| Flexible printed circuit (FPC) is a kind of printed circuit board that is made of flexiblematerial such as polyurethane and polyester. It can be bent, folded, rolled stretched and"moved freely" within a three-dimension space. Due to the above characteristics, FPC canconnect PCB and parts, which makes the product to fit the requirement of small size.Therefore, FPC is widely used for aviation, aerospace, military, automobile, consumingelectronics and so on. So the quality control plays a crucial role in the FPC producing process.The paper presents inspecting work based on machine vision. It contains four aspect, there isrespectively inspecting target location, searching inspecting area, extraction of feature, patternrecognition. On the basis of previous research, considering realistic application, the inspectingwork is discussed in sequence and the corresponding solutions have been given, and theresearch content and innovation work are as in the following:1) The method of correcting position error based on mutual information entropy isproposed in this paper. The realistic product is always different from standard template sincethe FPC manufacturing material is easily affected by thermal expansion and contraction.Correcting matching error is crucial work during manufacturing process. The mutualinformation entropy (MIE) is applied on evaluating the correlation between the images. TheMIE increases and reach maximum when the referent and inspecting FPC solders are alignedwith the same place. Moreover, The MIE in some image's region which contains more greyinformation can react to position error strongly. According to the above principle, the MIEand key region feature is applied to searching best optimal correction parameters to improvepositioning accuracy. The method is verified by a simulation and applied to the inspectingsystem. It demonstrates that the method can correct the position error more effectivelycompare with Hough transformation method and affine transformation method. The matchingaccuracy can reach16.6micron.2) The texture gradient directionality distribution entropy is proposed to describe thedistribution of inspecting objective contour's directionality.When solder deformation accurs,the method is applicated in extracting solder's contour feature and direcionaity feature. Finally,the contour similarity function is established. The valuable contour information is reversedand adopted to match inspecting area.3) Entropy statistic based on distribution of grey and texture gradient is proposed todescribe texture roughness feature.And it can effectively measure texture roughness.Then themethod is applied to the identification of defect on solder.The algorithm reaches satisfactory results and overcome the shortcoming of the traditional method.4) Directionality is an important visual feature of texture too. The thesis presents a robustimage texture directional measurement method. Circuit's defect can be detected by thetextural direction feature. Radon transformation is mathematical expression based onprojection transformation. During inspecting process it is implemented on circuit image toestablish texture direction characteristic similarity function. Then, some region in imagewhich contains the same texture direction feature is segmented. Furthermore, thedirectionality estimation method is presented. As the circuit is damaged, the directionality isweakened correspondingly.And the radon transformation value's fluctuation can response todirectionality.The detection of circuit's defect is achieved through analysis of the fluctuationon radon transformation value. The inspecting experiment demonstrates the method canextract defective region correctly and get satisfactory result.5) The platform is constructed and evaluated on-line testing. The inspecting resultdemonstrates accurate defect detection with low false alarms, and the efficiency can satisfythe requirement of online and real time inspection. The research work has good theoreticalsignificance and application value.
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