SBIR-STTR Award

This project describes research that will lead to the commercialization of the Tool Dashboard, a new technique for pre-process milling parameter selection. In this approach, dynamic models of the cutting tool and holder will be analytically coupled to spindle-machine measurements to predict the tool point dynamics. Given this information, stability lobe diagrams, which display stable and unstable cutting zones as a function of the axial depth of cut and spindle speed, will be constructed. This information will then be used to develop the user-friendly Tool Dashboard interface for parameter selection. By removing the current necessity for a separate measurement of each tool-holder-spindle-machine assembly, the requirements for significant measurement time, specialized measurement equipment, and trained personnel will be eliminated. This method leads to a commercially-viable approach where the spindle-machine dynamics are identified once, archived, and then used to predict the tool point dynamics for any tool-holder combination (analogous to the Human Genome Project that mapped the genes responsible for characteristics of the body). By this approach, users without measurement capabilities or knowledge of structural dynamics/chatter theory can take advantage of the substantial productivity increases and "first part correct" fabrication made possible through pre-process consideration of the milling process dynamics.

Keywords:
Milling, Dynamics, Chatter, Stability, Prediction, Receptance, Frequency Response

The objective of this research is to enable pre-process milling parameter selection for “first part correct” production. In the proposed solution the Receptance Coupling Substructure Analysis (RCSA) approach will be used to couple the measured spindle-machine dynamics to models of the tool and holder and predict the assembly’s dynamic response. Given this information, frequency/time-domain algorithms are applied to select feasible spindle speed-depth of cut pairs that enable first part correct production. The development of an automated artifact will be coupled with this technology to speed measurement and data transmission of spindle-machine receptances for evaluation of machine productivity and repeatability. Given the tool point response, the process stability and surface location error (part errors caused by forced vibrations) will be determined to separate feasible and infeasible zones within the spindle speed-axial depth of cut domain. Once acceptable combinations are determined, they will be presented in a new user-friendly format, the Tool Dashboard, similar to an automotive dashboard display. Tool Dashboards will be customized for various applications to analyze the effect of different process parameter combinations on vibration behavior (chatter sensitivity and surface location error) and energy consumption.

Keywords:
Machining, dynamics, vibration, chatter, depth of cut, spindle speed, productivity, repeatability