Publications

The problem of understanding and modeling the complicated physics underlying the action and response of the interfaces in typical structures under dynamic loading conditions has occupied researchers for many decades. This handbook presents an integrated approach to the goal of dynamic modeling of typical jointed structures, beginning with a mathematical assessment of experimental or simulation data, development of constitutive models to account for load histories to deformation, establishment of kinematic models coupling to the continuum models, and application of finite element analysis leading to dynamic structural simulation. In addition, formulations are discussed to mitigate the very short simulation time steps that appear to be required in numerical simulation for problems such as this. This handbook satisfies the commitment to DOE that Sandia will develop the technical content and write a Joints Handbook. The content will include: (1) Methods for characterizing the nonlinear stiffness and energy dissipation for typical joints used in mechanical systems and components. (2) The methodology will include practical guidance on experiments, and reduced order models that can be used to characterize joint behavior. (3) Examples for typical bolted and screw joints will be provided.

This paper documents the experimental validation of an active control approach for mitigating chatter in milling. To the authors knowledge, this is the first successful hardware demonstration of this approach. This approach is very different from many existing approaches that avoid instabilities by varying process parameters to seek regions of stability or by altering the regenerative process. In this approach, the stability lobes of the machine and tool are actively raised. This allows for machining at spindle speeds that are more representative of those used in existing machine tools. An active control system was implemented using actuators and sensors surrounding a spindle and tool. Due to the complexity of controlling from a stationary co-ordinate system and sensing from a rotating system, a telemetry system was used to transfer structural vibration data from the tool to a control processor. Closed-loop experiments produced up to an order of magnitude improvement in metal removal rate.

This report summarizes experimental and test results from a two year LDRD project entitled Real Time Error Correction Using Electromagnetic Bearing Spindles. This project was designed to explore various control schemes for levitating magnetic bearings with the goal of obtaining high precision location of the spindle and exceptionally high rotational speeds. As part of this work, several adaptive control schemes were devised, analyzed, and implemented on an experimental magnetic bearing system. Measured results, which indicated precision positional control of the spindle was possible, agreed reasonably well with simulations. Testing also indicated that the magnetic bearing systems were capable of very high rotational speeds but were still not immune to traditional structural dynamic limitations caused by spindle flexibility effects.

In this paper a methodology for analytically estimating the response of replacement components in a system subjected to worst-case hostile shocks is presented. This methodology does not require the use of system testing but uses previously compiled shock data and inverse dynamic analysis to estimate component shock response. In the past component shock responses were determined from numerous system tests; however, with limitations on system testing, an alternate methodology for determining component response is required. Such a methodology is discussed. This methodology is mathematically complex in that two inverse problems, and a forward problem, must be solved for a permutation of models representing variabilities in dynamics. Two conclusions were deduced as a result of this work. First, the present methodology produces overly conservative results. Second, the specification of system variability is critical to the prediction of component response.