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

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...

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Bibliographic Details
Published inIEEE transactions on systems, man, and cybernetics Vol. 19; no. 2; pp. 299 - 314
Main Authors Rangwala, S.S., Dornfeld, D.A.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.03.1989
Institute of Electrical and Electronics Engineers
Subjects
990200 - Mathematics & Computers
ANIMAL CELLS
Applied sciences
ARRAY PROCESSORS
AUTOMATION
COMPUTER CALCULATIONS
COMPUTER NETWORKS
COMPUTER-AIDED INSTRUCTION
COMPUTERIZED SIMULATION
Concurrent computing
Exact sciences and technology
Feedforward neural networks
Feeds
GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Input variables
LEARNING
Machining
Mechanical engineering. Machine design
NERVE CELLS
Network synthesis
Neural networks
OPTIMIZATION
PARALLEL PROCESSING
PROGRAMMING
REAL TIME SYSTEMS
SIMULATION
SOMATIC CELLS
TECHNOLOGY ASSESSMENT
Temperature
TRAINING
Learning
Automation
Simulation
Machining
Performance analysis
Neural network
Optimization
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ISSN0018-9472
2168-2909
DOI10.1109/21.31035

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Summary: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 tuning 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.< >
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ISSN:0018-9472
2168-2909
DOI:10.1109/21.31035