Online vision system for battery FPC connector defect detection based on active shape model template matching

Zhao, Zhuo; Li, Bing; Gao, Fei; Chen, Lei; Xin, Meiting

doi:10.1117/12.2511378

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…FPC is developed using copper-clad laminate (CCL) to create electronics circuit patterns and simultaneously provide mechanical strength to withstand deformation during operation [6]. The quality control process improvement on FPC largely focuses on the use of vision-based inspection technologies to detect defects in the FPC circuit [7]- [9]. Lamination is performed at the last stage of FPC manufacturing to increase the mechanical robustness of FPC [6].…”

Section: Introductionmentioning

confidence: 99%

DSC Assessment on Curing Degree of Micron-scaled Adhesive Layer in Lamination-pressed Flexible Printed Circuit Panels

Kok¹,

Lau²,

Rosli³

2021

JMechE

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Continuous monitoring and optimisation of the lamination process are critical in negating flexible printed circuit (FPC) delamination risk during operation. The main QC inspection criterion of the lamination adhesive’s curing degree is adhesive thickness. However, this method is prone to measurement error due to poor microscopy image definition and the inspector’s measurement parallax error. The feasibility of using thermal characterisation to measure the difference in curing degrees of micron-scaled adhesive layer of laminated FPC was investigated. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used according to IPC standards. Polyimide-epoxy adhesive coverlays were laminated onto both sides of FPC at 120 kgf/cm2 pressure and 180 °C temperature for 120 s. Then, the coverlays were subjected to oven curing at 150 °C for 60 min. The DSC detected a small difference in the curing degree of adhesive layers in the two cured FPC laminated in different laminating-press openings (T1 and T2). T2 had a glass transition temperature (Tg) of 106.5 °C, which was higher than that for T1 (105 °C), thereby suggesting that the former had a higher curing degree than the latter. This result was consistent with the adhesive thickness measurement result of the DSC samples. The adhesive thickness of T2 was smaller (30.97 µm) than that of the T1 (31.76 µm). T2 had a higher curing degree than T1 because of the larger shrinkage percentage. In comparison with DSC, TGA was unable to detect the curing degree difference between the samples because of the undetected weight loss resulting from the adhesive curing.

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Section: Introductionmentioning

confidence: 99%

DSC Assessment on Curing Degree of Micron-scaled Adhesive Layer in Lamination-pressed Flexible Printed Circuit Panels

Kok¹,

Lau²,

Rosli³

2021

JMechE

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“…However, the marking of pad is covered after the silicone cap pasting, resulting in the loss of the reference information, so it cannot be used as a reference either. The external reference method was based on the template matching (Tsai and Huang, 2019;Zhao et al, 2019). For a fixed format of solder BGA, the nondefective BGA was taken as a reference to obtain the coordinates of each ball and then these coordinates were compared with the actual coordinates of the ball in the target image to detect the deviation (Qixin et al, 2005;Jin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Positional deviation detection of silicone caps on FPCB

Yuan

2020

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Purpose The purpose of this paper is to propose a defect detection method of silicone caps positional deviation on flexible printed circuit board (FPCB) of keyboard based on automatic optical inspection. Design/methodology/approach First, the center of silicone caps of target keyboard FPCB image was extracted as feature points for generating the feature image which is used for registration rigidly with the reference feature image generated from the CAD drawings. Then, a flexible image registration method based on the surrounding-control-center B-splines (SCCB) strategy was proposed, which could correct the flexible deformation of the image generated by FPCB substrate while keeping the pasting deviation information about silicone caps unchanged. Finally, on this basis, a nearest neighbor strategy was proposed to detect the positional deviation of silicone caps. Findings Experimental results show that the proposed method can effectively detect the positional deviation defect of silicone caps. The G-mean value of the proposed method is 0.941746, which is 0.3 higher compared to that of similar research. Originality/value This paper presents a method to detect positional deviation defect of silicone caps on keyboard FPCB. Different from the classic B-spline image registration method, the proposed SCCB method used the neighborhood information of the pixel to be registered selectively to calculate the displacement vector needed for its registration, which overcame the problem that the silicone cap pasting deviation information disappears with the correction of the flexible deformation of the image.

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Online vision system for battery FPC connector defect detection based on active shape model template matching

Cited by 2 publications

References 5 publications

DSC Assessment on Curing Degree of Micron-scaled Adhesive Layer in Lamination-pressed Flexible Printed Circuit Panels

DSC Assessment on Curing Degree of Micron-scaled Adhesive Layer in Lamination-pressed Flexible Printed Circuit Panels

Positional deviation detection of silicone caps on FPCB

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