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Intelligent and integrated RFID (Ⅱ-RFID) system for improving traceability in manufacturing

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Advances in Manufacturing 2014年第2期2卷 106-120页

作者： ke-sheng wang Shanghai key Laboratory of Intelligent Manufacturing and Robotics Shanghai University

In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short *** companies need traceability,which means a real-time view into thenproduction processes and *** frequency identification（RFID） technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes *** has become an important driver in manufacturing and supply chain ***,there is still a challenge in effectively deploying RFID in *** paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID（n-RFID） system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing *** supports the Norwegian manufacturing industries survive and thrive in global *** future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.

关键词： Radio frequency identification(RFID) Intelligent manufacturing Traceability Visibility System integration Intelligent decision support systems

Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers： Industry 4.0 scenario

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Advances in Manufacturing 2017年第4期5卷 377-387页

作者： Zhe Li Yi wang ke-sheng wang Department of Mechanical and Industrial Engineering Norwegian University of Science and TechnologyTrondheim Norway School of Business Plymouth University Plymouth UK

Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules： sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.

关键词： Data mining (DM) Machine centers Predictive maintenance Industry 4.0

Tackling the storage problem through genetic algorithms

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Advances in Manufacturing 2014年第3期2卷 203-211页

作者： Lapo Chirici ke-sheng wang Department of Computer Science University of PisaPisaItaly Department of Production and Quality Engineering Norwegian University of Science and TechnologyTrondheimNorway

The capability automated warehouse in of a company to implement an an optimized way might be nowadays a crucial leverage in order to gain competitive advantage to satisfy the demand. The order picking is a warehouse function that needs to deal with the retrieval of articles from their storage locations. Merging several single customer orders into one, a picking order can increase efficiency of warehouse operations. The aim of this paper is to define throughout the use of ad-hoc genetic algorithm （GA） how better a warehouse can be set up. The paper deals with order batching, which has a major effect on efficiency of warehouse operations to avoid wastes of resources in terms of processes and to control possibility of unexpected costs in advance.

关键词： Genetic algorithms (GA) Warehousemanagement Order batching Optimization

A novel method for the evaluation of fashion product design based on data mining

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Advances in Manufacturing 2017年第4期5卷 370-376页

作者： Bao-Rui Li Yi wang ke-sheng wang Shanghai key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai 200072People's Republic of China School of Business Plymouth University Plymouth UK Knowledge Discovery Laboratory Norwegian University of Science and Technology Trondheim Norway

It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph （EEG） and eye-tracking data can serve as reflection of the subcon- scious activities of human beings. The application of advanced neuroscience technology in industrial operation management has become a new research hot spot. This study uses EEG equipment and an eye-tracking device to record a subject＇s brain activity and eye-gaze data, and then uses data mining methods to analyze the correlation between the two types of signals. The fuzzy theory is then applied to create a fuzzy comprehensive evaluation model. The neural attributes are used to quantify the factors affected by product appear- ance and evaluation indicators. We use women＇s shirts as research subjects for a case study. The EEG Emotiv device and Tobii mobile eye-tracking glasses are used to record a subject＇s brain activity and eye-gaze data in order to quantify the evaluation factors related to product appearance. This method not only scientifically evaluates the uniqueness of product appearance but also provides an objective reference for improving product appearance design.

关键词： Product appearance design Evaluation method Data mining Electroencephalograph (EEG) Eye tracking Fuzzy model

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Guest editorial

Advances in Manufacturing 2014年第1期2卷 1-2页

作者： ke-sheng wang Da-Wei Tu Knowledge Discovery Laboratory Department of Productionand Quality Engineering Norwegian University of Scienceand Technology Trondheim Norway Shanghai key Laboratory of Intelligent Manufacturingand Robotics Shanghai University Shanghai 200072People's Republic of China

Manufacturing is and continues to be an essential part of world＇s economy. Manufacturing accounts for around 16 % of Europe＇s GDP. It remains a key driver of R＆D, innovation, productivity growth, job creation. 80 % of innovations are made by industries. 75 % of EU exports are in manufacturing products. Each job in manufacturing generates two jobs in service. Manufacturing in the middle of 21st century will look very different from today, and will be virtually unrecognizable from that of 30 years ago. Smart factories will be capable of rapidly adapting their physical and intellectual infrastructures to exploit changes in technology as manufacturing becomes faster, more responsive to changing global markets and closer to customers. The main driver is intelligent and advanced manufacturing technologies.

