Defect manufacture

Defect. A plaintiff must estabUsh that the product which iajured him was defective. A product can be defective ia three ways it may be defectively manufactured, defectively designed, or sold with iaadequate warnings. 

Nearly every manufacturing, shipping, and installation process is a potential source of a defect. Manufacturers, in recognition of this, mount major efforts to minimize or eliminate defects. Although an exhaustive list of defects is beyond the scope of this book, some common ones will be discussed. 

D Defect Widely defined product design defect, manufacturing error (so that the... 

These new challenges in statistical process control have motivated the laxmch of quality control-oriented topics within several international research funding programs. For example, within the European Community 7th Framework Program, the topic zero defect manufacturing has generated calls for proposals in recent years. 

Failure cause is the process or mechanism responsible for initiating the failure mode. The possible processes that can cause component failure include physical failure, design defects, manufacturing defects, environmental forces, and so on. 

Strict liability applied to product liability suits makes a manufacturer or seller responsible for all product defects and particularly design defects, manufacturing defects, and defects in marketing. Strict liability wrongs do not depend on the degree of carefulness of the defendant. This means that the defendant is liable when it is shown that the product is defective and the product has caused harm to the consumer. 

The item failure rate is relatively high. Such failure is usually due to factors such as defective manufacture, incorrect installation, learning curve of equipment user, etc. Design should also aim at having a short initial period . 

Availability of a representative set of data examples. Such a set may significantly simplify construction of any automatic interpretation system. The example data is usually obtained from calibration pieces, however, they usually represent only the most common defects and are usually expensive to manufacture. Recently more and more data is stored as digital inspection records, unfortunately the stored data is rarely fully classified, as this would increase the cost of inspection (usually only the serious defects are given full description in the reports). 

Neural network classifiers. The neural network or other statistical classifiers impose strong requirements on the data and the inspection, however, when these are fulfilled then good fully automatic classification systems can be developed within a short period of time. This is for example the case if the inspection is a part of a manufacturing process, where the inspected pieces and the possible defect mechanisms are well known and the whole NDT inspection is done in repeatable conditions. In such cases it is possible to collect (or manufacture) as set of defect pieces, which can be used to obtain a training set. There are some commercially available tools (like ICEPAK [Chan, et al., 1988]) which can construct classifiers without any a-priori information, based only on the training sets of data. One has, however, always to remember about the limitations of this technique, otherwise serious misclassifications may go unnoticed. 

Figure 6. Correlation images of manufactured pipe with defect, under pressure changes of (a) 20psi, (b) 30psi and (e) 40psi. The sealing rule lies to the left of the pipe and the defect zone is highlighted by the white arrow.

The CamuS system is currently in the form of a laboratory prototype and is undergoing a series of validation tests using an extensive set of test-pieces covering a range of geometries and classes of defect which has been manufactured for the purpose. 

Therefore, it is important for judging the performance and the safety of the product to understand the size of the defect and the position by the ultrasonic method quantitatively. And, the reliability of the product improves further by feeding back this ultrasonic wave information to the manufacturing process. 

Instrumental Analysis. It is difficult to distiaguish between the various acryhcs and modacryhcs. Elemental analysis may be the most effective method of identification. Specific compositional data can be gained by determining the percentages of C, N, O, H, S, Br, Cl, Na, and K. In addition the levels of many comonomers can be estabhshed usiag ir and uv spectroscopy. Also, manufacturers like to be able to identify their own products to certify, for example, that a defective fiber is not a competitor s. To facihtate this some manufacturers iatroduce a trace of an unusual element as a built-ia label. 

The next step is to define the intermediate event, tire failure. There are two events which could contribute a worn tire resulting from much usage or a tire that is defective owing to a manufacturing problem. These are both basic events because additional information is needed for any further definition. 

A considerable assumption in the exponential distribution is the assumption of a constant failure rate. Real devices demonstrate a failure rate curve more like that shown in Eigure 9. Eor a new device, the failure rate is initially high owing to manufacturing defects, material defects, etc. This period is called infant mortaUty. EoUowing this is a period of relatively constant failure rate. This is the period during which the exponential distribution is most apphcable. EinaHy, as the device ages, the failure rate eventually increases. 

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