Thermal displacement reduction and compensation of a turning center

Fujishima, Makoto; Narimatsu, Koichiro; Irino, Naruhiro; Ido, Yutaka

doi:10.1016/j.cirpj.2018.04.003

Cited by 23 publications

(8 citation statements)

References 10 publications

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“…However, as obviously seen in robotics and autonomous vehicles, these techniques are used to develop intelligent features of smart products. Within the CIRP community, deep learning techniques have been applied to the following design and manufacturing research topics; cutting process monitoring [78], cutting process planning [79], process planning for polishing [265], robotics [140,266], feature extraction for inspection [275], optimisation for process planning [180], planning for machine allocation [188], operation scheduling [271], diagnostics [278,296], planning and management of energy consumption [46], and design [268].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Development capabilities for smart products

Tomiyama¹,

et al. 2019

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Development capabilities for smart products

Tomiyama¹,

et al. 2019

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“…According to [22] and [23], it is also suggested that linear axis thermal positioning error varies considerably under different feeding velocities. In this case, a synthetic model, considered to be appropriate for any operational conditions, needs to be built to generaly describe the thermal positioning error of a linear axis, where both geometric and thermal errors are taken into account simultaneously.…”

Section: Mechanisms Of Decoupling and Separating Thermal Positioning ...mentioning

confidence: 99%

A novel approach for decoupling and separating the thermal positioning errors of machining center linear axes

Yao¹,

Teng

Wang³

et al. 2023

Preprint

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CNC machining center linear axis thermal positioning errors, seen as the synthetic consequences of geometric and thermal errors, respectively generated due to the manufacturing and assembling inaccuracies and the asymmetric thermal deformation of the machining center structure, are significantly affected by varying position of the cutting point and shifting state of temperature field. Hence, developing a practical approach to reduce or even eliminate thermal positioning errors is crucial. This paper proposes a novel approach to decouple and separate machining center linear axis thermal positioning errors, based on which a highly accurate prediction model of the thermal positioning error is formulated. Firstly, a new concept on thermal positioning error sensitivity is presented where grey correlation analysis is borrowed to characterize the mapping between varying temperature fields and thermal positioning errors, according to which the sensor sensitivities and distributions are derived and optimized, respectively. Then, the thermal positioning errors are decoupled and separated into geometric and thermal errors by adopting multiple linear regression and GM (1, n) algorithms, respectively. Finally, the corresponding embedded compensation module is also developed within the SIEMENS 840D CNC system to realize the online compensation strategy providing the engineering applications. Experimental validations are performed on a commercial machining center, where the thermal positioning errors of the Z-axis are measured with the help of a laser interferometer testing kit and a thermal inspection instrument. The data comparisons indicate that the maximum thermal positioning errors of the Z-axis in the cold and warm state are respectively decreased for 86.5% and 71.6% after activating the compensation module, which also suggests that the proposed approach is adequate and accurate to decouple and separate the thermal positioning errors.

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“…(2) Among these methods, thermal error compensation is not only easy but also efficient. (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) Thus, it is the most commonly used way of enhancing the machining accuracy of machine tools. Generally speaking, a thermal error compensation procedure includes the following steps.…”

Section: Introductionmentioning

confidence: 99%

Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

Wang¹,

Yang²,

Wu³

et al. 2021

Sensors and Materials

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To produce a good machine tool, the thermally induced error in the machine during machining plays a crucial role and is an important issue needing to be resolved. The thermal error may account for 70% of the total error. There are three main approaches to solving the thermal error problem: preventing heat flows from hot components, designing a thermally stable structure for the machine, and compensating the thermal error using thermal error models. The first two approaches can be carried out in the primary design stage of machine tools, and they have been used in the manufacture of commercial products. The third approach, the strategy of thermal error compensation, is the most effective and popular approach. However, there are still many unsolved problems. Among these problems, the cutting conditions have a significant influence on the modeling precision of the thermal error. In this study, we develop an integral model based on the integrated grey system theory (IGST) in conjunction with a genetic-algorithm (GA)-optimized back-propagation neural network (BPNN) to investigate the influence of cutting conditions on a machine tool's thermal error. The model is chosen on account of its high ability in dealing with a small amount of training data. Results show that a single thermal error modeling formula cannot make accurate predictions for different cutting conditions. Suitable adjustment of the modeling parameters or the use of a multiple modeling scheme is needed.

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Thermal displacement reduction and compensation of a turning center

Cited by 23 publications

References 10 publications

Development capabilities for smart products

Development capabilities for smart products

A novel approach for decoupling and separating the thermal positioning errors of machining center linear axes

Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

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