• 마이크로전자및패키징학회지
• /
• 제27권3호
• /
• pp.35-39
• /
• 2020

Nonwet (Head in Pillow) 불량은 SMT(surface mount technology) 공정 불량 유형 중 하나로 이 불량은 solder paste misalign, reflow 조건, package warpage, package ball size 등과 같은 인자에 따라 불량이 발생을 한다. 이에 본 논문은 Nonwet 발생 인자 중 ① reflow 조건 ② package ball & solder paste misalign ③ package ball 크기 type에 대한 인자를 선정하여 nonwet 실험을 진행하였다. 먼저 reflow 조건의 경우 soldering 시간이 길 경우 nonwet risk가 증가를 하나, reflow 공정에 N₂를 적용할 시 solder ball 산화 억제에 따른 nonwet 개선을 확인 할 수 있었다. 또한 package ball과 solder paste misalign 발생 시 ball과 paste의 접촉 깊이가 20 ㎛ 이하의 경우 nonwet에 취약 했으며, package ball 체면적이 작을수록 nonwet 관점 개선됨을 확인 할 수 있었다.

• Journal of Information Processing Systems
• /
• 제19권6호
• /
• pp.745-755
• /
• 2023

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.

• Journal of Welding and Joining
• /
• 제13권2호
• /
• pp.42-52
• /
• 1995

In the evaluation of aging degradation on the structural materials based on the fracture mechanics, the detection and size prediction of defect are very important. Aiming at nondestructive detection and size prediction ol defect with high accuracy and resolution, therefore, an lnduced Current Focusing Potential Drop(ICFPD) technique has been developed. The principle of this technique is to induce a focusing current at an exploratory region by an induction wire flowing an alternating current(AC) that is a constant ampere and frequency. Defects are assessed with the potential drops that are measured the induced current on the surface of metallic material by the potential pick-up pins. In this study, the lCFPD technique was applied for evaluating the location and size of the surface crack and blind crack made in plate specimens, and also for detecting the defects existing in valve, a field component, that were developed by SCC etc. during the service. The results of this present study show that surface crack and blind crack are able to defect with potential drop. these cracks are distinguished with the distribution of potential drop, and the crack depths can be estimated with each normalized potential drop that are parameters estimating the depth of each type crack. In the field component, the defects estimated by experiment result correspond with those in the cutting face of the measuring point within a higher sensitivity.

• 대한기계학회논문집
• /
• 제16권4호
• /
• pp.819-827
• /
• 1992

본 연구에서는 전술한 바와 같은 반복되는 압연부하에 의한 롤의 피로특성을 규명하기 위해 열연공장의 조압연기중 표본작업 롤의 피로특성을 규명하기 위해 열연 공장의 조압연기중 표본 작업 롤을 선정하고 일정기간의 사용이력을 조사하였으며, 이 를 토대로 롤내부 온도 및 열응력 계산을 수행하여 압연중 발생되는 피로 균열의 크기 를 정량화 시켜보았다. 또 표면 균열관찰 실험 및 롤 표면흠 대응 실험을 통해 피로 균열이 압연판의 표면품질에 어떠한 영향을 미치는지 조사해보았다.

• 한국표면공학회지
• /
• 제51권1호
• /
• pp.1-10
• /
• 2018

Anodic oxidation treatment of metals is one of typical surface finishing methods which has been used for improving surface appearance, bioactivity, adhesion with paints and the resistances to corrosion and/or abrasion. This article provides fundamental principle, type and characteristics of the anodic oxidation treatment methods, including anodizing method and plasma electrolytic oxidation (PEO) method. The anodic oxidation can form thick oxide films on the metal surface by electrochemical reactions under the application of electric current and voltage between the working electrode and auxiliary electrode. The anodic oxide films are classified into two types of barrier type and porous type. The porous anodic oxide films include a porous anodizing film containing regular pores, nanotubes and PEO films containing irregular pores with different sizes and shapes. Thickness and defect density of the anodic oxide films are important factors which affect the corrosion resistance of metals. The anodic oxide film thickness is limited by how fast ions can migrate through the anodic oxide film. Defect density in the anodic oxide film is dependent upon alloying elements and second-phase particles in the alloys. In this article, the principle and mechanisms of formation and growth of anodic oxide films on metals are described.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1996년도 춘계학술대회 논문집
• /
• pp.24-29
• /
• 1996

The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

• 한국정보디스플레이학회:학술대회논문집
• /
• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
• /
• pp.1159-1162
• /
• 2004

A modeling of the nucleation and dynamical behavior of defects from an inhomogeneous surface configuration using fast Q-tensor method is realized. On modeling the defect nucleation and dynamics, A fast Q-tensor method is applied. From the numerical modeling, we confirmed that surface inhomogeneity which makes strong strain energy in the local liquid crystal director field could cause defects. Experimental result has compared with numerical modeling in order to verify the simulation of the defect nucleation.

• 항공우주기술
• /
• 제11권2호
• /
• pp.26-32
• /
• 2012

본 연구는 위성 열제어 주요 구성품중 하나인 SSM(이차면경) 표면에서 나타난 수상돌기(Dendrite) 현상에 대한 고장탐구 수행 내역과 그 결과를 정리한 것이다. 본 고장탐구 시편 시험을 통하여 SSM 표면의 은도금 층은 황 또는 염소 화합물이 함유된 환경에 장기간 직접 노출시키는 경우, 은도금 층이 황화 변색되는 현상이 나타날 수 있음을 확인하였으며 SSM 수상돌기(Dendrite) 현상을 방지하기 위해서는 가황 처리된 고무 패드와 직접 접촉하지 않도록 격리 보관할 필요가 있음을 알 수 있었다.

• 대한기계학회논문집A
• /
• 제31권6호
• /
• pp.651-658
• /
• 2007

A new tree-structured neural network classifier is proposed for the automatic real-time inspection of cold-rolled steel strip surface defects. The defects are classified into 3 groups such as area type, disk type, area & line type in the first stage of the tree-structured neural network. The defects are classified in more detail into 11 major defect types which are considered as serious defects in the second stage of neural network. The tree-structured neural network classifier consists of 4 different neural networks and optimum features are selected for each neural network classifier by using SFFS algorithm and correlation test. The developed classifier demonstrates very plausible result which is compatible with commercial products having high world-wide market shares.

• 전기학회논문지
• /
• 제66권7호
• /
• pp.1100-1104
• /
• 2017

A detector-based approach shows the limited performances for the defect inspections such as shallow fine cracks and indistinguishable defects from background. Deep learning technique is widely used for object recognition and it's applications to detect defects have been gradually attempted. Deep learning requires huge scale of learning data, but acquisition of data can be limited in some industrial application. The possibility of applying CNN which is one of the deep learning approaches for surface defect inspection is investigated for industrial parts whose detection difficulty is challenging and learning data is not sufficient. VOV is adopted for pre-processing and to obtain a resonable number of ROIs for a data augmentation. Then CNN method is applied for the classification. Three CNN networks, AlexNet, VGGNet, and mofified VGGNet are compared for experiments of defects detection.