Non-referential, self-compared shape defect inspection for bond pads with deformed shapes

Tsai, Du‐Ming; Su, Yan-Jheng

doi:10.1080/00207540701549590

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Li et al (2018) propose a stacking method for an OP system that combines two-dimensional and three-dimensional vision. Additionally, we found some studies about the similarity of the shapes of the products (Sun 2000;Tsai and Su 2009) and the size and the shape of the shelves (Zou et al 2017). None of the mentioned studies considered weight precedence constraints.…”

Section: Slap With Shape Constraintsmentioning

confidence: 99%

Improving Order-picking Operations with Precedence Constraints through Efficient Storage Location Assignment

Trindade

Sousa

Moreira

2021

UPjeng

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This paper is inspired by a manual picking retail company where shape and weight constraints affect the order-picking process. We proposed an alternative clustering similarity index that considers the similarity, the weight and the shape of products. This similarity index was further incorporated in a storage allocation heuristic procedure to set the location of the products. We test the procedure in a retail company that supplies over 191 stores, in Northern Portugal. When comparing the strategy currently used in the company with this procedure, we found out that our approach enabled a reduction of up to 40% on the picking distance; a percentage of improvement that is 32% higher than the one achieved by applying the Jaccard index, a similarity index commonly used in the literature. This allows warehouses to save time and work faster.

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Section: Slap With Shape Constraintsmentioning

confidence: 99%

Improving Order-picking Operations with Precedence Constraints through Efficient Storage Location Assignment

Trindade

Sousa

Moreira

2021

UPjeng

View full text Add to dashboard Cite

show abstract

“…In addition, the Fourier descriptors are simple to normalize data and are invariant to shape's rotation, scaling, translation, and changes of different starting points of boundary [21,22]. This Fourier descriptors scheme can achieve self-comparison between the testing image and the rebuilt one and do not need referential images and template matching processes [23]. Derivatives of the Fourier descriptors as shape descriptors improved the shape classifi cation and retrival performance through combining with wavelet transform and multiple resolutions [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the edge point number of testing objects are distinct in applications of industrial inspection, we select a fi xed percentage of the total number of edge points as the recovery points. The fi xed percentage is called retaining rate Pr and the number of recovery points is determined as: (6) In general, the Pr values within 5% ~ 10% can rebuild an approximated shape of a boundary [23]. If an object with a large indentation needs to be recovered, a less retaining rate will be selected.…”

Section: Image Modelingand Analysismentioning

confidence: 99%

Effective image models for inspecting profile flaws of car mirrors with applications

Peter¹,

Lin²,

Lin³

2020

J Appl Eng Science

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Since car mirrors are standard accessories with cars, the demand for car mirrors is growing and manufacturers also pay more emphasis on the increase of product quality. Common appearance fl aws of car mirrors include: scratches, bubbles, pinholes causing surface fl aw type and burrs,damaged edges causing profi le fl aw type. Currently, the inspection tasks are conducted by human inspectors. Since the profi le fl aws will cause structural damages of car mirrors and reduce ability to withstand stress, the level of damage suffered even more than the surface fl aws. In addition, the angle diversity of capturing images makes it hard to implement automatic optical inspection. Therefore, this study develops an automated profi le fl aw detection system for car mirrors to replace visual inspection personnel. This study proposes a self-contrast defect detection method for the profi le fl aw inspection of car mirrors. It is not required to provide a standard fl awless sample in detection process and derive information compared with testing samples. The proposed method fi rst extracts the contour information of the testing image by Fourier descriptors. Then, after some middle and high-frequency coeffi cients were fi ltered out, an approximated contour image can be rebuilt from the Fourier domain for comparing with the testing image. Finally, the fl aw districts are easily separated by image subtraction. Experimental results demonstrate that the fl aw inspection rate reaches 85.05%, and the incorrect alert rate is smaller than 0.07%, and the correct classifi cation rate is up to 97.47%.

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“…In addition, it automatically increases the inspection time [5]. Tsai et al proposed a non-referential defect detection approach for bond pads [11]. This approach restores the shape of the bond pads using Fourier image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest

Yuk

Park

et al. 2018

Applied Sciences

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Featured Application: The main contribution of this work is to propose an inspection method using image data generated at the actual manufacturing process. This proposed method can help printed circuit board (PCB) manufacturers more effectively detect defects, such as scratches and improper etching, in an automated optical inspection (AOI). Moreover, the proposed method of this work can be also applied to the field of dermatology, where it has to detect skin diseases, as well as in PCB inspection.Abstract: With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB), which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF), then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE) map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method.

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Non-referential, self-compared shape defect inspection for bond pads with deformed shapes

Cited by 5 publications

References 21 publications

Improving Order-picking Operations with Precedence Constraints through Efficient Storage Location Assignment

Improving Order-picking Operations with Precedence Constraints through Efficient Storage Location Assignment

Effective image models for inspecting profile flaws of car mirrors with applications

Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest

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