Data mining application for real-time distributed shop floor control

Ben‐Arieh, David; Chopra, Mukesh; Bleyberg, M.Z.

doi:10.1109/icsmc.1998.725075

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…There have been various attempts to use learning in scheduling problems [105][106][107][108][109]. Proposed methods for acquisition of scheduling rules by using inductive-learning techniques were applied to flow-shop scheduling problems [110][111][112][113][114][115], jobshop scheduling problems [116][117][118][119][120][121], and flexible manufacturing systems scheduling problems [122][123][124][125][126][127][128][129]. Experimental results indicated that effective scheduling can be achieved by applying the proposed methods.…”

Section: Applications Of Machine-learning Techniques In Manufacturingmentioning

confidence: 99%

Machine-learning techniques and their applications in manufacturing

Pham

Afify

2005

Proceedings of the Institution of Mechanical Engineers, Part B:

140

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Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.

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Section: Applications Of Machine-learning Techniques In Manufacturingmentioning

confidence: 99%

Machine-learning techniques and their applications in manufacturing

Pham

Afify

2005

Proceedings of the Institution of Mechanical Engineers, Part B:

140

View full text Add to dashboard Cite

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SAGE: An Ambient Intelligent Framework for Manufacturing

Hynes¹,

Monaghan²,

Thai³

et al. 2006

IFAC Proceedings Volumes

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To profit in today's market, small manufacturers must be efficient and agile to minimise cost and to meet variable market demand. This paper discusses an envisaged Semantic, Ambient, Generic and Extensible framework (SAGE) focused on the manufacturing environment. SAGE is based on the ambient intelligence philosophy which means it is focused on the human actor with the aim of aiding them to optimise their companies manufacturing processes. In order to achieve this goal SAGE combines several technologies: semantic reasoning, multi-modal communication and context-aware technologies within a Service Oriented Architecture (SOA). SAGE provides the manager with intuitive access to the real-time data describing the status of the shop floor environment and the ability to automate a response. Two manufacturing processes are targeted for systemic innovation: shop floor control and machine maintenance.

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Databases Modeling of Engineering Information

2009

Database Technologies

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Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

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Data mining application for real-time distributed shop floor control

Cited by 3 publications

References 4 publications

Machine-learning techniques and their applications in manufacturing

Machine-learning techniques and their applications in manufacturing

SAGE: An Ambient Intelligent Framework for Manufacturing

Databases Modeling of Engineering Information

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