Predictive analytics in quality assurance for assembly processes:
lessons learned from a case study at an industry 4
Abstract
Quality assurance (QA) is an important task in manufacturing to assess
whether products meet their specifications. However, QA might be
expensive, time-consuming, or incomplete. This paper presents a solution
for predictive analytics in QA based on machine sensor values during
production while employing specialized machine-learning models for
classification in a controlled environment. Furthermore, we present
lessons learned while implementing this model, which helps to reduce
complexity in further industrial applications. The paper’s outcome
proves that the developed model was able to predict product quality, as
well as to identify the correlation between machine-status and faulty
product occurrence.