Prediction Of Thermal Deformation Research Articles

Multi-verse optimizer for thermal error modeling approach of spindle system based on thermal image

Since the spindle thermal error of CNC machine tools has a significant impact on machining precision, this paper introduces a unique approach for modeling spindle thermal error. Several key steps are involved in the proposed approach. First, the Fluke thermal imaging camera is employed for acquiring thermal image information of the spindle system. Second, the Gaussian filter is employed to denoise the thermal image sequence. Next, the temperature values at the measurement points are extracted from the thermal image sequence according to the mapping relationship between the grayscale value and the temperature value. Subsequently, critical temperature points are identified from thermal images using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the correlation coefficient method. Finally, the multi-verse optimized NARX neural network is employed to investigate the nonlinear prediction of thermal deformation. The research is conducted on the VMC-850E vertical machining center as the subject of study. The performance of the model is validated under conditions of idle spindle and 5000 r/min, comparing prediction results against traditional algorithms. The findings demonstrate that the non-contact measurement method based on thermal imaging successfully establishes the thermal error model, achieving a prediction accuracy of 0.1517 μm for the MVO-NARX model.

Thermal deformation prediction for spindle system of machining center based on multi-source heterogeneous information fusion

Thermal deformation prediction for spindle system of machining center based on multi-source heterogeneous information fusion

Improved Accuracy of Prediction of Thermal Deformation of the Stern Shaft Block Considering the Erection Process

Thermo-elasto-plastic analysis using nonlinear finite element analysis is used for predicting welding-induced structural deformation; however, its application in the prediction of welding deformation in large structures, such as ships and offshore plants, is practically impossible. Therefore, in this study, an inherent strain-based thermal deformation analysis method, which can be applied to large structures, was developed; furthermore, the inherent strain-based analysis was used for predicting the welding deformation occurring during block erection welding. However, although the tendency is somewhat similar in terms of quality, it still differs from the actual measured value in terms of quantity. In this study, a regression equation was derived by analyzing the sensitivity of the variables used in the existing method, and, subsequently, the results corresponding to the measured values were obtained; finally, the accuracy improvement for the three ships was confirmed. Therefore, using the proposed method, it is possible to improve the technology of predicting and controlling hull deformation.

Prediction of Thermal Deformation and Real-Time Error Compensation of a CNC Milling Machine in Cutting Processes

This study constructs a prediction model of thermal deformation with an artificial neural network and implements the real-time error compensation for a three-axis vertical CNC milling machine in cutting processes to improve the thermal error of the workpiece. There are 32 PT-100 thermal sensors installed in key parts of the machine in order to measure the temperature of key machine parts in actual cutting conditions. Pearson’s correlation coefficients are used to select crucial temperature sensors for building the prediction model of thermal deformation. The reduced number of crucial temperature sensors in model construction can simplify the model complexity and speed up the response time of prediction. This study constructs a long short-term memory (LSTM) neural network model to predict the thermal error of the machine in cutting processes. This prediction model of thermal deformation can be further used in real-time error compensation of the workpiece in cutting processes. In an 8 h cutting experiment, the dimensions of the workpiece show that, with real-time error compensation, the thermal error in X-axis decreases from 7 µm to 3 µm, the thermal error in Y-axis decreases from 74 µm to 21 µm, and the thermal error in Z-axis decreases from −64 µm to −20 µm. The results indicate that the prediction model of thermal deformation and the real-time error compensation can significantly reduce the thermal error and improve the dimensional accuracy of the workpiece.

