Search results

The purpose of this paper is to apply an on‐line automatic inspection and measurement of surface defect of thin‐film transistor liquid‐crystal display (TFT‐LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method.

By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.

With template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects.

The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accurate results with a low‐error rate.

In terms of sample training, the principles of artificial neural network were used to train the sample detection rate. In sample analysis, character weight was implemented to filter the noise so as to enhance discrimination and reduce detection.

The paper describes how pre‐inspection image processing was utilized in collaboration with the system to excel the inspection efficiency of present machines as well as for reducing system misjudgment. In addition, the measure for improving cell defect inspection can be applied to production line with multi‐defects to inspect and improve six defects simultaneously, which improves the system stability greatly.

Outlines the defects and faults during the design stage that affect building maintenance in Saudi Arabia and their relative degree of importance. Performed a survey of a randomly selected sample of 90 contractors, 30 architectural/engineering firms (A/Es), and 20 owners from the Eastern Province of Saudi Arabia. The survey included 35 defects and the respondents were asked to indicate their degree of importance. The defects were grouped into six groups. The level of importance of the defects and the groups were measured and ranked by their severity index for contractors, owners and A/Es. The following results were obtained: contractors, A/Es and owners generally agree on the ranking of the individual defects; contractors and A/Es agree on the ranking of the defect groups whereas contractors and owners, A/Es and owners do not agree; the construction drawings group of defects was ranked highly by all three parties whereas the architectural design group of defects received a low ranking.

The purpose of this paper is to propose a defect detection method of bare printed circuit boards (PCB) with high accuracy.

First, bilateral filtering of the PCB image was performed in the uniform color space, and the copper-clad areas were segmented according to the color difference among different areas. Then, according to the chaotic characteristics of the spatial distribution and the gradient direction of the edge pixels on the boundary of the defective areas, the feature vector, which evaluates quantitatively the significant degree of the defect characteristics by using the gradient direction information entropy and the uniform local binary patterns, was constructed. Finally, support vector machine classifier was used for the identification and localization of the PCB defects.

Experimental results show that the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids.

Considering the limitations of describing all kinds of defects on bare PCB by using single kind of feature, the gradient direction information entropy and the local binary patterns were fused to build a feature vector, which evaluates quantitatively the significant degree of the defect features.

Laser metal deposition (LMD) is an important additive manufacturing (AM) technology, but the metallurgical defects, such as cracks and porosities, produced in LMD process will seriously affect the mechanical properties of the parts. The purpose of this paper is to propose a novel in-process defects detection method based on infrared scanning for LMD.

The defects detection principle is that, after every three to five layers have been deposited, the optical head of a high-precision infrared two-color pyrometer is driven to scan the defects by measuring the abnormal temperature peaks on the deposited surface. The experiments for verifying the defects detection principle were carried out.

The relationship between the temperature peak value and the dimensions of the defect was analyzed based on the heat conduction theory and curves of temperature peak value versus crack width or diameter of porosity.

This method can effectively improve the detection accuracy and the defects can be precisely located, which can meet the requirement of laser targeting re-melting and elimination of the defects.

The purpose of this study is to understand what health and safety hazards low-income households are subject to by surveying the real conditions of the defective housing of low-income households, and to find improvement strategies. For this purpose, we visited the concentrated areas of the multi-dwelling unit (MDU) (also known as multi-family residential) housing in Jungwon-gu and Sujeong-gu in Seongnam City, Kyunggi-do, one of the representative areas in Korea with a massive distribution of the low-income class. Based on the survey data, the level of housing defects were comparison analyzed per income decile (decile 1, decile 2, deciles 3–4), and per housing location, in the categories of subsidence, cracks in the wall, delamination, water leakage/infiltration, condensation, and contamination. The housing condition per income class was more defective in the decile 2 households rather than in the decile 2 households, and in the substructure more than in the superstructure. Among the six defects, contamination problems, caused by sub-standard living conditions, were the most frequent cases. Structural defects, subsidence and cracks in the wall, were found in the main living areas—the bedrooms and the living rooms. It was confirmed in this study that the conditions of low-income housing are serious, and that it is necessary to explore specific countermeasures in the near future.

