On-line predictive model for disassembly process planning adaptation

1999 ◽  
Vol 15 (3) ◽  
pp. 211-220 ◽  
Author(s):  
Nizan Salomonski ◽  
Eyal Zussman
Author(s):  
Yaoyao F. Zhao ◽  
Frederick M. Proctor ◽  
John A. Horst ◽  
Xun Xu

Machining process planning and measurement process planning have long received research attention from industry and academia. Machining and measurement process automation is well established for mass production in today’s manufacturing systems. However, over the years manufacturing systems have evolved in response to many external drivers including the introduction of new manufacturing technologies and materials, the constant evolution of new products and the increased emphasis on quality as well as the escalating global competition and pressing need for responsiveness, agility and adaptability. These external drivers compel the realization of cognitive manufacturing, in which machining and measurement are merged together to form a more informed, more flexible, and more controlled manufacturing environment. In this way, when unforeseen changes or significant alternations happen, machining process planning systems can receive on-line measurement results, make decisions, and adjust machining operations accordingly in real-time. This paper presents a new paradigm of process planning research and outlines the way to reach cognitive manufacturing. An integrated machining and measurement process planning prototype system has been developed and tested with case studies.


2013 ◽  
Vol 313-314 ◽  
pp. 355-358
Author(s):  
Chang Yuan Huang ◽  
Hai Peng Pan

Against the characteristics of the temperature in reactor such as time-delay, time-varying and difficulty to build a precise mathematical model in the chemical industry. Through the analysis of dynamic characteristics of the controlled object, the method of fuzzy-PID control was designed based on a predictive model. According to the detected temperature signal, the output deviation of the controller and the on-line identification of prediction model, this algorithm gains the predictive value which uses a generalized predictive model and the fuzzy-PID control. Then compare the predictive value with the reference trajectory to get the deviation. Finally use this deviation and the change of the deviation to optimize the PID control parameters and attain the appropriate amount of system control. The simulation results show that the fuzzy-PID control based on prediction model has strong adaptability, good robustness, control accuracy and higher practical value.


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