Dynamical Systems Diagnostics for Intelligent Machine Tools

This grant will support research that will contribute to new knowledge related to a manufacturing process, promoting both the progress of science and advancing national prosperity. Subtractive manufacturing processes make three-dimensional parts and objects by removing material in small layers. Machining is one of the more pervasive subtractive processes and includes material removal methods known as milling, drilling, broaching, and turning.

Each of these machining processes uses a cutting tool to remove material layers from a bulk material and to leave behind a desired three-dimensional part. These material removal processes are also one of the most widely used approaches to create metal, wood, and plastic parts for the automotive, aerospace, medical device industries. However, the accuracy, surface quality, and productivity of all machining processes are limited by the vibrations caused in the cutting process.

Although recent advances have unveiled the fundamentals physics behind these barriers for machining processes, there still exist a large gap between what is possible in the best academic lab and in a production setting. This project seeks to develop diagnostic tools that will enable manufacturers to take advantage of the latest academic knowledge for vibration problems, such as stability limitations, surface finish, and surface location error.

Therefore, the results of this grant will benefit the U.S. economy and society. This research involves and impacts several disciplines which include dynamical systems and control, manufacturing, machine learning, and data science. It is expected that this research, along with the complementary educational efforts, will help train the future workforce and broaden the participation of underrepresented groups in STEM disciplines.

Tool vibrations impose severe limitations on industrial capability, such as reduced accuracy, a poor surface finish, and increased costs which are linked to instability. Although past research has uncovered the fundamental mechanism that leads to instability, it is still nearly impossible for the U.S. industrial base to apply this knowledge due to the need for repetitive, costly, and manually intensive modal tests and separate cutting force tests.

This research will develop two data-driven approaches that will enable the U.S. industrial base to integrate machining dynamics with modern cyber-infrastructure. More specifically, the first research objective develops a new approach to automate the identification of the physical parameters required by predictive machining dynamics models and analysis tools.

This will enable modern cyber infrastructure to optimize cutting process parameters and thus allow better decision to be made that now include the limitations imposed by vibrations. The second research objective develops a data-driven approach to discover the governing equations of systems that include time delays. It is expected that this framework will provide a more comprehensive understanding of the important physical mechanisms to include in machining dynamics models; this method could also generate models that modern cyber infrastructure could use to obtain optimal cutting process parameters or monitor the cutting process to diagnose problems from model parameter changes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.