An Optical Inspection System for the Solder Balls of BGA using Support Vector Machine Classification
In this paper, the Support Vector Machine (SVM) is utilized to classify the defective solder ball of ball grid array (BGA) so as to promote the accuracy and efficiency of the optical inspection. The terms of inspections include the ball displacement, the ball over scale, the ball undersize, the ball absence, and ball bridging. There are two step of the SVM processing: the training step and the real testing step. The SVM is more efficiency because of the calculation in testing step is no relation to the number of the input vector. As this point, SVM is fit to build up the high dimension input recognize system such as BGA inspection. From the real testing results, the Support Vector Machine is proved to properly identify and classify the shape defects. The two dimensional BGA optical inspecting system is implemented by Visual Basic as the developing tool incorporated with the Halcon's function which is the database of the image processing on Windows operation system. For the development of the processing procedure of the automatic optical inspecting system with the designed lighting system, the precise geometrical information of the solder ball is evaluated by the sub-pixel method to identify the shape of solder ball and its location under the BGA board is offset and rotated at any angle.
Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line
Quality assessment in many production processes typically relies on manual inspections due to a lack of reference data and an effective method to classify defects in a systematic way. Recently, the real-time, automated approach for product quality assessment has been regarded an important aspect for smart manufacturing applications, such as in the automotive industry. In this research, we suggest a framework to pre-process the data for SVM-based decision making and implement the algorithm in the self-evolving quality assessment system based on the adaptive support vector machine (ASVM) model. An adaptive process is a feedback control that ensures the effectiveness of the support vector machine (SVM) algorithm over time and enables the improvement of SVM-based quality assessment in the real production process. Next, an industrial case study of a primer-sealer dispensing process in a sunroof assembly line of an automobile is illustrated with statistical analysis to verify and validate the applicability and effectiveness of the proposed ASVM-based quality assessment system. Defective patterns are then analyzed using an infrared thermal image of primer-sealer dispensing in a manufacturing process, which contains multi-modal data of dimensional information and temperature deviation from the dispending patterns in our study.
Error-burst detection with tandem CRCs
The authors investigate the efficacy of using two different cyclic redundancy check (CRC) codes in tandem to increase error-burst detecting capability. For a set of pairs of CRCs which are used in standards, it is found that the guaranteed detectable burst length is less than the sum of the individual guaranteed detectable burst lengths, but not much less. Thus strengthened CRC codes can readily be obtained using existing devices. >