Model-based wear prediction of milling machine blades

Abstract

Technological capabilities are enabling the implementation of various prognostic methodologies for industrial assets. Whereas companies are driving for finding new ways to improve asset operation and service offerings to their customers, model-based simulations can be deployed to model machine behavior for instance in usage cases unconventional for the asset. Modern embedded technologies in peripheral milling machines offer asset operation-related data collection and sharing online. Due to this capability, understanding of the asset operational behavior can be assessed remotely, and the data can be used to estimate the wear progress of the milling blades. However, creating a model-based simulation model is not heavily dependent on online data, yet the model construction requires knowledge about asset operation and physical behavior to enable purpose-fit and simple enough simulation construction. In this research, a model-based simulation model is created to predict peripheral milling machine blade wear in terms of average vibration and torque parameters. A blade wear variability in different usage profiles is being tested with a simulation test case. The results are proving that the model-based simulation model can be accurately used to emulate asset physical behavior by the means of torque and vibration parameters. Based on the results, changes in the asset vibration average trend can be distinctly utilized to estimate wear progress on the spindle cutting blades. Further, predicted vibration levels and blade lifetime estimations can be considered in PPX type of business model profitability or lifecycle related calculations where ownership of machines is retained by the manufacturing company.