Industrial inspection is one of the crucial tasks to ensure quality conformance of products. The inspection tasks can be done by using several methods like non-scaled goot go gauging, measuring instruments, or advanced non-touching tools. In this research visual inspection using a developed optical system is conducted. One of the aims of this research is to design an on line visual inspection system that is capable to test geometrical quality characteristics of 2-D machined products. The design process includes developing an economical optical system to acquire inspected product’s images. Image processing tools are utilized to deal with the product image; and extract features of its geometrical characteristics. A neural network-based methodology is developed and applied to decide whether the product conforms to pre-specified tolerances. The results of the developed methodology are compared to some statistics based visual approaches from the literature. The results show the goodness of the system as an automated visual inspection system and prove its superior performance with respect to other methods.
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