Machine tools are essential components of modern manufacturing. They are com posed of various mechanical, hydraulic, and electrical systems such as the spindle,
tool changer, cooling system, and the linear and rotary feed drives. Due to their com plexity, high cost, and importance to the manufacturing process it is recommended to
implement some sort of condition monitoring and predictive maintenance to ensure
that they remain reliable and high performing. One way of potentially implement ing predictive maintenance and condition monitoring is digital twins. Digital twins
enable the real-time, accurate, and complex modeling and monitoring of mechanical
systems. They utilize data collected from the system to constantly update their mod els which can be used for monitoring of the systems state and future predictions. This
work presents a digital twin workbench of a machine tool feed drive. The workbench
enables the collection and analysis of large, varied, high-frequency data which can be
used to construct a digital twin of the feed drive. A digital twin can enable many
other useful functionalities. Some of these functionalities include condition moni toring, modeling, control, visualization, and simulation. These functionalities can
enable maximum asset performance and are key in implementing effective predictive
maintenance. The main contributions of this work are the following: The design and
iv
construction of a machine tool feed drive which implements a novel external distur bance force method. A new method of fault detection in ball screws using interacting
multiple models which was shown to provide accurate estimates of levels of preloads
in a ball screw driven feed drive. A digital twin based modeling strategy and analysis
of the data generated by the system including system modeling and observations on
modeling difficulties. / Thesis / Master of Applied Science (MASc) / Digital twins enable the real-time, accurate, and complex modeling and monitoring
of mechanical systems. Machine tools are essential components of modern manufac turing. They are composed of various mechanical, hydraulic, and electrical systems
such as the spindle, tool changer, cooling system, and linear and rotary feed drives.
This work presents the design of a workbench of a machine tool linear feed drive, a
fault detection strategy, and a digital twin modeling solution. The workbench enables
the collection and analysis of large, varied, high-frequency data which can be used to
construct a digital twin of the feed drive. A digital twin can enable many other useful
functionalities. Some of these functionalities include condition monitoring, modeling,
control, visualization, and simulation. These functionalities can enable maximum
asset performance and are key in implementing effective predictive maintenance.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29393 |
| Date | January 2024 |
| Creators | Sicard, Brett |
| Contributors | Gadsden, Stephen Andrew, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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