Thermally induced positioning error modelling and compensation based on thermal characteristic analysis

Feng, Wenlong; Li, Zihan; Gu, Qiushi; Yang, Jing

doi:10.1016/j.ijmachtools.2015.03.006

Cited by 67 publications

(33 citation statements)

References 17 publications

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“…Technical Gazette 24, 6(2017), 1697-1703 Many methods are known for minimising the unfavourable effects of this phenomenon. The literature describes methods based on design, manner of operation, thermal stabilisation and thermal compensation [10][11][12]. The most common solutions are a direct measurement system, cooling of the ball screw transmission, or preheating of the machine tool [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

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Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

2017

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Original scientific paper The need to improve the accuracy of positioning of a servo-drive was the stimulus for research on a new sensorless method for compensation of thermal deformations of ball screws, enabling predictive compensation of the elongation of such a screw based on historical data. Models have been developed for the predictive compensation of thermal deformations of ball screws in CNC machines, in the form of single-directional multi-layered neural networks with error back-propagation (MLP), radial basis function neural networks (RBF) and Kohonen networks. Neural networks were developed with different structures and learning parameters, and these networks were compared. Models were evaluated in terms of the effectiveness of operation of the networks. The models were tested on real data. Keywords: ball screw; CNC machine tool; neural network; prediction model; thermal compensation Predvidiva kompenzacija toplinskih deformiranja kuglastih vijaka u CNC strojevima primjenom neuronskih mrežaIzvorni znanstveni članak Potreba za povećanjem točnosti pozicioniranja servo-pogona bila je poticaj za istraživanje nove metode bez senzora za kompenzaciju toplinskih deformiranja kuglastih vijaka, koja će omogućiti predvidivu kompenzaciju izduženja takvih vijaka na temelju prikupljenih podataka. Razvijeni su modeli za predvidivu kompenzaciju toplinskih deformiranja kuglastih vijaka u CNC strojevima, u obliku jednosmjernih višeslojnih neuronskih mreža s unatražnim rasprostiranjem greške (MLP), neuronskih mreža s funkcijom radijalne baze (RBF) i Kohonen mreža. Razvijene su neuronske mreže s različitim strukturama i parametrima učenja, i te su se mreže uspoređivale. Modeli su se procjenjivali prema učinkovitosti mreža. Modeli su se ispitivali s realnim podacima.

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Section: Literature Reviewmentioning

confidence: 99%

“…The search continues for an effective, universal, simple and cheap method of compensating for the elongation of ball screw transmission systems [6][7][8][9][10][11][12][15][16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

2017

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“…Configuration error, dimension error Total error of whole grinding machine support vector machine. (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) Meanwhile, many auxiliary methods for thermal error modelling were proposed, e.g., modelling with data preprocessed methods, different models for different machine parts, and multimode models [e.g., particle swarm optimization (PSO), ant colony optimization (ANT), genetic algorithm (GA), and Grey system theorem (GT)]. (16)(17)(18) The compensation accuracy of machine tools was apparently improved via the use of the abovementioned thermal error models.…”

Section: Introductionmentioning

confidence: 99%

Sensing and Compensating the Thermal Deformation of a Computer-numerical-control Grinding Machine Using a Hybrid Deep-learning Neural Network Scheme

Wang¹,

Shen²,

Yang³

et al. 2019

Sensors and Materials

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Thermal error plays a deterministic role in the machining precision of computer-numericalcontrol (CNC) tool machinery. Previously, three ways had been proposed to overcome thermal error problems: prevention, restraint, and compensation. The first two ways may be performed in the initial design stage. The last one includes the challengeable features of caseby-case simulation of cutting paths, searching for characteristic temperature points, thermal deformation measurement, and establishing an accurate thermal model. Different from most of the previous studies concerning mathematical thermal models, which have many restrictions and disadvantages, in this study, we propose a novel hybrid thermal error modelling scheme of the Grey system theorem and deep-learning neural network. Specifically, a linear-guideway grinding machine, never seen in previous thermal-error-compensation-related studies, was chosen as the target to identify the usefulness of our proposed scheme. Results show that the proposed hybrid model has a comprehensive prediction ability of thermal behavior for the target CNC grinding machine.

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“…Improvements in the error avoidance approach are strongly limited by the existing technologies and by the cost factor [7,8], and consequently error compensation has become an important research topic in the field of high-precision machine tools. Precision in the mentioned CNC machine tools is mainly affected by geometric and thermal deformation errors, which are responsible for more than 60% of the total machining error [9]. They do not require a real-time correction because they are static or quasi-static errors and they are usually compensated by thorough but expensive calibration procedures, allowing the acquisition of the volumetric error of the machine.…”

mentioning

confidence: 99%

“…In most cases applications described in literature are focused only on one specific aspect of the machine. This seems to be a critical aspect, since some works just consider the compensation of thermal errors [9,15], others focus only on the speed of a predictive and compensative algorithm (without considering the data it can treat) and others only implement software for better errors measurement [16,17]. Improve machine error compensation by applying software methods which consider the entirety of the factors is still an ongoing goal.…”

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confidence: 99%

Experimental Results Of A Self-Learning Compensation System For High Precision Manufacturing

Silvestri¹,

Fontanesi²,

Carnevale³

2019

IJITEE

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 Abstract: This paper presents the experimental results obtained by applying the results of some recently concluded European projects, whose objective was the development and validation of hardware and control systems for production, based on understanding, evaluating and controlling the performance of a machine tool. It is based on a self-learning controller capable of managing a large quantity of data acquired by sensor systems, as well as on-board artifacts and finished work piece measurements that, associated to operating conditions, permit the accumulation of knowledge regarding the behaviour of machines. Relying on this experience-based approach, the controller can predict the errors that a machining process will present under different conditions and can thus adapt compensation tables. The approach set out has been implemented in a demonstrator consisting of a 5-axis high-precision boring machine, fully functional in an industrial shop floor but used under controlled environmental conditions (thermostatic chamber and special machine foundations). Its software system supports measurement procedures and is able to integrate data acquisition from different sensor systems, to calculate the volumetric error with 3D representations, to provide models for calculation of error functions and to integrate communication processes with the CNC. It can therefore operate in actual production sites, introducing relevant improvements in the machine tools manufacturing field. This paper presents the experimental results obtained during the project validation, including a comparison between on field measurements and compensation tables calculated on the basis of the predictions of the self-learning system. The analysis of the data gathered highlights the system's capability to deal with both simple linear dependencies (e.g. between error and mass variation) and complex, non-linear but repeatable trends. Results discussion, relating to two different and independent axis, demonstrates the applicability of the system under real operating conditions.

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Thermally induced positioning error modelling and compensation based on thermal characteristic analysis

Cited by 67 publications

References 17 publications

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Sensing and Compensating the Thermal Deformation of a Computer-numerical-control Grinding Machine Using a Hybrid Deep-learning Neural Network Scheme

Experimental Results Of A Self-Learning Compensation System For High Precision Manufacturing

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