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Solder Paste Inspection and 3D Shape Estimation Using Directional Led Lightings

Solder Paste Inspection and 3D Shape Estimation Using Directional Led Lightings


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About the Book

This dissertation, "Solder Paste Inspection and 3D Shape Estimation Using Directional LED Lightings" by Ming-hei, Chu, 朱銘熙, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Solder Paste Inspection and 3D Shape Estimation Using Directional LED Lightings Submitted by CHU Ming Hei for the degree of Master of Philosophy at The University of Hong Kong in Dec 2007 In this thesis, a novel methodology for solder paste inspection and 3D shape estimation using directional LED lightings is presented. The proposed methodology is based on a specially designed directional LED lighting to highlight the geometrical features of the solder paste block. Solder paste inspection and shape estimation are then carried out based on the highlighted features. The aim of this research is to develop a methodology to inspect solder paste blocks and estimate their shapes. To achieve these objectives, a special directional LED lighting is designed to highlight the geometrical features of the blocks. Under the designed lighting, solder paste images with highlighted features are taken by a digital camera. The captured images are then processed and used to inspect the printing quality and estimate the shape of the solder paste blocks. To carry out quality inspection, the processed solder paste images are passed to a fuzzy system for Solder Paste Quality Scoring. First, a number of features are extracted as the inputs for the fuzzy system. The quality scores of the solder paste blocks are then calculated by the fuzzy system based on a set of fuzzy rules. Neural-fuzzy training can be applied to the proposed fuzzy system for fine-tuning the behavior according to the users' preference. I On the other hand, the processed solder pastes images are given to a Shape Estimation System for estimating the the shape of the solder paste. The Shape Estimation System consists of two parts: shape estimation algorithm and parameter optimization. The shape estimation algorithm uses a set of parameters to estimate the shape. The set of parameters includes the processed solder paste images and the physical properties of the solder paste block, such as slope of edges, etc.. The estimation process is then carried out by calculating a set of surface heights for the solder paste block. The estimated shape of the solder paste block is represented by the calculated set of surface heights. To improve the accuracy of the estimation, parameter optimization can be carried out to search for an optimal set of algorithm parameters using the actual solder paste shape. Experimental results show that the proposed methodology gives high accuracy in the solder paste inspection. The estimated shape from the 3D shape estimation system is acceptable. In the evaluation of the fuzzy system for Solder Paste Quality Scoring, the proposed system is tested with different types of solder paste blocks. Results show that the fuzzy system gives high inspection accuracy towards different types of solder paste blocks. In addition, experiments show that the neural-fuzzy training can adjust the fuzzy system according to the users' preference. For the Shape Estimation System, the proposed algorithm can give acceptable estimated shape towards different types of solder paste block. The evaluation results also show that the parameter optimization can find sets of optimal algorithm parameters which can give estimated shape with higher accuracy. II DOI: 10.5353/th_b3963417 Subjects: Solder p


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Product Details
  • ISBN-13: 9781361471333
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 176
  • Weight: 699 gr
  • ISBN-10: 1361471336
  • Publisher Date: 27 Jan 2017
  • Binding: Hardback
  • Language: English
  • Spine Width: 11 mm
  • Width: 216 mm


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