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. A conference paper detailing the work in this topic can be found here (open access). A journal article describing the research can be accessed from here (open access) and the code to implement the STAS methdology can be accessed from here .
Achieving zero defects in manufacturing brings forth the need for simulations that emulate real processes and products as accurately as possible. Such simulations heavily rely on accurate representation of non-ideal parts i.e. models of actual manufactured parts with geometric and dimensional errors caused due to uncertainties in manufacturing processes.
A morphing Gaussian Random Field methodology was developed to address the limitations of state-of-art methodologies. A conference paper detailing the preliminary work in this topic can be found here (open access). A journal article is in review (arXiv version) and the code to implement the mGRF methdology can be accessed from here.