Burggräf, PeterPeterBurggräfWagner, JohannesJohannesWagnerHeinbach, BenjaminBenjaminHeinbachSteinberg, FabianFabianSteinbergPérez M., Alejandro R.Alejandro R.Pérez M.Schmallenbach, LennartLennartSchmallenbachGarcke, JochenJochenGarckeSteffes-lai, DanielaDanielaSteffes-laiWolter, MoritzMoritzWolter2022-03-062022-03-062021https://publica.fraunhofer.de/handle/publica/27120110.1016/j.procir.2021.11.108Quality 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 papers 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.enmachine-learningpredictive qualityproductionquality assuranceclassification003005006518journal article