The objective of the paper is to demonstrate a measurement strategy for online inspection of surface defects in products, especially discontinuities which appear in castings after machining. The essence of the proposed online vision inspection system is to provide an automated method for obtaining and analyzing images of the inspected surfaces, to allow an unmistakable and consistent finding of defects and specifying their types. The proposed solution could improve productivity and quality in the manufacturing process especially for the automotive industry and significantly improve automatization in die casting technology. This is one of the most fundamental problems of a research studies in process design for automotive parts such as: handles of transmission systems, cylinder pistons and cylinder front faces in engine bodies, that has significant influence for the manufacturing cost. Proposed by the author an online vision inspection systems are responded to users demands for systems that are easier to use, low-cost, and flexible. Using a combination of a vision hardware and software, the proposed vision system could analyze the images, usually in a fraction of a second. Since the human visual inspection is slow and expensive, a smart technology is an alternative solution for the online inspection. The developed smart technology uses advance vision system with specially designed lighting, an advanced image processing algorithm for defect detection and their classification.
Online Inspection System for Maize Kernel Based on Computer Vision
