Quality 4.0 methodology for manufacturing processes

Abstract

In this work, the pathway for the implementation of Quality 4.0 is reviewed. Several articles are written to detail the evolution from the execution of Six sigma DMAIC methodology and how it can be adapted to the use of AI to create smart factories and smart processes. The main objective is to expand the current conformance rate of this methodology and find the defective items that can be overlooked in manufacturing processes. This research ranges from the selection of the data to train the available models, how can it be corrected and improved, the different processes to handle real-world data sets, the use of different ML algorithms for data analysis, the adaptation of this MLAs to quality standards in Quality 4.0 practices, to the curricular needs for Quality 4.0 for problem solving replacing Six Sigma practices. This thesis will focus only on the description of the decay of the Six Sigma DMAIC paradigm and its evolution to Quality 4.0, and the Process Monitoring for Quality methodology for rare event detection, which are the most noted journal papers in which I could collaborate.