Learning and optimization of machining operations using computing abilities of neural networks

Journal Article · Wed Mar 01 04:00:00 EST 1989 · IEEE (Institute of Electrical and Electronics Engineers) Transactions on Systems, Man, and Cybernetics; (USA)

DOI:https://doi.org/10.1109/21.31035· OSTI ID:5242983

Rangwala, S S; Dornfeld, D A ^[1]

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1. California Univ., Berkeley, CA (USA). Dept. of Mechanical Engineering

The success of automated manufacturing relies to a large extent on the development of computer-based learning schemes that are able to code operational knowledge and to use this knowledge for synthesizing optimal strategies for machining operations. The authors present a scheme that uses a feedforward neural network for the learning and synthesis task. Neural networks consist of a collection of interconnected processors that compute in parallel. The parallelism allows the network to examine various constraints simultaneously during the learning phase and enables reductions in computing time that are attractive in real-time applications. The learning abilities of these networks in a turning operation are discussed. The network learns by observing the effect of the input variables of the operation (such as feed rate, depth of cut, and cutting speed) on the output variables (such as cutting force, power, temperature, and surface finish of the workpiece). The learning phase is followed by a synthesis phase during which the network predicts the input conditions to be used by the machine tool to maximize the metal removal rate subject to appropriate operating constraints. Simulation results presented demonstrate the neural networks can learn and synthesize knowledge effectively, thereby offering a new framework for implementing optimal machining schemes in automated manufacturing.

• OSTI ID: 5242983
• Journal Information: IEEE (Institute of Electrical and Electronics Engineers) Transactions on Systems, Man, and Cybernetics; (USA), Journal Name: IEEE (Institute of Electrical and Electronics Engineers) Transactions on Systems, Man, and Cybernetics; (USA) Vol. 19:2; ISSN 0018-9472; ISSN ISYMA
• Country of Publication: United States
• Language: English

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