Guideline for Deployment of Machine Learning Models for Predictive Quality in Production

作     者：Henrik Heymann Alexander D. Kies Maik Frye Robert H. Schmitt Andrés Boza 

作者机构：Fraunhofer Institute for Production Technology IPT Steinbachstr. 17 Aachen 52074 Germany Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University Campus-Boulevard 30 Aachen 52074 Germany Centro de Investigación Gestión e Ingeniería de la Producción (CIGIP) Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain 

出 版 物：《Procedia CIRP》 

年 卷 期：2022年第107卷

页      面：815-820页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

主　　题：Artificial Intelligence Machine Learning Deployment Production Manufacturing Predictive Quality 

摘      要：Predicting product quality represents a common area of application of machine learning (ML) in manufacturing. However, manifold challenges occur during the integration of ML models into production processes. Therefore, this paper aims to provide a guideline for the deployment of ML models in production environments. Relevant decisions and steps for deploying models in predictive quality use cases are demonstrated. The results for each component of the proposed guideline - deployment design, productionizing & testing, monitoring, and retraining - have been validated with industry experts including exemplary implementations.