Effect of Coolant Temperature on the Thermal Compensation of a Machine Tool

Machine tool (MT) accuracy is an important factor in the industry and is affected by heat generation through internal and external moving parts; the electrical components used; and variable environmental temperatures. Thermal errors lead to 40–60% of all MT errors. To improve MT accuracy, efficient techniques to minimize thermal errors must be identified. This study investigated the coolant temperature effects under different rotating speeds of a standalone built-in spindle system and computer numerical control (CNC) machine with a direct-drive spindle on the accuracy of thermal deformation prediction. The z-axis thermal deformation of the standalone built-in spindle system and CNC machine with a direct-drive spindle was conducted at different spindle rotating speeds and coolant temperatures at a constant coolant flow rate of 5 LPM. All experiments were conducted in a steady and dynamic operation according to ISO 230-3. For the standalone built-in spindle system, in comparison to the Mares model, the developed new model based on the coolant temperature effect on the Mares model (Mares CT model) can improve the thermal deformation prediction accuracy by 18.17% to 39.50% at different coolant temperatures of 12 ∘C to 26 ∘C and the accuracy can be controlled within the range of 0.03 μm to 5.24 μm, while the supply coolant temperature is above 16 ∘C. However, the thermal compensation analysis of the Mares CT model for a CNC machine with a direct-drive spindle shows a thermal deformation prediction accuracy improvement of 58.30% to 66.35% at different coolant temperatures of 22 ∘C to 28 ∘C and the accuracy can be controlled within the range of 0.14 μm to 4.05 μm. To validate the feasibility of the compensation model in real machining processes, dynamic operational analysis was performed for a standalone built-in spindle system and a CNC machine with a direct-drive spindle, and the thermal deformation prediction accuracy improved by 12.19% to 35.53% with the standalone built-in spindle system and 40.25% to 60.33% with the CNC machine with a direct-drive spindle. The compensation model analysis shows that the coolant temperature has a high impact on thermal deformation prediction and markedly affects system accuracy within certain limits.

Positioning Accuracy and Numerical Analysis of the Main Casting Mechanism of the Hybrid Casting Robot

The positioning accuracy is a key index to measure the performance of the robot. This paper studies the positioning accuracy of the main pouring mechanism of the hybrid truss pouring robot and analyzes that the main error sources affecting the positioning accuracy are machining error, assembly error, and thermal deformation error. Error transfer matrix is constructed to describe the influence of machining errors and assembly errors on the position and pose of the terminal, and the error parameters have physical significance. The probability distribution of sensitive errors is discussed. A joint regression prediction model based on sensitive error sets is established to determine the thermal deformation error on the basis of fully considering the contribution rate of component error. The results show that the position error has a wide range of influences on the end pose, but the angle error is more sensitive, and the probability distribution of the sensitive error is concentrated. The reliable data can be obtained without reorganizing the measurement in the calibration process. The joint regression model considering the contribution rate of component error can effectively eliminate the collinearity problem in the prediction of thermal deformation from a single heat source. Compared with the single regression model, it has better prediction accuracy and effect.

Thermal Deformation Modeling for Phased Array Antenna Compensation Control.

Thermal compensation control can correct errors caused by the thermal deformation of phased array antenna (PAA) panels. Thermal deformation of the panel is needed to calculate the compensation value. While the PAA is working, thermal deformation is unconditional to measure, but predicting it by temperature is feasible. However, thermal deformation is also affected by other factors, such as the structural shape, assembly method, and material parameters, and it is difficult to measure these parameters of PAA because of the complex structure. In contrast, the measurement method of the temperature and thermal deformation of the PAA in the laboratory is much easier. Therefore, a comprehensive influence parameters (CIPs)-finite element method (FEM) method was proposed in this study, it can extract the influence of above parameters on thermal deformation from temperature and thermal deformation measurement data and build a thermal deformation prediction model. Experiments have verified that the CIPs-FEM can greatly reduce the difficulty of thermal deformation modeling and have a high prediction accuracy.

Structural components with sensing capability of three-dimensional temperature distribution for thermal deformation prediction

Approximately 70% of the errors in machining processes are attributed to thermal deformation. In this regard, the compensation scheme is effective in canceling out thermal deformation and maintaining good machining quality, but it requires a fine measurement of the temperature distribution of the machine structure. Thus, in this study, a structural component for machine tools with a large-scale array of temperature sensors interconnected in series (LATSIS) is proposed. The three-dimensional temperature distribution is obtained by the embedded LATSIS, which potentially realizes thermal error compensation with excellent accuracy. The main purpose of this research is to evaluate the potential superiority of the proposed composite material compared to conventional materials, such as damping properties, weight, and thermal compensation capability. Moreover, a block-shaped structural component made of polymer concrete (PC) embedded in a steel shell was designed. The LATSIS was also embedded in the PC. For comparison, another structure was designed with cast iron to have a similar rigidity and fit in a design space similar to that of the proposed component. Intermittent heating experiments were conducted using components fixed as a cantilever. The temperature and displacement changes at the edges of each cantilever were collected using LATSIS and laser displacement meters, respectively. By interpolating the temperature data and obtaining the temperature distributions, the thermal deformations using thermal finite element method analysis were predicted. The results indicated that the thermal deformation could be predicted more accurately in the structure made with the proposed composite material compared to that made of cast iron.

