Prior Knowledge-Embedded Machine Learning-Driven Cutting Force Monitoring in Machinery Industry

International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–6

Abstract

Condition monitoring of manufacturing processes plays a vital role in the era of Industry 4.0 and sustainable development goals. The use of condition monitoring enables soft sensing of indirectly measurable or non-measurable production indicators, e.g., cutting forces, as the foundation for a digital shadow or digital twin powered by emerging technologies such as artificial intelligence (AI) and edge computing techniques. Hence, when properly applied, it leads to an improvement in process understanding, production planning, quality control, and predictive maintenance. Despite these merits, the adaptability and reliability of condition monitoring at the production field level are limited by the modeling uncertainties due to the rapidly changing industrial environment driven by dynamic customer demands. This study presents a novel strategy to overcome these shortcomings by employing machine internal signals and prior knowledge-embedded machine learning techniques. A concept of incorporating production domain-specific physical models and logical rules into generalized data-driven methods is proposed. A hybrid learning force model based on machine internal signals was constructed. As a result, the soft sensor encompasses stochastic effects that are neglected in the existing model and estimates the reliability of the modeled findings. Prospectively, this allows for online parametrization of the model within dynamically changing conditions. This concept is exemplarily implemented in an industrial use case.

Authors 4

1. Ziqi Huang Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Aachen, Germany

2. Tiandong Xi Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Aachen, Germany

3. Marcel Fey Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Aachen, Germany

4. Christian Brecher Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, Aachen, Germany