprecise in practical situations. Accordingly, the output responses should be assumed to be so-called fuzzy data. We propose a constructive methodology to obtain the fuzzy estimate of the yield index S _pk with the help of the extension principle of fuzzy sets theory. This study, based on an analytical approach, is an advancement over existing technology in the area of process capability analysis that is easy to implement in plant applications.     

A multi-criteria analysis for an internet of things application recommendation system

Various internet of things applications are available that cover every aspect of daily life and users can subscribe to numerous IoT applications. Selecting the most suitable IoT applications for individual users is a critical challenge. This study aims to solve this challenge by proposing recommendation system using a hybrid multicriteria decision-making approach based on the analytical hierarchy process and simple additive weight methods. Based on the opinions and preferences of experts, the model and the hierarchy were designed to assess and compare three crucial criteria, namely smart objects, applications, and providers. The results show that applications criterion is more important for users than the other two criteria. In specific, privacy, reliability, and availability are crucial criteria for IoT applications.     

An Optimal Classification Model for Rice Plant Disease Detection

Internet of Things (IoT) paves a new direction in the domain of smart farming and precision agriculture. Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent. In smart farming, IoT devices are linked among one another with new technologies to improve the agricultural practices. Smart farming makes use of IoT devices and contributes in effective decision making. Rice is the major food source in most of the countries. So, it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices. The development and application of Deep Learning (DL) models in agriculture offers a way for early detection of rice diseases and increase the yield and profit. This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine (CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment. The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet. The CNNIR-OWELM method uses histogram segmentation technique to determine the affected regions in rice plant image. In addition, a DL-based inception with ResNet v2 model is engaged to extract the features. Besides, in OWELM, the Weighted Extreme Learning Machine (WELM), optimized by Flower Pollination Algorithm (FPA), is employed for classification purpose. The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernel . The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another. The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905, specificity of 0.961, and accuracy of 0.942.     

Time-Temperature Superposition to Determine the Stress-Rupture of Aramid Fibres

Conventional creep testing takes a long time to obtain stress-rupture data for aramid fibres at the low stress levels likely to be used in practical applications. However, the rate of creep of aramid can be accelerated by a thermally activated process to obtain the failure of fibres within a few hours. It is possible to obtain creep curves at different temperature levels which can be shifted along the time axis to generate a single curve know as a master curve, from which stress-rupture data can be obtained. This technique is known as the time-temperature superposition principle and will be applied to Kevlar 49 yarns. Important questions relating to the techniques needed to obtain smooth master curves will be discussed, as will the validity the resulting curves and the corresponding stress-rupture lifetime.     

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.     

Development of oligonucleotide microarray involving plasma polymerized acrylic acid

This paper presents the manufacturing of biochips by using the COOH- derived polymer coating deposited by plasma polymerization of acrylic acid. This technology is based on depositing a thin layer obtained by plasma polymerization of acrylic acid which allows a further covalent immobilization of biomolecules on glass substrates. The plasma power value was optimized to maximize the stability of plasma polymerized acrylic acid (PPAA) coatings in water, which has a very important role for such applications. In order to obtain a covalent immobilization of DNA probes on the PPAA coated surface, the activation protocol of carboxylic function was carried out with the help of N-Hydroxy Succinimide and 1-Ethyl-3-(3-DimethylAminopropyl) Carbodiimide. The efficiency of PPAA coated in microarray applications was compared with two types of commercial slides. Such surfaces have shown very interesting results in terms of relative density of attached DNA probe molecules and signal-to-background ratio measured for target DNA hybridization. Nonspecific DNA bonding measurements showed only a small amount of nonspecific physisorption between the DNA probe and the PPAA-activated surfaces. This work shows that the plasma polymerization technique can be successfully applied to produce a high-quality glass surface for the manufacturing of DNA arrays.     

