汽车发动机前端附件驱动(Front end accessory drive,简称FEAD)系统的动力学特性直接影响汽车的噪声(Noise)、振动(Vibration)和声振粗糙度(Harshness),进而影响汽车的安全性和舒适性。建立了考虑发电机超越皮带轮(Overrunning alternat...汽车发动机前端附件驱动(Front end accessory drive,简称FEAD)系统的动力学特性直接影响汽车的噪声(Noise)、振动(Vibration)和声振粗糙度(Harshness),进而影响汽车的安全性和舒适性。建立了考虑发电机超越皮带轮(Overrunning alternator pulley,OAP)影响的FEAD系统的非线性旋转振动模型。分析了OAP对FEAD系统动态特性的影响,得到了OAP系统刚度对张紧轮角度波动、带段B1和B3动态张力的影响。研究为FEAD系统和OAP的优化设计提供了必要的理论依据和实践指导。展开更多
Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the ...Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.展开更多
The tool point frequency response function(FRF) is commonly obtained by impacting test or semi-analytical techniques.Regardless of the approach,it is assumed that the workpiece system is rigid.The assumption is valid ...The tool point frequency response function(FRF) is commonly obtained by impacting test or semi-analytical techniques.Regardless of the approach,it is assumed that the workpiece system is rigid.The assumption is valid in common machining,but it doesn’t work well in the cutting processes of thin-wall products.In order to solve the problem,a multi-degree-of-freedom dynamic model is employed to obtain the relative dynamic stiffness between the cutting tool and the workpiece system.The relative direct and cross FRFs between the cutting tool and workpiece system are achieved by relative excitation experiment,and compared with the tool point FRFs at x and y axial direction.The comparison results indicate that the relative excitation method could be used to obtain the relative dynamic compliance of machine-tool-workpiece system more actually and precisely.Based on the more precise relative FRFs,four evaluation criterions of dynamic stiffness are proposed,and the variation trend curves of these criterions during the last six months are achieved and analyzed.The analysis results show that the lowest natural frequency,the maximum and the average dynamic compliances at x axial direction deteriorate more quickly than that at y axial direction.Therefore,the main cutting direction and the large-size direction of workpieces should be arranged at y axial direction to slow down the deterioration of the dynamic stiffness of machining centers.The compliance of workpiece system is considered,which can help master the deterioration rules of the dynamic stiffness of machining centers,and enhance the reliability of machine centers and the consistency of machining processes.展开更多
In the traditional Markov chain model (MCM), aleatory uncertainty because of inherent randomness and epistemic uncertainty due to the lack of knowledge are not differentiated. Generalized interval probability provides...In the traditional Markov chain model (MCM), aleatory uncertainty because of inherent randomness and epistemic uncertainty due to the lack of knowledge are not differentiated. Generalized interval probability provides a concise representation for the two kinds of uncertainties simultaneously. In this paper, a generalized Markov chain model (GMCM), based on the generalized interval probability theory, is proposed to improve the reliability of prediction. In the GMCM, aleatory uncertainty is represented as probability; interval is used to capture epistemic uncertainty. A case study for predicting the average dynamic compliance in machining processes is provided to demonstrate the effectiveness of proposed GMCM. The results show that the proposed GMCM has a better prediction performance than that of MCM.展开更多
Cutting chatter is a violent self-excited vibration between a tool and a workpiece.Its negative effects mainly include poor surface quality,inferior dimensional accuracy,disproportionate tool wear or tool breakage,and...Cutting chatter is a violent self-excited vibration between a tool and a workpiece.Its negative effects mainly include poor surface quality,inferior dimensional accuracy,disproportionate tool wear or tool breakage,and excessive noise.Therefore,early recognition and online suppression of chatter vibration are necessary.This paper proposes a novel synthetic criterion(SC)for early chatter recognition.The proposed SC integrates standard deviation(STD)and one-step autocorrelation function(OSAF).Moreover,this paper revised the fast algorithm of OSAF.We can quantitatively divide a chatter vibration signal into three stages,which are stable stage,transition stage and chatter stage according to the SC.Compared with STD,the SC can improve the reliability of chatter recognition and the threshold of SC is not sensitive to variable cutting conditions.This paper presents an original algorithm of SC and its fast algorithm in detail.The fast algorithm of SC in this paper improves the computation efficiency compared with the original algorithm of SC.To validate the effectiveness of the proposed SC,a series of milling experiments were conducted under different cutting conditions.In these experiments,the vibration signals were acquired by two accelerometers mounted on the spindle house.The experimental results showed that the proposed SC could effectively recognize chatter vibration at an early stage of chatter vibration,which saved valuable time for online chatter suppression.展开更多
基金supported by National Key Basic Research Program of China(973Program,Grant No.2005CB724100,Grant No.2011CB706803)National Natural Science Foundation of China(Grant No.50675076,Grant No.50575087,Grant No.51075161)National Hi-tech Research and Development Program of China(863Program,Grant No.2008AA042802)
文摘Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.
