Machining process language understanding in 3D process modeling

Author(s):  
Haitao Fan ◽  
Shusheng Zhang ◽  
Yunfei Shi ◽  
Julu Cao
2012 ◽  
Vol 468-471 ◽  
pp. 607-612
Author(s):  
Shi Ping Zhang ◽  
Yi Chao Ding ◽  
Jing Wang ◽  
Yuan Hui Li

It is difficult to build a strict mathematical model for WEDM due to the complication of the machining process and the nonlinear relation between process parameters and process targets. The neural network is suited to the modeling of complex system, because it has the functions of self-organized, self-learning and associative memory, and properties of distributed parallel type and high robustness. Therefore, this paper attempts to use the RBF neural network for the process modeling of WEDM.


1989 ◽  
Vol 111 (2) ◽  
pp. 133-139 ◽  
Author(s):  
S. D. Fassois ◽  
K. F. Eman ◽  
S. M. Wu

A fast, on-line algorithm for machining process modeling and control is proposed. The modeling is accomplished via a new recursive estimator that offers good accuracy at a minimal computational load. Its Fast Kalman-type version, that further reduces its computational complexity, is also presented. The adaptive controller, which is based on on-line identification and closed-loop pole assignment, is characterized by a low computational load and no need for a priori process information. The analytical results are supplemented by numerical simulations, where the proposed scheme is used for the control of a turning operation and shown to offer very good performance under noisy conditions and suddenly changing machining dynamics.


2010 ◽  
Vol 4 (3) ◽  
pp. 213-213
Author(s):  
Keiichi Shirase

In the 5 decades-plus since the first numerical control (NC) machine tool was demonstrated at the Massachusetts Institute of Technology in Boston, MA, USA, advances such as high-speed, multi-axis and multi-tasking machine tools have been introduced widely to achieve high quality and productivity in machining operations. In order to handle these sophisticated machine tools freely and effectively, sophisticated NC programs are conventionally required in advance for problem-free machining. Computer simulation and optimization of cutting processes by considering process physics, machine tool dynamics and kinematics and process constraints are helpful in the strategic process planning operation and useful in preparing sophisticated NC programs. However, challenges and models quantitatively predicting cutting process performance remain to be developed. Topics of interests in this special issue include but are not limited to - machining process modeling - machine tool dynamics modeling - cutting force, cutting temperature, surface roughness, etc., prediction - machining stability prediction - simulation-based machining-process diagnostics - optimization using machining simulation The review paper and ten research works accepted are related to state-of-the-art modeling and simulation applicable to the machining and manufacturing domains. Besides traditional machining, nontraditional machining such as laser machining for micromachining have been explored. Also the machining of calcium polyphosphate (CPP) for tissue engineering applications has been investigated. The articles in this special issue are sure to prove interesting, informative, and inspiring to our readers on advances in cutting process modeling and simulation. Finally, we thank the authors, reviewers, and editors for their invaluable contributions and generous efforts in enabling this issue to be published.


2017 ◽  
Vol 14 ◽  
pp. 1-5 ◽  
Author(s):  
Zhipeng Pan ◽  
Steven Y. Liang

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