Towards zero-defect manufacturing (ZDM)—a data mining approach

Wang, Kesheng

doi:10.1007/s40436-013-0010-9

Cited by 114 publications

(59 citation statements)

References 45 publications

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“…So you can support decision making process. [13] Data warehouse in the mid-1980s, it is a subject-oriented, integrated, nonvolatile, time-varying data collection, the data warehouse is to target different sources, different from the structure of the data after processing in data warehouse store, retrieve and maintain its support for a comprehensive, highlevel analysis and processing, and large amounts of complex data in decision support. Data Warehouse so that the user has the freedom to extract any data, without interfering with the normal operation of the business database.…”

Section: E Data Analyingmentioning

confidence: 99%

Logistics 4.0 Solution-New Challenges and Opportunities

Wang¹

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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Abstract-Logistics 4.0 will change and improve traditional logistics and its self-perception. Logistics has become a core pillar in the value chain for industries and it is crucial for them to have the right good with the right quantity and right quality at the right time at the right place and in the right condition and at right price (7R), otherwise they cannot be competitive in the market. In a dynamically changing and uncertain logistic environment, fulfilling these requirements is getting more and more difficult. The highly dynamic and uncertain logistic markets and huge logistic networks require new methods, products and services. Today's consumer behavior leads to new logistics challenges and opportunities. The concept of the cyber physical system (CPS), the wireless networks, the Internet of Things and Services (IOT&S), Big Data/Data Mining (DM) and cloud computing, etc. seem to be the probable technical solution for that. Its consequent implementation inevitably results in the necessity to reconsider some basic concepts of traditional logistics. This paper is to outline the vision of "Logistics 4.0" and give a definition and propos some basic technical components of logistic 4.0. Additionally it emphasizes the question, which paradigm changes will emerge from the fourth industrial revolution and how to address them proactively. Two laboratory cases show how to implement the technologies in logistics 4.0.

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Section: E Data Analyingmentioning

confidence: 99%

Logistics 4.0 Solution-New Challenges and Opportunities

Wang¹

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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“…2) opens up innovative new possibilities for companies. [13] [14] Data generated by Cyber-physical systems (CPS) and transmitted by Internet if Things (IOT) monitoring machine/process condition is automatically reviewed to pick up any patterns that indicate a possible fault through Data Mining systems. This decision use Internet of service (IOS) to allow the onset of a stoppage to be recognized early and corrective measures to be planned and introduced in the most effective way.…”

Section: B a Ipdm (Intelligent Predicitve Mantenance) For Elevator Smentioning

confidence: 99%

Intelligent Predictive Maintenance (IPdM) for Elevator Service- Through CPS, IOTaS and Data Mining

Wang¹,

Griffiths²,

Guo³

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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Abstract-With the rapid economic growth and urbanization development, most of elevator production and service companies have completely changed their service policy opting to eliminate the standard preventive service policy. They now provide some form of predictive service policy and emphasize that they utilize remote monitoring of elevators to detect faults and estimate when components may need to be maintained due to actual usage. However, most of them are not true predictive maintenance policy. They merely a preventive, slightly enhanced, usage-based program. What is the future of elevator production and service companies? This paper will challenge the word "predictive maintenance" and present the framework of intelligent predictive maintenance system for smart elevator service based on industry 4.0 concepts.

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“…Similar approaches like cloud computing and the internet of things (IoT) can improve accessibility to information networks, and centralize data sets in manufacturing activities in order to make use of it in decision making [1]. Data representation, data automation, data mining and data analytics technologies improve the management of manufacturing knowledge particularly related to process planning and decision making in the middle and late design development stages [2,3]. Virtual systems propose using simulated computer generated environments and software interface to manage knowledge based on the representations of real life process capability feedback [4].…”

Section: Introductionmentioning

confidence: 99%

Improving Design for Manufacturing implementation in knowledge intensive collaborative environments: An analysis of organisational factors in aerospace manufacturing

Souri

Gao

Owodunni

et al. 2017

2017 IEEE Technology &Amp; Engineering Management Conference (TEMSCON)

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Abstract-Today, the manufacturing industry is under pressure to be able to rapidly come up with innovative designs and produce them in much shorter timeframes in order to keep up with growing customer demands and quickly gain new business. One of the ways used to achieve shorter time to markets for new product developments is by using design for manufacturing (DFM) methods to reduce time and energy going into resolving manufacturing based defects. It is also more vital in today's manufacturing industry to make use of DFM methods much earlier in the product development lifecycle in order to prevent potentially known quality defects from happening and save on costs associated with late design changes. This requires enabling a more systemic feedback cycle of production data for the creation of DFM knowledge repositories as well as overcoming some wider knowledge sharing barriers across the organization. This investigation focuses on how the communications of manufacturing knowledge from production data is affected by factors within the overall organization. The investigation is one of the few that consider the influence of organizational factors on the communication of engineering knowledge as well as the related knowledge sharing barriers. The project is carried out empirically with a large UK based aerospace manufacturing company by gathering data primarily from observations and interviews. This paper presents the findings followed by discussions for improving DFM Knowledge Management in the aerospace industry.

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Towards zero-defect manufacturing (ZDM)—a data mining approach

Cited by 114 publications

References 45 publications

Logistics 4.0 Solution-New Challenges and Opportunities

Logistics 4.0 Solution-New Challenges and Opportunities

Intelligent Predictive Maintenance (IPdM) for Elevator Service- Through CPS, IOTaS and Data Mining

Improving Design for Manufacturing implementation in knowledge intensive collaborative environments: An analysis of organisational factors in aerospace manufacturing

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