Virtual Metrology for Machining Applications

Production quality and process efficiency are the two main drivers that lead any industrial strategy. To ensure product quality, a duality historically existed between two approaches, namely batch sampling and systematic sampling. In batch sampling, the batch homogeneity is assumed, and a subset is measured, whereas, in systematic sampling, every part of the batch is measured independently. The latter approach is too expensive to be considered. Therefore, batch sampling is implemented, which, however, involves sacrifices in product quality, as the homogeneity assumption cannot always be true. Today, virtual metrology (VM) is emerging with the promise of enabling a new standard for quality control by offering a third approach that combines the benefits of the others. VM estimates product quality while preserving process efficiency. It enables continuous systematic monitoring of production in hidden time. To do so, it leverages process data and relates them to product quality through machine learning algorithms. In this work we focus on VM for milling operations, with a specific attention on the estimation of the dimensional quality of single pass milled parts. To do so, two experiments were conducted in an industrial environment. Various combinations of recorded process variables, data synchronization algorithms, dimensionality reduction algorithms, and regression algorithms were tested to maximize model accuracy. A new baseline for dimensional quality estimation in an industrial production environment is set with a mean absolute error of 14.4 μm. Besides the proof of concept, we provide directions on how to design and tune a VM algorithm for milling operations.