![]() However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. ![]() The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 20 dealing with predictive quality in manufacturing. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. ![]() Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance.
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