Combined defects in ball bearings may be caused during the use or manufacturing process, which can significantly affect their vibration characteristics. The previous defect models in the literature can only describe single defects such as the surface waviness and localized defect. This paper aims to propose an in-depth understanding of radial vibrations of a ball bearing with the combined defect.

A dynamic model for a ball bearing with the combined defect including the surface waviness and localized defect on its races is proposed. The effects of the combined defect sizes on the radial bearing vibrations are investigated. The results from the proposed model considering the combined defect are compared with the available results from the previous methods considering the single defects.

The acceleration amplitude is significantly affected by the surface waviness, localized defect and the combined defect on its races. The effect of the combined defect on the acceleration amplitude is larger than that of the single defect. The amplitude and peak frequency of the spectrum of acceleration for the combined defect increases with the defect sizes. The RMS value of the accelerations for the combined defect increases with the combined defect sizes.

Consequently, the proposed model can predict more accurate and in-depth understanding of the radial vibrations caused by the combined defect in the ball bearing.

Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on adaptive wavelet.

Fabric defects can be regarded as the abrupt features of textile images with uniform background textures. Wavelets have compact support and can represent these textures. When there is an abrupt feature existed, the response is totally different with the response of the background textures, so wavelets can detect these abrupt features. This method designs the appropriate wavelet bases for different fabric images adaptively. The defects can be detected accurately.

The proposed method achieves accurate detection of fabric defects. The experimental results suggest that the approach is effective.

This paper develops an appropriate method to design wavelet filter coefficients for detecting fabric defects, which is called adaptive wavelet. And it is helpful to realize the automation of textile industry.

Development of non‐destructive methodology for detection of arc strike, spatter and fusion type of welding defects which may form on steam generator (SG) tubes that are in close proximity to the circumferential shell welds. Such defects, especially fusion‐type defects, are detrimental to the structural integrity of the SG. This paper aims to focus on this problem.

This paper presents a new methodology for non‐destructive detection of arc strike, spatter and fusion type of welding defects. This methodology uses remote field eddy current (RFEC) ultrasonic non‐destructive techniques and K‐means clustering.

Distinctly different RFEC signals have been observed for the three types of defects and this information has been effectively utilized for automated identification of weld fusion which produces two back‐wall echoes in ultrasonic A‐scan signals. The methodology can readily distinguish fusion‐type defect from arc strike and spatter type of defects.

The methodology is unique as there is no standard guideline for non‐destructive evaluation of peripheral tubes after shell welding to detect arc strike, spatter and fusion type of welding defects.

AN exceedingly careful control of the surface of aero‐engine parts has beyond doubt become an excellent habit both with manufacturers and those who are to use the engine. A crack on a new part, or one which will cause a fatigue failure in work—these are the defects looked for by inspectors during manufacture, overhaul, or repairs. Cracks are very frequent causes of accidents and this fear often underlies the rejection of parts which are only suspect but which might work quite well until normal wear and tear would cause them to exceed permissible tolerances. In many cases, electro‐magnetic examination or etching reveal defects on the surface of engine parts which cannot be defined: in such cases, for the sake of certainty the part is rejected on the ground that it is cracked or made from faulty material.

Castings defects are usually easy to characterize, but to eradicate them can be a difficult task. In many cases, defects are caused by the combined effect of different factors, whose identification is often difficult. Besides, the real non‐quality costs are usually unknown, and even neglected. This paper aims to describe the development of a modular tool for quality improvement in foundries, and its main objective is to present the application potential and the foundry process areas that are covered and taken into account.

The integrated model was conceived as an expert system, designated Qualifound, which performs both qualitative and quantitative analyses. For the qualitative analyses mode, the nomenclature and the description of defects are based on the classification suggested by the International Committee of the Foundry Technical Association. Thus, a database of defects was established, enabling one to associate the defects with the relevant process operations and the identification of their possible causes. The quantitative analysis mode deals with the number of produced and rejected castings and includes the calculation of the non‐quality costs.

The validation of Qualifound was carried out in a Portuguese foundry, whose quality system had been certified according to the ISO 9000 standards. Qualifound was used in every management area and it was concluded that the application had the required technological requisites to provide the necessary information for the foundry management to improve process quality.

The paper presents a successful application of an informatics tool on quality improvement in foundries.