D-MAX DMC-20SEC Manual de usuario descargar pdf (Pagina 36)

Secondly parallel pseudo and slip-through reduction were carried out (Table IX, Fig. 11, Fig.

12).

Real failures

(

Quasi-tombstone

)

[pieces]

Pseudo failures

[pieces]

364 (

) 67 841

Real failures

(

Quasi-tombstone

)

[pieces]

Pseudo failures

[pieces]

627 (

) 58 027

After optimization (30 days testing period)

Inspected components

(solder joints)

[pieces]

Detected failures [pieces]

Pseudo rate

[ppm]

4 655 392

(9 310 784)

58 654

12 560

Before optimization (30 days testing period)

68 205

Detected failures [pieces]

Pseudo rate

[ppm]

Inspected components

(solder joints)

[pieces]

2 423 334

(4 846 668)

27 995

Table 13. Results of macro optimization

Another very serious question is about the parameter optimization process, namely how can

the AOI engineers validate the new parameter values determined by the optimization process?

Certainly a correction of a bad classification cannot be validated only by examination of the

specified image, but it is necessary to check several other instances. Therefore, to execute a

reliable validation process, the engineers have to collect a large image database (“image base”)

covering all cases as they occur in the best possible way. Unfortunately, creating a good and

usable image base is a long and sometimes impossible task because of several – often contra‐

dicting – criteria. A manual image collection by the engineers is very time-consuming and in

case of automatic systems (like AOIs) there is only a limited possibility because of the high

number and varied type of data. Automatic methods are faster but during the collection, some

falsely classified images can be put in the image base which makes the parameter optimization

impossible. For example, if an image containing a faulty component is placed into the “good”

part of the image base, the optimization process will try to adjust to the parameters that the

AOI algorithm has classified the image as “good”. As a result, the optimized macro cannot

recognize this specified error which can indicate slippages causing the greatest type of

inspection catastrophe.

The number of stored images is also a very important factor. If the image base contains too

many images, the resources (processor, hard drive, network etc.) become overloaded and the

optimization process can only be executed slowly. On the other hand in case of a small image

base the algorithm validation is neither reliable nor accurate enough.

Materials Science - Advanced Topics

422

1 2 ... 31 32 33 34 35 36 37 38 39 40 41 ... 55 56

Comentarios a estos manuales

Sin comentarios

D-MAX DMC-20SEC Manual de usuario Pagina 36

Comentarios a estos manuales