Determination of thermal deformation prediction formula for CNC lathe by a small number of experiments using the all pair method

The thermal deformation of machine tools often reduces their accuracy. Therefore, we have been trying to compensate for the thermal deformation by predicting their variations and adjusting the position of the tool according to predicted values. The prediction equation of the thermal deformation in the CNC lathe is determined by measuring the temperature variations at a few specific points of the machine. In order to obtain such coefficients with a few experiments, the previous study has used an orthogonal array to determine the experimental condition. However, orthogonal arrays have specific patterns, so it cannot create the flexible condition. This study attempts to use All pair method for determining the prediction equation more efficiently, which is normally used in software testing, it can make testing patterns have all combinations of all parameters in a few experiments. Although all pair method is not widely used in the mechanical experiment, its effectiveness is confirmed.

A hyper-reduction computational method for accelerated modeling of thermal cycling-induced plastic deformations

For materials under cyclic thermal loadings, temperature and strain rate-dependent creep deformation can occur due to the thermal expansion mismatch near material interfaces, leading to deterioration of fatigue life. Simulation of the nonlinear mechanical deformation processes of materials subjected to thermal cycling with high-fidelity numerical models often consume high computational costs due to material nonlinearities and long thermal loading period. To accelerate such thermal cycling simulation, an efficient reduced-order modeling framework is proposed. We adopt the reproducing kernel particle method (RKPM) to generate offline high-fidelity model snapshots, which are utilized to create low-dimensional surrogate models based on the proper orthogonal decomposition under Galerkin projection. Based on the stabilized conforming nodal integration technique, gradient smoothing at single integration point per integration cell is employed in conjunction with a least-squares stabilization for high computational efficiency. To further speed up the simulation, a hyper-reduction method is introduced to avoid the full domain integration during the elastoplastic online simulation. Numerical examples on modeling of thermal cycling-induced plastic deformation and thermal fatigue life prediction of a flip chip assembly are analyzed to demonstrate the effectiveness of the present hyper-reduced-order model in significant reduction of computational time while preserving desired accuracy.

Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending

This paper describes a quick and accurate method for predicting thermal deformation due to flame bending of the curved plate located before and after the hull. Flame bending is a common method to deform the curved plate used in shipyards. Three-dimensional thermo-elasto-plastic analysis is known as the most accurate method for predicting deformed shape in the automation of frame bending. However, the three-dimensional analysis takes a lot of computational time. The quick prediction method, strain as direct boundary (SDB), was introduced, which is a simplified prediction method based on thermal strain. This simplified method implements an equivalent load as a temperature difference that can simulate thermal deformation by flame. In the case of multiple heating lines by the flame bending, the residual strain generated by the first heating line affects the other lines. To consider the effect of residual strain, the plastic material properties are also considered. Then, the distance ratio from the center line is used to generate the same temperature field in grid mesh. The results of the prediction were evaluated for the heat affected zone (HAZ) of the specimen obtained through the flame bending experiment. Therefore, this paper introduced detail procedure of the proposed SDB method and the experimental results for the practical application.

Soft sensor based on DBN-IPSO-SVR approach for rotor thermal deformation prediction of rotary air-preheater

Abstract The rotor thermal deformation prediction is a challenging task due to the sophisticated processes involved in rotary air-preheater and limited hardware sensors, so it is necessary to employ a soft sensor model to get the accurate deformation value for the existing sealing technology. Additionally, aiming at the problems of low accuracy and underutilization of data of traditional methods, a novel soft sensor for air-preheater based on DBN-IPSO-SVR is proposed. The grey relational analysis (GRA) method is employed to provide reliable input variables for model training. The deep belief network (DBN) and the support vector regression (SVR) with the improved particle swarm optimization (IPSO) as data-driven model are employed to extract the features in the data. The results demonstrate the IPSO can obtain better parameters, the proposed soft sensor model significantly improved the performance of rotor thermal deformation prediction and is therefore a valuable non-contact measure tool for controlling air leakage.

