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New Approaches for Automated Intelligent Quality Inspection System Integration of 3D Vision Inspection, Computational Intelligence, Data Mining and RFID Technology

Yu, Quan
Doctoral thesis
Åpne
Fulltext not available (Låst)
Permanent lenke
http://hdl.handle.net/11250/284333
Utgivelsesdato
2015
Metadata
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Samlinger
  • Institutt for maskinteknikk og produksjon [3255]
Sammendrag
Product quality is one of the critical issues for a company to be competitive in the

global market. Quality inspection is an essential step in quality control process, to

examine the semi or final products for the following production process, prevent the

interruption of the manufacturing process and reduce the economic loss. Defected

final products will lower the loyalty of customers and competitiveness in the market;

while defected semi products will affect the following production process even

damage the manufacturing devices, thereby leading to the economic loss for the

company. With the requirements of inspection speed and accuracy, as well as of

special production conditions, sensor based quality inspection has been well

developed, which also enables the automated quality inspection combining with data

analysis and artificial intelligence techniques. Automated, smart quality inspection

is developed to meet the need for accurate, fast and objective quality inspection.

Artificial intelligence or data mining techniques are generally applied to achieve the

computer aided decision support or decision making.

This thesis presents a framework of automated intelligent quality inspection

system, integrating the structured light based 3D vision system, computational

intelligence/data mining approaches and RFID technology. The structured light

system (SLS) is applied as the data acquisition system in the framework, which

obtains the point cloud of the inspected product. The quality inspection system

consists of two subsystems. The first one is matching based quality inspection

involving the point cloud processing, 3D point cloud template matching and

applications of computational intelligence during these processes, such as the 𝑘-d

tree searching, particle swarm optimization for the automated matching process;

while the other is data mining based quality inspection involving the feature

extraction, data mining modelling and classifier evaluation. Typical data mining

methods such as decision tree, artificial neural networks (ANN) and support vector

machine (SVM) are all studied for the data mining based inspection case. Moreover,

as the integration system of the framework, RFID system is introduced in this thesis

and the combination with the quality inspection system is also studied.

The outcome of the thesis can be applied in production line in manufacturing

industry and combining with the WPM or MES system.
Utgiver
NTNU
Serie
Doctoral thesis at NTNU;2015:35

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