The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally in...The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.展开更多
To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the character...To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.展开更多
文摘The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.
基金supported by the Scientific Development Pro-gram of Shandong Province(Grant No.2012GGB01071)the Doctoral Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XNBS1225)the School Scientific Research Fund Program of Shandong Jianzhu University (Grant No. XN110108)
文摘To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.
