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The axial thermal errors of CNC lathes are mostly caused by the thermal deformation of the screw and spindle. It is necessary to establish a prediction model to evaluate the thermal error distribution of CNC lathes. First, a new real-time virtual frictional heat model under the time-varying working speed of various kinematic pairs in the screw and spindle systems was proposed using the real-time torque current. Furthermore, a novel one-dimension lumped capacity method, which can be used to determine the temperature field rapidly, was proposed, considering the sensitiveness of the temperature of the measuring point with the heat generation rate. By an inverse FEM thermal-solid method of screw and spindle systems, the friction heat distribution and sensitiveness of the temperature of the measuring point with the heat generation rate were obtained. Consequently, based on the one-dimension lumped capacity method, the real-time thermal error prediction model was established, which is used to predict the temperatures and axial thermal errors of the screw and spindle of the machine tools. From the FANUC digit control system, the servomotor torque currents of the screw and spindle and the position of the moving-nut were read and used as inputs by the FOCAS library function. The effectiveness of the proposed prediction model of thermal errors is verified through the experimental and FEM results. This method uses the servomotor torque current collected by the FOCAS library function to determine the heat boundary condition. It can quickly and accurately determine the thermal errors without requiring additional temperature sensors. [ABSTRACT FROM AUTHOR]

The ball screw system operates under the coupling condition of external loads, preloads, and thermal loads, and its thermo-mechanical characteristics significantly affect its dynamics performance. Therefore, a thermal-mechanical real-time dynamics model is necessary. In this work, the screw systems thermo-mechanical coupling mechanism was first analysed. Second, a new dynamic thermal network model for the screw system with moving heat excitation was proposed using the position of the moving nut obtained by FANUC Open CNC API Specifications (FOCAS), which can rapidly determine the transient temperature field. Based on this model, a new thermo-mechanical coupling real-time dynamics model of the screw system was established. The influence of temperature on dynamics characteristics was analysed using the dynamics model, and the results showed that the system preload caused by thermal elongation of the screw decreases with an increase in temperature. The system preload caused by the thermal deformation of the bearing increases with an increase in temperature. The total preload of the system depends on the interaction between the thermal deformation of the screw and the axial thermal deformation of the bearing. Additionally, the system stiffness and natural frequency variation relative to the increase in temperature and position of the carriage were obtained. Thermo-mechanical dynamics performance experiments of the feed system were carried out to validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The feed system of CNC machine tool has the characteristics of multiple heat sources and strong time-varying heat generation rate of each heat source. However, previous studies have paid little attention to the time-varying heat generation rate of each heat source, so it is difficult to accurately predict the temperature rise and thermal error of the lead screw. In this paper, Monte Carlo (MC) simulation-integrated FEM method was used to capture the heat generation rate of each heat source in the feed system. Consequently, relationship of the ratio of each heat source to the total heat generation rate of the system with the running time was obtained. Then, based on heat ratios of the heat sources, the torque current of the servo motor in the feed system was proposed to calculate the heat generation rate of each heat source. Finally, based on the finite difference method of one-dimensional heat conduction equation, numerical prediction model for thermal error of the lead screw was put forward. Finally, the accuracy and validity of the calculation model were verified by comparing with the experimental results. [ABSTRACT FROM AUTHOR]

The positioning precision of the ball screw system in machine tools is primarily affected by the thermal deformation of the screw. Hence, the adaptive prediction method must be established to calculate thermal errors of the ball screw system. First, based on heat transfer theory, a new adaptive moving thermal network model of the screw system with moving thermal excitation and varying operating conditions is proposed. The time-serial data of kinematic-pair representative temperatures and the time-serial position data of the moving nut are used as inputs. This adaptive model is used to determine the temperatures of the key elements in the supporting bearings and those of the moving nut and key points of the screw shaft. Further, the heat generation rate of each kinematic pair can be obtained simultaneously. Furthermore, a real-time prediction method of thermal errors of the screw system with moving thermal excitation under various operating conditions is presented. The effectiveness and robustness of the real-time prediction method of thermal errors was verified experimentally. This method is suitable for the rapid prediction of thermal errors of screw systems by monitoring the real-time kinematic-pair representative temperatures and real-time position of the moving nut. • A new adaptive moving thermal network model for a screw system is proposed. • Based on the moving network model, the real-time thermal error model is proposed. • The heat generation rates of the heat sources are simultaneously identified in real-time. • The presented model can be used for the multiple varying working conditions. • This model has self-adaptive ability and robustness. [ABSTRACT FROM AUTHOR]

The thermal error of machine tools is one of the main factors affecting the machining accuracy of machine tools. Most of the existed literatures do not consider the randomness of the influencing factors of thermal error, there are still difficulties in accurate prediction and compensation of thermal error. Therefore, the new prediction models with higher accuracy and reliability are urgently needed. Through simulating the dynamic process of memory loss of the human brain after receiving external excitation, a new dynamic time-varying memory intelligent algorithm with external excitation is proposed in this paper. Furthermore, a novel random dynamic time-varying memory intelligent algorithm with external excitation is proposed considering the randomness of factors in this paper. Considering the randomness of factors affecting thermal error, the proposed models are used for the prediction of reliability of thermally-deduced positioning accuracy for machine tool ball screw system under external excitation caused by manufacturing process alternations of machine tools. Finally, the effectiveness of the proposed models is verified by the experiment. Because the presented model can consider the randomness of factors affecting thermal error and the impact and hysteresis phenomenon caused by the alternations of multiple processes, it is suitable for the accurate prediction of the dynamic characteristics under the alternation of arbitrary excitation considering the reliability. [Display omitted] • A new time-varying dynamic memory intelligent model with external excitation was proposed. • A novel random time-varying dynamic memory intelligent model with external excitation was proposed. • This model is suitable for the accurate prediction of dynamic characteristics considering reliability. • The experiments verify the effectiveness and feasibility of the proposed models. [ABSTRACT FROM AUTHOR]

Thermally induced elongation of a screw is the primary cause of deterioration in the positioning accuracy of half-closed-loop screw systems in machine tools. The failure mode of ball-screw feed-drive systems in relation to the positioning accuracy is defined as the response error of the carriage exceeding the axial tolerance. It is still challenging to accurately predict the thermal errors using conventional methods because the randomness of the influencing factors is not considered. This paper presents an improved random thermal network model subjected to dynamic thermal excitation for calculating real-time transient temperatures of ball-screw systems. Furthermore, a time-varying reliability model is proposed for estimating the gradual reliability of the thermally induced positioning accuracy of ball-screw systems with random thermal boundary parameters using the random thermal network. A ball-screw system of a computerized numerical controlled lathe is used as an example to show the practical application of the proposed model. Repeated experiments demonstrate the accuracy retaining ability and robustness of this method. This work provides the theoretical and practical method for real-time monitoring of the positioning accuracy reliability index for ball-screw systems and it has high accuracy considering the random parameters. • An improved random thermal network model subjected to dynamic thermal excitation was presented. • This method can obtain key point transient temperatures of ball-screw systems. • A time-varying positioning accuracy model for screw systems was proposed using random thermal network. • This model can quickly and accurately predict positioning accuracy. [ABSTRACT FROM AUTHOR]