Purpose -The detection of invisible micro cracks (m-cracks) in multi-crystalline silicon (mc-si) solar wafers is difficult because of the wafers' heterogeneously textured backgrounds. The difficulty is twofold. First, invisible m-cracks must be visualized to imaging devices. Second, an image processing sequence capable of extracting m-cracks from the captured images must be developed. The purpose of this paper is to reveal invisible m-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 m-cracks. After being able to see the invisible m-cracks, a region-growing flaw detection algorithm was then developed to extract m-cracks from the captured images. Findings -The experimental results showed that the proposed m-cracks inspection system is effective in detecting m-cracks. In addition, the system can also be used for the inspection of silicon solar wafers for stain, pinhole, inclusion and macro cracks. The overall accuracy of the defect detection system is 99.85 percent. Research limitations/implications -At present, the developed prototype system can detect m-crack down to 13.4 mm. The inspection resolution is high but the speed is low. However, the limitation on inspection speed can easily be lifted by choosing a higher resolution NIR camera. Practical implications -Generally, this paper is a great reference for researchers who are interested in developing automatic optical inspection systems for inspecting solar wafer for invisible m-cracks. Originality/value -The research described in this paper makes a step toward developing an effective while low-cost approach for revealing invisible m-crack of mc-si solar wafers. The advantages provided by the proposed system include excellent crack detection sensitivity, capability of detecting hidden subsurface m-cracks, and low cost.

No abstract

The purpose of this research is to use the visual inspection technique for the automatic tool wear measurement of different coated drills. The tool wear images with the different coated drilling are captured using a machine vision system incorporating with an effective vertex detection algorithm based on subpixel edge detector and Gaussian filter is presented. 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. All drilling tests were carried out under dry cutting conditions without any coolant being used, The TiAlN-coated drilling has the least wear rate amongst these coated drilling cutters and has the longest tool life in this experiment