Investigation of ball screw preload variation based on dynamic modeling of a preload adjustable feed-drive system and spectrum analysis of ball-nuts sensed vibration signals

Feng, Guo-Hua; Pan, Yi-Lu

doi:10.1016/j.ijmachtools.2011.09.008

Cited by 126 publications

(65 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1 is the schematic diagram of the inertial force excitation technique in [7,8]: the slider accelerates or decelerates randomly and continuously in a small area. The inertial forces, as shown in Fig.1b, are proven to be squarewave pulses in [9]. Fig.…”

Section: Background Of Aemamentioning

confidence: 90%

A Method for Identification of Machine-tool Dynamics under Machining

et al. 2015

View full text Add to dashboard Cite

Dynamic properties of machine-tool structures are likely to change under machining conditions. Thus, the dynamic parameters obtained by traditional experimental modal analysis in the static state may not characterize accurately the dynamics of the machine tool structure in operation. This paper proposes a new method of so-called AEMA (Active Excitation Modal Analysis) to identify the dynamic modal parameters of a machine tool structure during machining. A random cutting excitation technique realized by cutting a specially designed workpiece is proposed to provide strong and evenly distributed excitation within the frequency range of interest. The surface of the workpiece has a long narrow random zigzag width, which randomizes the resulting cutting forces. The LSCE (Least Square Complex Exponential) method is employed to estimate the modal parameters from just the measured responses. Then an algorithm based on two novel tools, the harmonic frequency fence and the spectrum abruptness ratio, is presented to eliminate the harmonic modes attributed to AC power and rotation frequency. The abruptness ratio is used to detect the basic frequency, and then the fence filters out the harmonic modes caused by peaks at integer multiples of the basic frequency through narrow frequency fence slots followed by a damping ratio limit. Finally, the proposed AEMA method is experimentally validated and shows satisfactory results.

show abstract

Section: Background Of Aemamentioning

confidence: 90%

A Method for Identification of Machine-tool Dynamics under Machining

et al. 2015

View full text Add to dashboard Cite

show abstract

“…A MATLAB script was written to simultaneously solve equations (9), (10), (13), (14), and (19) for every load-speed combination. Using these equations, which may be solved via iterative techniques, F c and M G are determined to be functions of v R =v and v m =v.…”

Section: Ball Bearingsmentioning

confidence: 99%

An optimum preload method for machine tool spindle ball bearings

Zhang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

To provide a suitable rotation rate for different machining processes, a single machine tool spindle should work over a wide range of speeds. This study considered the effects of speed on dynamic behaviour of ball bearings and combined the fatigue life model and ball bearing internal load distribution model to determine the appropriate preload. First, the influence of speed on internal load distribution and ball bearing contact angles was analysed. The preload was calculated using a ball bearing internal load distribution model. Next, assuming constant bearing fatigue life, the theoretical preload curves were determined using the fatigue life model by changing the reliability factor. Finally, at low speeds, the maximum designed preload (design value) was set as the initial preload. With the increase in speed, the optimum preload was hierarchically obtained within the internal region between the theoretical preload curves. An experimental test rig for the optimum preload of ball bearings, which can automatically adjust the preload, was developed. The proposed method for determining the optimum preload was verified using the measured performance indicators, including the temperature, motor currents, and vibration of ball bearings. The results showed that the optimum preload suggestion made the test ball bearings exhibit excellent behaviour.

show abstract

“…Motor torque via current sensors (Li et al, 2014, Uhlmann et al, 2008, Zhou et al, 2009), accelerometers (Feng & Pan, 2012, Huang et al, 2010, Liao & Lee, 2009)…”

Section: Introductionmentioning

confidence: 99%

A Sensor-based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes

Vogl

Calamari

et al. 2016

Procedia Manufacturing

View full text Add to dashboard Cite

A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a ‘sensor box’ to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality.

show abstract

Investigation of ball screw preload variation based on dynamic modeling of a preload adjustable feed-drive system and spectrum analysis of ball-nuts sensed vibration signals

Cited by 126 publications

References 12 publications

A Method for Identification of Machine-tool Dynamics under Machining

A Method for Identification of Machine-tool Dynamics under Machining

An optimum preload method for machine tool spindle ball bearings

A Sensor-based Method for Diagnostics of Geometric Performance of Machine Tool Linear Axes

Contact Info

Product

Resources

About