In-Process Quality Improvement (IPQI)

IPQI project's goal is to develop, implement, demonstrate, and pilot in the industry, a systematic closed-loop quality improvement methodology with in-process adaptive monitoring capable of self-recovery from 6-sigma quality defects.

As a part of this project I developed the STAS methodology , which enables the prediction of whole part deviation of a free-form surface based on partial measurement of the surface. Partial measurements reduce the time needed for the inspection of a given part and enable the effective in-line application of a 3D-Optical scanner.

The video below illustrates the methodology applied to an automotive door inner component, where, it can be seen that, the prediction error is almost zero after completion of 3 out of the total 18 measurements required to measure the entire part.

Research articles describing the STAS methodology in detail can be downloaded from HERE & HERE , the Matlab code implementing the same can be found HERE .

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Manoj Babu
Senior Teaching Fellow

I hold a PhD in Engineering with research interests in digital manufacturing, applied machine learning and stochastic modelling of Manufacturing Assembly Process.

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