Taking the evolution process of TFT-LCD industry as an example,this paper applied history-friendly model to analyze the effect of technology innovation and learning,and market demand growth and fluctuation on the evol...Taking the evolution process of TFT-LCD industry as an example,this paper applied history-friendly model to analyze the effect of technology innovation and learning,and market demand growth and fluctuation on the evolution of production organization pattern in strategic emerging industries.Our research indicates that:(1) when market demand maintains linear growth,continuous technology innovation capabilities of vertically integrated enterprises in leading position of an industry are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(2) when market demand is in cyclical fluctuation,the technology learning capabilities of specialized enterprises in catch-up position are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(3) when market demand growth is under cyclical fluctuation,if the relative gap between technology innovation capabilities of vertically integrated enterprises and technology learning capabilities of specialized enterprises remains constant,the phase when industry cycle moves from trough to plateau is the best time window for specialized enterprises to catch up with and overtake vertically integrated enterprises.Hence,policy design supporting the development of strategic emerging industries should give full consideration to factors like market demand environment and technology innovation and learning capabilities of domestic enterprises.展开更多
Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on f...Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.展开更多
基金Financial support from Key Program of National Social Sciences Foundation of China(Grant No.10AJL008)is gratefully acknowledged
文摘Taking the evolution process of TFT-LCD industry as an example,this paper applied history-friendly model to analyze the effect of technology innovation and learning,and market demand growth and fluctuation on the evolution of production organization pattern in strategic emerging industries.Our research indicates that:(1) when market demand maintains linear growth,continuous technology innovation capabilities of vertically integrated enterprises in leading position of an industry are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(2) when market demand is in cyclical fluctuation,the technology learning capabilities of specialized enterprises in catch-up position are the key factor in deciding whether dominant production organization pattern will shift from vertical integration to specialization;(3) when market demand growth is under cyclical fluctuation,if the relative gap between technology innovation capabilities of vertically integrated enterprises and technology learning capabilities of specialized enterprises remains constant,the phase when industry cycle moves from trough to plateau is the best time window for specialized enterprises to catch up with and overtake vertically integrated enterprises.Hence,policy design supporting the development of strategic emerging industries should give full consideration to factors like market demand environment and technology innovation and learning capabilities of domestic enterprises.
基金supported by the Major State Basic Research Development of China (Grant No. 2011CB706803)National Natural Science Foundation of China (Grant No. 50875098)Important National Science & Technology Specific Projects of China (Grant No. 2009ZX04014-024)
文摘Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
