scholarly journals Surface Defect Detection in a Tile using Digital Image Processing: Analysis and Evaluation

2015 ◽  
Vol 116 (10) ◽  
pp. 33-35 ◽  
Author(s):  
Foram Sanghadiya ◽  
Darshana Mistry
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Defect Detection ◽  
Digital Image ◽  
Surface Defect ◽  
Analysis And Evaluation ◽  
Surface Defect Detection

scholarly journals Research on Surface Defect Detection of Aluminum based on Image Processing

2020 ◽  
Vol 25 ◽  
pp. 822-825
Author(s):  
Xuemin Liu ◽  
Ce Bian ◽  
Di Yin ◽  
Yuxuan Zhu ◽  
Yasheng Yuan ◽  
...  
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

A Review Paper on Product Surface Defect Detection of Ironing Process

2016 ◽  
Vol 836 ◽  
pp. 147-152
Author(s):  
Akhmad Faizin ◽  
Arif Wahjudi ◽  
I. Made Londen Batan ◽  
Agus Sigit Pramono
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Forming Process ◽  
Metal Forming Process ◽  
Surface Defect Detection ◽  
Product Surface ◽  
Processing Techniques

The quality of product of manufacturing industries depends on dimension accurately and surface roughness quality. There are many types of surface defects and levels of surface roughness quality. Ironing process is one type of metal forming process, which aims to reduce the wall thickness of the cup-shaped or pipes products, thus increasing the height of the wall. Manually surface inspection procedures are very inadequate to ensure the surface in guaranteed quality. To ensure strict requirements of customers, the surface defect inspection based on image processing techniques has been found to be very effective and popular over the last two decades. The paper has been reviewed some papers based on image processing for defect detection. It has been tried to find some alternatives of useful methods for product surface defect detection of ironing process.

scholarly journals Vehicle Components Defect Detection via Machine Vision

2020 ◽  
pp. 131-137
Author(s):  
Armen Petrosyan
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Machine Vision ◽  
Digital Image Processing ◽  
Defect Detection ◽  
Digital Image ◽  
Production Lines ◽  
Vehicle Components

The task of quality control and automatic defect detection on production lines is one of the most important tasks of machine vision and digital image processing (DIP). This article provides one example of solving such a problem using various DIP algorithms.

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