Defects classification

The major drawback of this identification method, as used to date, is that only a part of the useful information contained into original Bscan image, i.e. segmented Bscan image, is used for defect characterization. Moreover, it requires the availability of defect classification information (i.e. if the defect is volumetric or planar, e.g. a crack or a lack of fusion), which, generally, may be as difficult to obtain as the defect parameters themselves. Therefore, we... 

The geometrical measurements previously extracted help the making decision system to decide for example whether the defect is linear or not. This defect discrimination into two categories is considered as a first attempt for defect classification. To this end, we define a linearity ratio (Ri) Rl =Length / width. If Rl is equal or near to "1", the defect is volumic, otherwise it is a linear defect. 

Carbides Cemented Carbide Producers Association 712 Lakewood Center North Cleveland, Ohio 44107 Standards Developed bj Cemented Carbide Producers Association ie, standard shapes, sizes, grades, and designations and defect classification. 

Y. Gotkis, Post-CMP Surface Defectivity Classification, Origin, and Defect Reduction, IPEC Planar PACRIM Chemical Mechanical Planarization Symposium, Hsin Chu, Taiwan Sept. 30-Oct. 1, 1997. 

With PVDF the idea of defect classification was used to define a set of boundary conditions that satisfy the geometry of the ct relaxation in the a-phasc... 

Most structural materials are susceptible to a wide range of defects. Any flaw alters the behavior of a structure, even if only minutely. The larger the flaw the more it reduces the useful properties of the material. One of the challenges in modem materials engineering is defect reduction. Defect reduction involves defect detection, defect source determination and mechanisms and defect elimination. There is no single method of detect review that can fully characterize every defect each defect classification method has its own strengths. 

Provide information on any adhesives used for blistered packages. Defect classification data should be considered. Permeation and leaching/migration test-... 

One lot of 25 wafers has been polished on two different oxide CMP tools using two different oxide CMP slurries. The 13 wafers polished on the second CMP tool show zero final test yield. You have inspected the wafers and even did defect classification and have not seen any difference at the time and still see no difference in revisiting all the data you have collected. What could have happened Find and explain two different possible scenarios. 

What is the benefit of defect classification Assuming you being an R D engineer working on the next technology process, would you use ADC Give the reasons and the consequences of your choice. 

Automatic-defect classification. Available at http //www.ornl.gov/sci/ismv/research industrial adc.shtml. 

Provide information on any adhesives used for blistered packages. Defect classification data should be considered. Permeation and leaching/ migration testing should be conducted and reported. (Information in a DMF or data from studies conducted by the fabricator may be sufficient.)... 

The tendency to have a pack component specification which is all-embracing is also changing. A simpler procedure utilises a series of information documents which lay out the procedures that a supplier has to follow for selective package forms, i.e. glass bottles, plastic bottles, laminates, labels, collapsible tubes, etc. It is then possible to have an abbreviated specification document which covers critical, major and minor defect classifications, advice on delivery and identification, and basic information on the material to be employed, etc. The specification therefore cross-references to its respective information (component manual) document and becomes considerably simplified in terms of both layout and detail. This is particularly important now that specifications are being computerised in conjunction with stock control and purchasing. 

The normal defect classification is into three categories which are allied to progressively tighter acceptance levels (limits) or AQLs, as follows. 

Defect classifications (weight, seams, seals, wall thickness, pin holes, etc.). 

Steiner. K. V, (1992), Defect classification in composites using ultrasonic nondestruclite evaluation techniques. Damage Detection in Composite Materials. ASTM. STP 1128, pp. 72-84. 

Yang, X., Pang, G. K. H. Yung, N. 2004. Discriminative training approaches to fabric defect classification based on wavelet transform. Pattern Recognition 37(5), 889-899. 

Choi et al. [21] reported a fabric defect identifying system using fuzzy inference system. Image pre-processing techniques that enhance raw images were applied before the defect classification. Only a small number of fuzzy inference mles were developed to identify the non-defect, slub (warp direction), slub (weft direction), nep and composite defect. The experimental results with this approach demonstrated that the proposed method could able to identify the fabric defects at an extent comparable to that of a human inspector. 

---