Optimization of High Speed Machine Tool Spindle to Minimize Thermal Distortion

Grama, Srinivas N.; Mathur, Ashvarya; Aralaguppi, Ramesh H.; Subramanian, T. L.

doi:10.1016/j.procir.2017.03.253

Cited by 17 publications

(7 citation statements)

References 12 publications

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“…Even displacements of up to several tenths of a millimeter can occur [35]. Research in which the influence of temperature rise on the spindle elongation is quantified can be found, for example, in [11,[36][37][38][39][40][41][42][43]. In general, the displacement of the TCP can be described by the superposition of the thermally induced displacements of the machine components within the thermo-mechanical effect chain.…”

Section: Influence Of Thermal Load On Manufacturing Accuracymentioning

confidence: 99%

Cooling of motor spindles—a review

Denkena

Bergmann

Klemme

2020

Int J Adv Manuf Technol

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Thermally induced loads in motor spindles can cause a number of undesired effects. As a result, the process capability of spindles, and thus, the productivity of a process can decrease. Future motor spindles will be exposed to higher mechanical and especially thermal loads due to trends aiming to increase power densities and maximum speeds. These trends are amplified by increasingly powerful drive concepts and developments in bearing technology. Therefore, researchers assume that it will not be possible to raise the performance potential of spindles due to insufficient cooling of its heat sources. A series of different cooling concepts have been researched and developed in recent decades. These developments have been made for different purposes. They also differ considerably in terms of their cooling principles and cooling performance. In this article, these cooling approaches and the motivations for their development are described. Firstly, the causes of heat development in motor spindles are described in a historical context. Subsequently, the effects of heat development on the manufacturing-relevant properties of motor spindles are revealed. Finally, current deficits in the area of spindle cooling and the need for the development and transfer into industrial practice of more efficient and cost-effective cooling concepts to overcome future challenges are discussed.

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Section: Influence Of Thermal Load On Manufacturing Accuracymentioning

confidence: 99%

Cooling of motor spindles—a review

Denkena

Bergmann

Klemme

2020

Int J Adv Manuf Technol

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“…They used regression theory to establish a thermal error correction model for machine tool positioning and straightness, validating its accuracy on the QLM27100-5X experimental platform of a five-axis linked gantry machine tool sourced from Xiqiao Lian CNC Machine Tool Sales Co., Ltd., located in Wuxi, China. Combining principal component analysis and fuzzy clustering analysis, Grama [11] established a compensation model within a linear regression framework using selected temperature sensors. Based on the analysis of heat generation and transfer in a ball screw system, Hu Shi [12] mathematically modeled the axial thermal expansion of the screw, expressing the relationship between thermal errors and axial elongation rates to characterize the distribution of thermal errors.…”

Section: Introductionmentioning

confidence: 99%

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Fang,

Qian,

et al. 2024

Electronics

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Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.

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“…Ye et al [7] proposed a thermal error regression model for determining the thermal deformation coefficient of the moving shaft of a gantry milling machine. Grama et al [8] developed a thermal error compensation model in a linear regression framework, where the thermal key point was selected using principal component analysis and k-means clustering. Li et al [9] established a thermal error prediction model based on an improved backpropagation (BP) neural network.…”

Section: Introductionmentioning

confidence: 99%

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

Liu

et al. 2021

Adv. Manuf.

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The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

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Optimization of High Speed Machine Tool Spindle to Minimize Thermal Distortion

Cited by 17 publications

References 12 publications

Cooling of motor spindles—a review

Cooling of motor spindles—a review

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

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