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Glass Bulb Production

Object of Investigation

The project was performed for GE in 1991.

The object of investigation was to review a production line containing a furnace, transportation line, and the bulb formation unit (producing special bulbs for nuclear reactors). Two-hundred (200) different parameters were measured at 3 minute intervals for quality control. These measurements consisted of physical and operational variables The parameter used to define bulb quality was the geometric size.

Primary Goals

The main goal was to determine a set of variables that essentially predicted the bulb size.


The data set was considerably noised, and the preliminary (conventional) statistical analysis revealed no noticeable relationships in the database. The opinion of plant engineers (based on their intuition) indicated a satisfactory prediction was possible.

The Result

The KET analysis did confirmed that a prediction was possible. The results discovered that only 15 of 200 variables were essential to predict the problem. The constructed predictor demonstrated a 93% accuracy. Additionally, there were two major database shortcomings identified: a lower frequency of quality control measurements (that parameters every 0.5 minutes were more desirable) and presence of (human factors) artifacts in the database that interrupted normal measurements).


The economy obtained from reducing the frequency of measurements was an immediate gain. The opportunity to use a quality predictor and apply it to quality control, promised potential improvement in the manufacturing process.

How it was Done

An Information Analysis Module from the KET Tool Kit was used to measure the dependency between variables, identify and list the essential ones. This type of analysis has great advantages comparatively with the classical correlation theory, because it does not require any assumptions about the underlying models (as the correlation analysis inherently does).

Subsequently, Topologic Analysis was used to assess the data informativeness. It discovered shortcomings in the database and assess the potential contribution of different parameters before the actual predictors were constructed. Finally, the predictors were constructed with the help of the Fast Analytical Descriptor from KET’sModel-Free,”  Tool Kit, instead of using a predetermined one and simply fitting the coefficients).


The further applications of KET may include the creation of a control/monitoring system with diagnostic, decision making and data collecting modules. The system was designed to adaptive to technological process changes.

  New Feature !!

A new KET module was introduced recently to support interaction with "Mathematica 8" system.

  News !!

KET, LLC joined BioMed Content Group, Inc. in initiative of using AI agents to facilitate work of physicians and educators.

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