关键词：游客先进制造技术制造业组成部分世界经济技术变革基础设施全球市场

Models for lifetime estimation： an overview with focus on applications to wind turbines

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Advances in Manufacturing 2014年第1期2卷 79-87页

作者： Thomas M. Weite ke-sheng wang SINTEF Energy Research 7465 Trondheim Norway Department of Production and Quality Engineering NorwegianUniversity of Science and Technology Trondheim Norway

This paper provides an overview of models and methods for estimation of lifetime of technical components. Although the focus in this paper is on wind turbine applications, the major content of the paper is of general nature. Thus, most of the paper content is also valid for lifetime models applied to other technical systems. The models presented and discussed in this paper are classified in different types of model classes. The main classification used in this paper divides the models in the following classes： physical models, stochastic models, data-driven models and artificial intelligence, and combined models. The paper provides an overview of different models for the different classes. Furthermore, advantages and disadvantages of the models are discussed, and the estimation of model parameters is briefly described. Finally, a number of literature examples are given in this paper, providing an overview of applications of different models on wind turbines.

关键词： Lifetime estimation Model classification Wind turbine

Industry 4.0： a way from mass customization to mass personalization production

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Advances in Manufacturing 2017年第4期5卷 311-320页

作者： Yi wang Hai-Shu Ma Jing-Hui Yang ke-sheng wang School of Business Plymouth University Plymouth UK Department of Production and Quality Engineering Norwegian University of Science and Technology S.P.Andersens Veg 5 7031 Trondheim Norway School of Business Management Shanghai PolytechnicUniversity Shanghai 201209 People's Republic of China

Although mass customization, which utilizes modularization to simultaneously increase product variety and maintain mass production （MP） efficiency, has become a trend in recent times, there are some limitations to mass customization. Firstly, customers do not participate wholeheartedly in the design phase. Secondly, potential combinations are predetermined by designers. Thirdly, the concept of mass customization is not necessary to satisfy individual requirements and is not capable of providing personalized services and goods. Industry 4.0 is a collec- tive term for technologies and concepts of value chain organization. Based on the technological concepts of radio frequency identification, cyber-physical system, the Inter- net of things, Internet of service, and data mining, Industry 4.0 will enable novel forms of personalization. Direct customer input to design will enable companies to increasingly produce customized products with shorter cycle-times and lower costs than those associated with standardization and MP. The producer and the customer will share in the new value created. To overcome the gaps between mass customization and mass personalization, this paper presents a framework for mass personalization production based on the concepts of Industry 4.0. Several industrial practices and a lab demonstration show how we can realize mass personalization.

关键词： Industry 4.0 Smart manufacturing Mass customization production (MCP) Mass personalization production

Guest editorial of “Intelligent logistics and supply chains”

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Advances in Manufacturing 2014年第2期2卷 95-96页

作者： Yi wang Jan Ola Strhagen ke-sheng wang School of Material The University of ManchesterManchester UK Knowledge Discovery Laboratory Department of Production and Quality EngineeringNorwegian University of Science and TechnologyTrondheimNorway

Information and communication technology （ICT） has been considered as an enabling tool for having more effective and efficient operations in the management of logistics and supply chains for many years. Various information and communication technologies are used to improve perfor- mance of the logistics and supply chain network, such as ＂data mining＂, ＂radio frequency identification （RFID）＂, ＂machine learning＂, ＂smart transportation＂. While ICT systems are vital components in logistics and supply chains, their successful management rests on intelligent and inte- grated decision making throughout the logistics network. Intelligent decision technologies are applied to collect and store real-time data and then analyze and interpret the data and information of product, inventory, manufacturing and sales to discover useful knowledge for making better operation and management decisions. Advanced simulation and optimization of planning and scheduling systems are also used for improvement of inventory, production, pro-curement, and distribution planning. Intelligent agents can communicate with different partners in the supply chain, assist in collecting information, share product information, negotiate prices, and distribute alerts throughout the logis- tics and supply chains networks.

关键词： Guest editorial of Intelligent logistics and supply chains RFID

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

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Advances in Manufacturing 2015年第2期3卷 97-104页

作者： ke-sheng wang Zhe Li JФrgen Braaten Quan Yu Knowledge Discovery Laboratory Department of Production and Quality EngineeringNorwegian University of Science and Technology key Laboratory of Intelligent Manufacturing and Robotics Shanghai University

It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.

关键词： Backlash error Artificial neural network(ANN) Machine centers Predictive maintenance