Research on thermal deformation of large CNC gear profile grinding machine tools

2016 ◽  
Vol 91 (1-4) ◽  
pp. 577-587 ◽  
Author(s):  
Shilong Wang ◽  
Baocang Zhou ◽  
Chenggang Fang ◽  
Shouli Sun
Keyword(s):  
Machine Tools ◽  
Thermal Deformation ◽  
Profile Grinding ◽  
Grinding Machine ◽  
Gear Profile

Probe Position Planning for Measuring Cylindrical Gears on a Four-Axis CNC Machine

2012 ◽  
Vol 579 ◽  
pp. 297-311
Author(s):  
Yi Hui Lee ◽  
Shih Syun Lin ◽  
Yi Pei Shih
Keyword(s):  
Mathematical Models ◽  
Gear Tooth ◽  
Experimental Result ◽  
Tooth Surface ◽  
Measuring Process ◽  
Profile Grinding ◽  
Cylindrical Gears ◽  
Large Size ◽  
Grinding Machine ◽  
Gear Profile

During large-size gear manufacturing by form grinding, the actual tooth surfaces will differ from the theoretical tooth surface because of errors in the clamping fixture and machine axes and machining deflection. Therefore, to improve gear precision, the gear tooth deviations should be measured first and the flank correction implemented based on these deviations. To address the difficulty in large-size gear transit, we develop an on-machine scanning measurement for cylindrical gears on the five-axis CNC gear profile grinding machine that can measure the gear tooth deviations on the machine immediately after grinding, but only four axes are needed for the measurement. Our results can serve as a foundation for follow-up research on closed-loop flank correction technology. This measuring process, which is based on the AGMA standards, includes the (1) profile deviation, (2) helix deviation, (3) pitch deviation, and (4) flank topographic deviation. The mathematical models for measuring probe positioning are derived using the base circle method. We also calculate measuring positions that can serve as a basis for programming the NC codes of the measuring process. Finally, instead of the gear profile grinding machine, we used the six-axis CNC hypoid gear cutting machine for measuring experiments to verify the proposed mathematical models, and the experimental result was compared with Klingelnberg P40 gear measuring center.

Free-Form Flank Correction in Helical Gear Grinding Using a Five-Axis Computer Numerical Control Gear Profile Grinding Machine

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Yi-Pei Shih ◽  
Shi-Duang Chen
Keyword(s):  
Correction Method ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Free Form ◽  
Profile Grinding ◽  
Wheel Profile ◽  
Grinding Machine ◽  
Tooth Flank ◽  
Five Axis ◽  
Gear Profile

To reduce form grinding errors, this paper proposes a free-form flank topographic correction method based on a five-axis computer numerical control (CNC) gear profile grinding machine. This correction method is applied not only to the five-axis machine settings (during grinding) but also to the wheel profile (during wheel truing). To achieve free-form modification of the wheel profile, the wheel is formulated as B-spline curves using a curve fitting technique and then normal correction functions made up of four-degree polynomials are added into its working curves. Additionally, each axis of the grinding machine is formulated as a six-degree polynomial. Based on a sensitivity analysis of the polynomial coefficients (normal correction functions and CNC machine settings) on the ground tooth flank and the topographic flank errors, the corrections are solved using the least squares method. The ground tooth flank errors can then be efficiently reduced by slightly adjusting the wheel profile and five-axis movement according to the solved corrections. The validity of this flank correction method for helical gears is numerically demonstrated using the five-axis CNC gear profile grinding machine.

Thermal Characteristic Analysis CNC Multi-Grinding Machine

2015 ◽  
Vol 764-765 ◽  
pp. 398-402
Author(s):  
Gyung Tae Bae ◽  
Bo Sung Kim ◽  
Ji Hun Pak ◽  
Hong Man Moon ◽  
Jung Pil Noh ◽  
...  
Keyword(s):  
Structural Analysis ◽  
Machine Tools ◽  
Thermal Deformation ◽  
Thermal Characteristic ◽  
Thermal Characteristics ◽  
Characteristic Analysis ◽  
Hydrostatic Bearing ◽  
Processing Accuracy ◽  
Grinding Machine ◽  
Deformation Error

Recently, it is essential to enhance the value of the products to make them more competitive. Therefore, the technical level of the high-precision products is required. Thermal deformation error, which accounts for a significant effect of processing accuracy of machine tools. In order to reduce thermal deformation error such studies the thermal characteristics of the Hydrostatic spindle is required. In this study, we could confirm the reliability of the analysis by assessing the thermal characteristics through measurement of the grinding machine temperature and thermal structural analysis. The temperature of the front bearing 10 °C or more higher than the temperature of the rear bearing, thermal deformation of the spindle, was found to be dependent on the temperature of the hydrostatic bearing. And could identify the thermal characteristics of the hydrostatic spindle.

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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