Prediction of Thermal Deformation of Rotary Table in Multifunction Machine Tool Using Neural Networks

Prediction of Thermal Deformation of Rotary Table in Multifunction Machine Tool Using Neural Networks

Determination of thermal deformation prediction equation for a compact CNC lathe by using experimental design method

Determination of thermal deformation prediction equation for a compact CNC lathe by using experimental design method

Thermal deformation prediction of high-speed motorized spindle based on thermal behaviour simulation

This paper conduct research to the temperature field distribution and spindle’s thermal deformation of motorized spindle by applying finite element thermal-structural coupled numerical simulation technology. And this paper also researched the radial displacement produced by the inner and outer ring’s axial displacement of the angular contact ball bearing. Thus the combination between the finite element thermal-structural coupled numerical simulation technology and thermal error analytic modelling is realized. The analysis results of motorized spindle error is made. The analysis results show that this method is effective in predicting the temperature and thermal error of motorized spindle.

A Theoretical Solution for Thermal Warpage of Flip-Chip Packages

The control of thermally induced warpage on electronic packages is an important issue during their fabrication process and assembly. Apart from a commonly used finite-element method and experimental methods for thermally induced warpage analysis of the packages, it still needs a fundamental theory to provide an insight of mechanics to assist and further check the finite-element and experiment-obtained results, especially for critical parameter identification. This article aims to take a close look into the well-known, closed-form Suhir theory for calculating thermally induced deformation of die–substrate assembly and correlate it with a finite-element analysis and further to propose a new Suhir-solution-based theory for predicting the thermal deformation of flip-chip packages. Through this article, the shear-effect region has been found, and its effect on curvature distribution over entire package length and corresponding warpage has been examined in terms of elastic modulus and thickness of underfills. The other parameters such as extended substrate and fillets of underfill are also studied. The limitation and feasibility of the theoretical solution to thermal deformation (warpage) prediction of flip-chip packages are discussed in detail by comparing with the finite-element and moire experimental results in this article.

Real-time thermal deformation compensation method for active phased array antenna panels

The electromagnetic (EM) performance of active phased array antennas (APAAs) is significantly influenced by thermal deformation of the antenna panels caused by thermal power consumption and ambient temperature change. This paper proposes a method to reduce the influence of thermal deformation on the EM performance of small APAAs via real-time compensation. In the proposed method, a thermal deformation prediction model is first constructed based on analysis of experimental data. The model is then used to accurately predict thermal deformation based on the measured values of several key temperature sensors in real time, and the EM performance is compensated by adjusting the array phase difference controlled by a phased shifter according to the predicted results. The results of experiments conducted indicate that the thermal deformation model has high prediction accuracy, and the decrease in EM performance caused by antenna panel thermal deformation is effectively reduced by the proposed method. Thus, the proposed method has positive implications for improving the overall performance of APAAs.

Thermal deformation prediction and reducing of machine tool

Thermal deformation prediction and reducing of machine tool

Thermal monitoring and thermal deformation prediction for spherical machine tool spindles

Machine tool operations and processing can cause temperature changes in various components because of internal and external thermal effects. Thermal deformations caused by thermal effect in machine tools can result in errors in processing size or shape and decrease processing precision. Thus, this paper focuses on the analysis of heating during machine tool spindle?s high speed operation, which is the heat source that causes component and structural deformation. In this paper, thermal monitoring was used to build a thermal error prediction model. Temperature change around the spindle was measured with a DS18B20, then multiple regression analysis was used to establish the relationship between thermal deformation quantity and temperature fields at specific points. Finally, finite element analysis was used to build the thermal error model. A solution for the correlation coefficient was obtained using the least squares method. The result of this study verified that finite element analysis can predict front bearing and rear bearing temperature rise, and is consistent with laboratory results. The error in thermal steady-state deformation prediction was less than 2 ?m. This information can be used by the controller to effectively compensate the processing and improve processing precision.

Thermal stress deformation prediction for rotary air-preheater rotor using deep learning approach

Failures often occur in the seal clearance measuring sensor due to the harsh operating conditions of the rotary air-preheater in power plant boilers. Therefore, it is necessary to predict the rotor deformation to eliminate the effects of failures on the gap control system. An air-preheater rotor thermal stress deformation prediction method is proposed in this paper based on deep learning. Firstly, a stacked auto-encoder (SAE) is constructed and trained to learn the feature information which is hidden within the input data (the temperature of flue gas side inlet, air side outlet, flue gas side outlet, air side inlet); then, an Elman neural network is constructed and trained using the output of the encoder part of the well trained stacked auto-encoder to predict rotor deformation. Simulation and experimental results show that the proposed SAE-Elman prediction method can obtain the effective feature representation and has better prediction precision compared with other traditional prediction methods.