Work

Hybrid ML for Parameter Prediction in Production

Year: 2023

Type: article

Abstract: In the past, research in the production domain was driven by mathematical and physical description of production technologies. Over the last years, data-driven approaches like machine learning (ML) and artificial intelligence (AI) gave the research a new direction. Often, already existing knowledge is neglected when using data-driven approaches resulting in models that do not represent the best possible results. By combining these two approaches all available knowledge is used generating the best possible model. This combination is called hybrid modeling. In this paper, hybrid ML as part of hybrid modeling is introduced and the benefits and challenges using hybrid ML for the prediction of process parameters in the production domain are demonstrated.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2023.06.141

Authors: Jonas Dorißen, Henrik Heymann, Robert H. Schmitt

Institutions: Fraunhofer Institute for Production Technology IPT, RWTH Aachen University

Language: French

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie