Application of the digital model thermal errors of machine tools in automated production

Abstract The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

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Thermal Error Modeling of Machine Tools Based on M-RAN

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.529 ◽

2011 ◽

Vol 121-126 ◽

pp. 529-533

Author(s):

Jian Han ◽

Li Ping Wang ◽

Ning Bo Cheng ◽

Xu Wang

Keyword(s):

Machine Tool ◽

Eddy Current ◽

Machine Tools ◽

Thermal Error ◽

Error Modeling ◽

Test Results ◽

Thermal Errors ◽

Machining Center ◽

Machining Errors ◽

Minimal Resource

Thermal error in machine tools is one of the most significant causes of machining errors. This paper presents a new modeling method for machine tool error. Minimal-resource allocating networks (M-RAN) are used to establish the relationships between the temperature variables and thermal errors. Pt-100 thermal resistances and eddy current sensors are used to measure the temperature variables and the thermal errors respectively. A machining center is used to experiment. The test results show that method with minimal-resource allocating networks can predict the thermal errors of the machine accurately.

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Thermal error analysis of taurenEDM machine tool based on FCM fuzzy clustering and RBF neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202241 ◽

2021 ◽

pp. 1-12

Author(s):

Jianyong Liu ◽

Yanhua Cai ◽

Qinjian Zhang ◽

Haifeng Zhang ◽

Hu He ◽

...

Keyword(s):

Neural Network ◽

Temperature Field ◽

Prediction Model ◽

Machine Tool ◽

Temperature Measurement ◽

Reference Point ◽

Clustering Algorithm ◽

Rbf Neural Network ◽

Thermal Error ◽

Thermal Errors

A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the taurenEDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the taurenEDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors.

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PREDICTION OF THERMAL ERRORS IN MACHINE TOOLS THROUGH DECOUPLED SIMULATIONS USING GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

MM Science Journal ◽

10.17973/mmsj.2021_7_2021076 ◽

2021 ◽

Vol 2021 (3) ◽

pp. 4683-4691

Author(s):

T. Suresh Kumar ◽

◽

J. Glaenzel ◽

M. Bergmann ◽

M. Putz ◽

...

Keyword(s):

Genetic Algorithm ◽

Machine Tool ◽

Machine Tools ◽

Waste Heat ◽

Internal Heat ◽

Thermal Error ◽

Heat Sources ◽

Thermal Errors ◽

Production Processes ◽

Internal Heat Sources

Thermal errors are one of the major contributors towards positioning discrepancies in machine tools in precision machining. Along with friction and waste heat generated from production processes and internal heat sources, environmental influences around the machine tool create considerable thermal gradients followed by non-linear structural deformations. Efficient quantification of these three contributing sources of thermal errors are required in order to formulate a reliable thermal-error compensation system. The creation of all possible thermal configurations, which a machine tool could be subjected to, is experimentally infeasible and requires complex and time-consuming coupled flow and thermo-structural simulations. This paper presents a new approach in thermal error prediction by using CFD and finite element (FE) simulations to train a three-level interconnected neural network system. The first level essentially decouples flow simulations from thermo-structural simulations using optimal FE node points found using a Genetic Algorithm (GA), which significantly reduces the required training data. The boundary convection data obtained from this level is used in the second level to predict possible thermal configurations of the machine tool, after careful consideration of parameters related to internal heat sources and production processes. The third level maps these thermal configurations onto displacements on the machine tool.

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Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

SN Applied Sciences ◽

10.1007/s42452-021-04427-5 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Jianlei Zhang ◽

Yukun Zeng ◽

Binil Starly

Keyword(s):

Neural Network ◽

Tool Wear ◽

Machine Tool ◽

Recurrent Neural Network ◽

Machine Tools ◽

Prediction Performance ◽

Sequential Data ◽

Diagnosis And Prognosis ◽

Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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Application of Ubiquitous Sensor Network in Collection and Analysis of CNC Machine Tool Processing Status Information

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.455.621 ◽

2010 ◽

Vol 455 ◽

pp. 621-624

Author(s):

X. Li ◽

Y.Y. Yu

Keyword(s):

Machine Tool ◽

Sensor Network ◽

Machine Tools ◽

Thermal Error ◽

Development Trend ◽

Cnc Machine Tools ◽

Cnc Machine Tool ◽

Cnc Machine ◽

Status Information ◽

The Development Trend

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.

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Modeling for Machine Tool Thermal Error Based on Grey Model Preprocessing Neural Network

Journal of Mechanical Engineering ◽

10.3901/jme.2011.07.134 ◽

2011 ◽

Vol 47 (07) ◽

pp. 134 ◽

Cited By ~ 10

Author(s):

Yi ZHANG

Keyword(s):

Neural Network ◽

Machine Tool ◽

Thermal Error ◽

Grey Model

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Numerical Analysis of Thermal Error for a 4-Axises Horizontal Machining Center

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.868.64 ◽

2017 ◽

Vol 868 ◽

pp. 64-68

Author(s):

Yu Bin Huang ◽

Wei Sun ◽

Qing Chao Sun ◽

Yue Ma ◽

Hong Fu Wang

Keyword(s):

Machine Tool ◽

Working Condition ◽

Prediction Method ◽

Thermal Error ◽

Error Modeling ◽

Model Verification ◽

Design Stage ◽

Thermal Errors ◽

Machining Center ◽

Machining Errors

Thermal deformations of machine tool are among the most significant error source of machining errors. Most of current thermal error modeling researches is about 3-axies machine tool, highly reliant on collected date, which could not predict thermal errors in design stage. In This paper, in order to estimate the thermal error of a 4-axise horizontal machining center. A thermal error prediction method in machine tool design stage is proposed. Thermal errors in workspace in different working condition are illustrated through numerical simulation and volumetric error model. Verification experiments shows the outcomes of this prediction method are basically correct.

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Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-019-04375-w ◽

2019 ◽

Vol 105 (1-4) ◽

pp. 1497-1505 ◽

Cited By ~ 9

Author(s):

Bo Li ◽

Xitian Tian ◽

Min Zhang

Keyword(s):

Neural Network ◽

Machine Tool ◽

Bp Neural Network ◽

Thermal Error ◽

Error Modeling ◽

Thermal Error Modeling ◽

Machine Tool Spindle ◽

Improved Algorithm

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

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405417696335 ◽

2017 ◽

Vol 231 (5) ◽

pp. 768-778 ◽

Cited By ~ 7

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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