Quality Improvement of Milling Processes Using Machine Learning-Algorithms

Maik Frye, Robert Heinrich Schmitt

Abstract

The increasing digitalization and industrial efforts towards artificial intelligence foster the use of Machine Learning (ML)-algorithms in the production environment. Within production, different application areas and use-cases arise for the usage of ML. In this paper, we focus on the implementation of ML- algorithms for a milling process where critical process conditions are predicted. Based on the predicted process conditions, the machining parameters can be adjusted in advance to avoid critical conditions of the process. The avoidance of critical process conditions increases the quality of the products, since quality characteristics such as surface roughness or dimensional deviations can be influenced. To ensure the transferability of the results to other applications, we follow a methodical approach. The results of the ML- models are discussed critically and further steps are derived in order to use ML-models successfully in the future.

Keywords

Quality Improvement, Predictive Process Control, Machine Learning, Artificial Intelligence, Data Preprocessing, Artificial Neural Networks, Random Forest, Gradient Boosting

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IMEKO-TC10-2019-022.pdf

DOI

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IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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