Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

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 based on machine sensor values during production while employing specialized machine-learning models classification controlled environment. Furthermore, we present lessons learned implementing this model, which helps reduce complexity further industrial applications. The paper’s outcome proves that the developed model was able predict product quality, as well identify correlation between machine-status and faulty occurrence.