Abstract
Quality control plays a crucial role to meet the high and accurate quality of production in many manufacturing industries. The high quality products may become unreliable due to surface defects. Generally, quality control is more important in automotive industry such as in the field of car-body parts manufacturing. The exterior appearance of the car body should be smooth surfaces and edges with flawless nature. In order to build such flawless body parts, surface defect detection system is taken into account. In this paper, an Automated Defect Detection (ADD) system is presented. The design of the ADD system consists of two steps. The first step is considered as a classification system where the given image is classified into defected or non-defected using Gabor expansion with Principal Component Analysis (PCA). The next step is segmentation where the region of defect is identified using local thresholding. The evaluation is performed on raw alloy steel surface and machined surfaces of steel and cast iron. Results prove that the ADD system classify the input image into defect/no defect with 100% accuracy by a simple nearest neighbor classifier and with 94.5% detection accuracy for the segmentation system.
Highlights
The manual techniques for surface defect detection require only trained operators to check corresponding defect criteria
The Automated Defect Detection (ADD) system plays an important role in many industries and several algorithms are involved for automated inspection
A novel ADD system is designed by exploiting Gabor expansion, Principal Component Analysis (PCA) and local thresholding approaches for raw material and machined surface
Summary
The manual techniques for surface defect detection require only trained operators to check corresponding defect criteria. It will always lead to intricacy in surface defect detection. Over the years numerous researches are carried out to improve the defect detection system without human involvement. Intensive research work has been undertaken in the development of automated analysis methods to assist manufacturing industries. Artificial Neural Network based ADD is discussed in [1] for strip steel surface. It employs self adjusted learning rate with back propagation algorithm and diagnoses the middle flaw in strip steel surfaces. The surface roughness is measured and the micro cracks are identified from the scanned electron microscope images by different control techniques
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
