Tool condition monitoring is gaining a parallel development with the advancement of automatic manufacturing processes in the last thirty years due to the increasing need for improvement of product quality. The advances of digital image processing techniques used in tool condition monitoring are an important research interest due to the improvement of machine vision system, computing hardware and non-tactile application. In this paper, a review of development of digital image processing techniques in tool condition monitoring is discussed and finally a conclusion is drawn about required systematic research in this field.

Application of digital image processing in tool condition monitoring: A review

Solar power has become an attractive alternative of electricity energy. Solar cells that form the basis of a solar power system are mainly based on multicrystalline silicon. A set of solar cells are assembled and interconnected into a large solar module to offer a large amount of electricity power for commercial applications. Many defects in a solar module cannot be visually observed with the conventional CCD imaging system. This paper aims at defect inspection of solar modules in electroluminescence (EL) images. The solar module charged with electrical current will emit infrared light whose intensity will be darker for intrinsic crystal grain boundaries and extrinsic defects including micro-cracks, breaks and finger interruptions. The EL image can distinctly highlight the invisible defects but also create a random inhomogeneous background, which makes the inspection task extremely difficult. The proposed method is based on independent component analysis (ICA), and involves a learning and a detection stage. The large solar module image is first divided into small solar cell subimages. In the training stage, a set of defect-free solar cell subimages are used to find a set of independent basis images using ICA. In the inspection stage, each solar cell subimage under inspection is reconstructed as a linear combination of the learned basis images. The coefficients of the linear combination are used as the feature vector for classification. Also, the reconstruction error between the test image and its reconstructed image from the ICA basis images is also evaluated for detecting the presence of defects. Experimental results have shown that the image reconstruction with basis images distinctly outperforms the ICA feature extraction approach. It can achieve a mean recognition rate of 93.4% for a set of 80 test samples.

Defect Detection in Solar Modules Using ICA Basis Images

Defect detection of solar cells in electroluminescence images using Fourier image reconstruction

Control charts have been proposed for use with industrial or medical image data from machine-vision systems in a number of ways, several of which are examined here. There are differences between the standard uses of control charts and the ways they are ..

A Review and Perspective on Control Charting with Image Data

Bearing defect inspection based on machine vision

Purpose – The detection of invisible micro cracks (μ‐cracks) in multi‐crystalline silicon (mc‐si) solar wafers is difficult because of the wafers' heterogeneously textured backgrounds. The difficulty is twofold. First, invisible μ‐cracks must be visualized to imaging devices. Second, an image processing sequence capable of extracting μ‐cracks from the captured images must be developed. The purpose of this paper is to reveal invisible μ‐cracks that lie beneath the surface of mc‐si solar wafers.Design/methodology/approach – To solve the problems, the authors first set up a near infrared (NIR) imaging system to capture images of interior μ‐cracks. After being able to see the invisible μ‐cracks, a region‐growing flaw detection algorithm was then developed to extract μ‐cracks from the captured images.Findings – The experimental results showed that the proposed μ‐cracks inspection system is effective in detecting μ‐cracks. In addition, the system can also be used for the inspection of silicon solar wafers for s...

Micro crack detection of multi‐crystalline silicon solar wafer using machine vision techniques

Flaw detection of cylindrical surfaces in PU-packing by using machine vision technique

The vision-based of automated tool wear monitoring systems are very important and efficient for unmanned machining systems. This research is use the machine vision inspection technique to automatic tool wear monitoring measurement of different coated drills. The tool wear images are captured using a machine vision system incorporating with an effective extract vertex algorithm based on subpixel edge detector and Gaussian filter is presented. Finally, Statically Process Control (SPC) technique is applied to detect vertices. The results show that the proposed algorithm is an effective method for the different coated drilling factor is recognized to make the most significant contribution to the over all performance. The TiAlN-coated drilling has the least wear rate amongst these coated drilling cutters and has the longest tool life in this experiment.

Vision-based automatic tool wear monitoring system

In this paper, the measurements along with color image segmentation to detect all possible defects in BGA (ball grid array) type PCB (printed circuit boards) were presented. We use feature extraction and analysis as well as BPN (back-propagation neural) network classification to classify the detected defects. There are variable defects to be detected and classified including stain, scratch, solder-mask, and pinhole. The experimental results show that the proposed algorithm is successful in detecting and classifying the defects on gold-plating regions. The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method, using unsophisticated and economical equipment, is also verified in providing highly accurate results with a low error rate.

http://140.134.32.129/cvision/paper/2008bga.pdf

Yih-Chih Chiou

Papers

Micro crack detection of multi‐crystalline silicon solar wafer using machine vision techniques

Flaw detection of cylindrical surfaces in PU-packing by using machine vision technique

Vision-based automatic tool wear monitoring system

The feature extraction and analysis of flaw detection and classification in BGA gold-plating areas

Intelligent segmentation method for real-time defect inspection system