Flanges for mounting grinding wheels on grinding machine tools.

1984 ◽  
Keyword(s):  
Machine Tools ◽  
Grinding Wheels ◽  
Grinding Machine

Improving Contour Accuracy of Machine Tools Using an Integral-Gain Scheduler

2005 ◽  
Vol 219 (7) ◽  
pp. 511-518 ◽  
Author(s):  
R C Ko ◽  
M C Good
Keyword(s):  
Machine Tools ◽  
High Performance ◽  
Pulse Width Modulation ◽  
Tracking Error ◽  
Contour Error ◽  
Current Loop ◽  
Pwm Inverter ◽  
Current Controller ◽  
Reversal Error ◽  
Grinding Machine

In high-precision machine tools, contour error at axis reversal can significantly reduce the quality of products. Resulting from non-linear friction behaviour, the reversal error is traditionally handled by the velocity controller, which highly relies on a high-performance current servo. However, the widely employed pulse width modulation (PWM) inverter in the power stage of the current servo operates with a severe non-linearity known as deadband. The deadband effect degrades the current-loop tracking performance and consequently hinders the velocity controller in responding to friction disturbances. The result is a significant and oscillatory tracking error, or contour error in a multiaxis system. Unlike other approaches where the deadband is compensated via measurement or estimation, a control system approach is proposed in this paper where the deadband is treated as a voltage perturbation in the current loop. The proposed scheme incorporates a feedforward signal from the current command and schedules the integral action in the current controller accordingly. The proposed scheme was implemented in digital servo drives of a commercial grinding machine. Experiments show that the proposed scheme is an effective and practical solution for this type of problem.

Thermal error modelling of motorised spindle in large-sized gear grinding machine

2017 ◽  
Vol 231 (5) ◽  
pp. 768-778 ◽  
Author(s):  
He Dai ◽  
Shilong Wang ◽  
Xin Xiong ◽  
Baocang Zhou ◽  
Shouli Sun ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Tools ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Support Vector ◽  
Inference System ◽  
Thermal Errors ◽  
Gear Grinding ◽  
Neuro Fuzzy ◽  
Grinding Machine

Thermal errors are one of the most significant factors that influence the machining precision of machine tools. For large-sized gear grinding machine tools, thermal errors of beds, columns and rotary tables are decreased by their huge heat capacity. However, different from machine tools of normal sizes, thermal errors increase with greater power in motorised spindles. Thermal error compensation is generally considered as a relatively effective, convenient and cost-efficient approach in thermal error control and reduction. This article proposes two thermal error prediction models for motorised spindles based on an adaptive neuro-fuzzy inference system and support vector machine, respectively. In the adaptive neuro-fuzzy inference system–based model, the temperature values are divided into different groups using subtractive clustering. A hybrid learning scheme is adopted to adjust membership functions so as to learn from the input data. In the particle swarm optimisation support vector machine–based model, particle swarm optimisation is used to optimise the hyperparameters of the established model. Thermal balance experiments are conducted on a large-sized computer numerical control gear grinding machine tool to establish the prediction models. Comparative results show that the adaptive neuro-fuzzy inference system model has higher prediction accuracy (with residual errors within ±2.5 μm in the radial direction and ±3 μm in the axial direction) than the support vector machine model.

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