Analytical and Experimental Identification of Nonlinearities in a Single-Nut, Preloaded Ball Screw

Cuttino, James F.; Dow, Thomas A.; Knight, Byron F.

doi:10.1115/1.2828782

Cited by 51 publications

(11 citation statements)

References 3 publications

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“…This relationship is derived using a large number of finite element simulations based on the elastic contact theory of Mindlin. This theory is experimentally verified by Cuttino [15] for a single-nut, preloaded, precision ball screw. The motion of the balls in the set-up consists of a rolling and spinning part, and the spinning part is (simplistically) assumed to be responsible for the hysteretic behavior.…”

Section: Introductionmentioning

confidence: 77%

Characterization of frictional hysteresis in ball-bearing guideways

Al‐Bender

Symens²

2005

Wear

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Section: Introductionmentioning

confidence: 77%

Characterization of frictional hysteresis in ball-bearing guideways

Al‐Bender

Symens²

2005

Wear

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“…More general applications of finite element analyses and applications are given by Cook et al [9,10]. The use of finite element models to develop empirical relationships for design has been shown by Cuttino and Dow [11] and Cuttino et al [12].…”

Section: Performance Parametersmentioning

confidence: 98%

Design and development of a toroidal flexure for extended motion applications

Cuttino

Dijck²,

Brown

2005

Precision Engineering

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“…The Hilbert–Huang transform has also been applied to processing and research on ball screw failure and mechanical signals. 2,3 The pretension of the ball screw in a feed drive system improves its mechanical strength and maintains a minimum rigidity. However, the continual movement of the ball screw subjects it to thermal deformation, which results in positioning errors in the feed system and reduces the overall positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

A revisit for the diagnosis of the hollow ball screw conditions based classification using deep learning

Huang

Chuang

Lin

2022

Measurement and Control

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Hollow ball screws play a vital role in high-quality precision manufacturing, in which sensors are used to obtain useful data. The use of artificial intelligence to determine the condition of machines is a major trend, and installing multiple sensors on relevant objects may be prohibitively expensive. This study compares a machine learning method based on a support vector machine (SVM) with deep learning methods. In the machine learning strategy, built-in signals from internal parameters are used to determine the condition of the hollow ball screw. Rather than using a graphics processing unit (GPU), feature engineering and then Fisher’s score were applied to determine the most representative parameters from fusion sensor signals to perform SVM classification. For the deep learning method, a GPU was employed instead of data cleaning; a long- and short-term memory (LSTM) network and hybrid convolutional neural network (CNN) and an LSTM network were used to automatically extract effective features and then perform classification. Thus, the deep learning approach enabled classification of one-dimensional (1D) and multidimensional data sets, through which users can obtain suitable configurations for various sensors according to cost and other requirements for classification accuracy. We compared the accuracies of the LSTM, CNN–LSTM, and SVM models by using multiple datasets. The 3D LSTM model with current, rpm, and additional linear scale signals provided good synergy for feature extraction and resulted in the maximum accuracy close to the highest accuracy of the supervised fine-tuned SVM model by using current signal. Datasets that are unrepresentative according to FE analysis may be useful in deep learning. The advantages of using a deep learning model without feature extraction relative to those of SVM learning was investigated.

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Analytical and Experimental Identification of Nonlinearities in a Single-Nut, Preloaded Ball Screw

Cited by 51 publications

References 3 publications

Characterization of frictional hysteresis in ball-bearing guideways

Characterization of frictional hysteresis in ball-bearing guideways

Design and development of a toroidal flexure for extended motion applications

A revisit for the diagnosis of the hollow ball screw conditions based classification using deep learning

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