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

Year: 2022

Type: article

Abstract: 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.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2022.05.068

Authors: Henrik Heymann, Alexander D. Kies, Maik Frye, Robert H. Schmitt, Andrés Boza

Institutions: Fraunhofer Institute for Production Technology IPT, RWTH Aachen University, Universitat Politècnica de València

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: European Commission