基金supported by National Natural Science Foundation of China(Grant No.51175208)National Key Basic Research Program of China(973 ProgramGrant No.2011CB706803)
文摘The tool point frequency response function(FRF) is commonly obtained by impacting test or semi-analytical techniques.Regardless of the approach,it is assumed that the workpiece system is rigid.The assumption is valid in common machining,but it doesn’t work well in the cutting processes of thin-wall products.In order to solve the problem,a multi-degree-of-freedom dynamic model is employed to obtain the relative dynamic stiffness between the cutting tool and the workpiece system.The relative direct and cross FRFs between the cutting tool and workpiece system are achieved by relative excitation experiment,and compared with the tool point FRFs at x and y axial direction.The comparison results indicate that the relative excitation method could be used to obtain the relative dynamic compliance of machine-tool-workpiece system more actually and precisely.Based on the more precise relative FRFs,four evaluation criterions of dynamic stiffness are proposed,and the variation trend curves of these criterions during the last six months are achieved and analyzed.The analysis results show that the lowest natural frequency,the maximum and the average dynamic compliances at x axial direction deteriorate more quickly than that at y axial direction.Therefore,the main cutting direction and the large-size direction of workpieces should be arranged at y axial direction to slow down the deterioration of the dynamic stiffness of machining centers.The compliance of workpiece system is considered,which can help master the deterioration rules of the dynamic stiffness of machining centers,and enhance the reliability of machine centers and the consistency of machining processes.
基金supported by the National Key Basic Research Program of China (973 Program) (Grant No. 2011CB706803)the National Natural Science Foundation of China (Grant Nos. 51175208, 51075161)
文摘In the traditional Markov chain model (MCM), aleatory uncertainty because of inherent randomness and epistemic uncertainty due to the lack of knowledge are not differentiated. Generalized interval probability provides a concise representation for the two kinds of uncertainties simultaneously. In this paper, a generalized Markov chain model (GMCM), based on the generalized interval probability theory, is proposed to improve the reliability of prediction. In the GMCM, aleatory uncertainty is represented as probability; interval is used to capture epistemic uncertainty. A case study for predicting the average dynamic compliance in machining processes is provided to demonstrate the effectiveness of proposed GMCM. The results show that the proposed GMCM has a better prediction performance than that of MCM.
基金supported by the National Basic Research Program of China (Grant No. 2011CB706803)the National Natural Science Foundation of China (Grant Nos. 51175208, 51075161)Science Fund of Hebei University of Science and Technology (Grant No. XL201121)
文摘Cutting chatter is a violent self-excited vibration between a tool and a workpiece.Its negative effects mainly include poor surface quality,inferior dimensional accuracy,disproportionate tool wear or tool breakage,and excessive noise.Therefore,early recognition and online suppression of chatter vibration are necessary.This paper proposes a novel synthetic criterion(SC)for early chatter recognition.The proposed SC integrates standard deviation(STD)and one-step autocorrelation function(OSAF).Moreover,this paper revised the fast algorithm of OSAF.We can quantitatively divide a chatter vibration signal into three stages,which are stable stage,transition stage and chatter stage according to the SC.Compared with STD,the SC can improve the reliability of chatter recognition and the threshold of SC is not sensitive to variable cutting conditions.This paper presents an original algorithm of SC and its fast algorithm in detail.The fast algorithm of SC in this paper improves the computation efficiency compared with the original algorithm of SC.To validate the effectiveness of the proposed SC,a series of milling experiments were conducted under different cutting conditions.In these experiments,the vibration signals were acquired by two accelerometers mounted on the spindle house.The experimental results showed that the proposed SC could effectively recognize chatter vibration at an early stage of chatter vibration,which saved valuable time for online chatter suppression.
