dc.description.abstract | 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. | nb_NO |