Ultrasonic consolidation for embedding SMA fibres within aluminium matrices

For this paper, adaptive composites will be considered as structural materials for advanced aerospace applications which have the ability to measure and respond to external stimuli by adapting the structure accordingly, through embedded active or passive functional elements. The intention of this project was targeted at the fabrication of adaptive composites using a novel layered manufacturing technique called ultrasonic consolidation (UC). This paper details the initial study of this research to identify plastic deformation of the matrix material around shape memory alloy (SMA) fibres, and bond quality, based on the microscopic observation and mechanical test results obtained. The embedding method, considered during this study, has successfully produced laminate specimens, with full consolidation, within seconds, using low oscillation amplitude and low contact pressures (<300 kPa). This work will report on the identification of the bonding characteristics for these SMA fibres when embedded in aluminium alloy 3003 specimens.     

Simultaneous optimal selection of design and manufacturing tolerances with different stack-up conditions using genetic algorithms

Tolerance design is one of the most critical aspects of product design and development process as it affects both the product's functional requirements and manufacturing cost. Unnecessarily tight tolerances lead to increased manufacturing cost, while loose tolerances may lead to malfunctioning of the product. Traditionally, this important phase of product development is accomplished intuitively to satisfy design constraints, based on handbooks' data and/or skill and experience of the designers. Tolerance design carried out in this manner does not necessarily lead to an optimum design. Research in this area indicates that, in general, tolerance design is carried out sequentially in two steps; (1) tolerance design in CAD to obtain design or functional tolerances and (2) tolerance design in CAPP to obtain manufacturing tolerances. Such a sequential approach to tolerance design suffers from several drawbacks, such as more time consumption, suboptimality and unhealthy working atmosphere. This paper reports on an integrated approach for simultaneous selection of design and manufacturing tolerances based on the minimization of the total manufacturing cost. The nonlinear multivariable optimization problem formulated in this manner may result in a noisy solution surface, which can effectively be solved with the help of a global optimization technique. A solution methodology using genetic algorithms and applying penalty function approach with proper normalization of the penalty terms for handling the constraints is proposed. The application of the proposed methodology is demonstrated on a simple mechanical assembly with different tolerance stack-up conditions.     

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services and scalable resources. However, the existing cloud-based approach should consider the above constraints to efficiently place and process IoT applications. In this paper, an efficient optimization approach for placing IoT applications in a multi-layer fog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into account IoT application requirements, available resource capacities, and geographical locations of servers, which would help optimize IoT application placement decisions, considering multiple objectives such as data transmission, power consumption, and cost. Simulation experiments were conducted with various IoT applications (e.g., augmented reality, infotainment, healthcare, and compute-intensive) to simulate realistic scenarios. The results showed that the proposed approach outperformed the existing cloud-based approach in terms of reducing data transmission by 64% and the associated processing and networking power consumption costs by up to 78%. Finally, a heuristic approach was developed to validate and imitate the presented approach. It showed comparable outcomes to the proposed model, with the gap between them reach to a maximum of 5.4% of the total power consumption.     

我国ITS物联网发展策略研究

物物相联的物联网热浪为交通日益增长的我国提供了新的解决途径与发展机遇。提出了物联网下智能交通运输系统（ITS）的概念与架构,探讨了物联网下的ITS与传统ITS的区别及变革;分析了我国发展ITS物联网的基础条件以及需要突破的重点,提出了我国ITS物联网发展策略,以期促进我国交通运输业和物联网产业的快速发展。     

基于QFD和AHP法的适老浴室柜设计研究

目的 设计出符合老年人实际使用需求的浴室柜产品,更好地帮助他们完成日常起居的生活。方法 以质量功能展开(QFD)理论和层次分析法(AHP)作为理论支撑,构建适老浴室柜质量屋(HOQ)设计理论模型,并以此为依据对适老浴室柜进行设计实践。首先通过实地访谈和观察法等方式,获取老年人的行为状况和需求信息,利用KJ法对需求进行聚类和精炼,得到用户需求层次化结构;然后运用AHP法计算出各层需求的权重值;最后利用HOQ理论将用户各项需求转化为适老浴室柜设计特征。结果 依据设计特征得到一款最为符合老年人需求的适老浴室柜设计方案。结论 该方法能够挖掘出用户最为迫切的需求,使设计出的产品更为科学与合理,更好地帮助老年人应对衰老所带来的困扰,同时对同类产品的设计研究也具有重要的参考意义。