Manufacturing Knowledge Modeling Based on Artificial Neural Network for Intelligent CAPP

CAPP(computer aided process planning) is a key technology for integration of CAD and CAM. Process planning relies on manufacturing knowledge and CAPP is characterized with multi-knowledge resources, multi-task, multi-level and multi-constrain so process planning is hard to automate. This paper introduces the artificial neural network for unstructured manufacturing knowledge modeling, knowledge is represented as neural network weight value matrix, and then form ANN database to support intelligent CAPP. Example about cutting force modification is presented to test the feasibility of this approach. As Intelligent CAPP is knowledge based and structure of CAPP varies with types of knowledge representation, this paper presents the system structure of intelligent CAPP system. This system employs the black-board inference, unified manufacturing resource and part model, multi-knowledge database to realize the process planning automatically.

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Application of Artificial Neural Network Techniques in Computer Aided Process planning- A review

International Journal of Process Management and Benchmarking ◽

10.1504/ijpmb.2020.10021148 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Natarajan Krishnan ◽

Gokulachandran J

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Planning ◽

Computer Aided Process Planning ◽

Computer Aided ◽

Artificial Neural

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Application of artificial neural network techniques in computer aided process planning - a review

International Journal of Process Management and Benchmarking ◽

10.1504/ijpmb.2021.112257 ◽

2021 ◽

Vol 11 (1) ◽

pp. 80

Author(s):

K.K. Natarajan ◽

J. Gokulachandran

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Planning ◽

Computer Aided Process Planning ◽

Computer Aided ◽

Artificial Neural

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A parameter estimation approach to artificial neural network weight selection for nonlinear system identification

[Proceedings 1992] The First IEEE Conference on Control Applications ◽

10.1109/cca.1992.269812 ◽

2003 ◽

Cited By ~ 3

Author(s):

T.L. Ruchti ◽

R.H. Brown ◽

J.J. Garside

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Parameter Estimation ◽

System Identification ◽

Nonlinear System ◽

Nonlinear System Identification ◽

Selection For ◽

Artificial Neural ◽

Network Weight

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The Artificial Neural Network Modeling of Dynamic Hysteresis Phase Diagram: Application on Mean-Field Ising Hysteresis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.813.16 ◽

2013 ◽

Vol 813 ◽

pp. 16-19

Author(s):

Wimalin Laosiritaworn ◽

Kanokwan Kanchiang ◽

Yongyut Laosiritaworn

Keyword(s):

Neural Network ◽

Phase Diagram ◽

Artificial Neural Network ◽

Mean Field ◽

Three Dimensions ◽

System Structure ◽

Neural Network Modeling ◽

Hysteresis Behavior ◽

Artificial Neural ◽

Target Data

This work used Artificial Neural Network (ANN) to investigate the hysteresis behavior of the Ising spins in structures ranging from one-to two-and three-dimensions. The equation of magnetization motion under the mean-field picture was solved using the Runge-Kutta method to extract the Ising hysteresis loops with varying the temperature, the external magnetic field parameters and the system structure (via the variation of number of nearest neighboring spins). The ANN was then used to establish relationship among parameters via Back Propagation technique in ANN training. With the trained networks, the ANN was used to predict hysteresis data, with an emphasis on the dynamic critical point, and compared with the actual target data. The predicted and the target data were found to agree well which indicates that the ANN functions well in modeling hysteresis behavior and its critical phase diagram across systems with different structures.

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