Machiming operations selection is a key issue in today's research of computer aided process planning (CAPP). Traditionally, this task is carried out by process planners and knowledge base systems. Recently, process planners have started using newer artificial intelligent techniques, such as neural networks, fuzzy logic, intelligent agents, etc. to model machining operations. In this study, the problem of machining operations selection for hole making operations is investigated. A neural network model is proposed to generate the needed machining operations and their sequence based on hole attributes and accuracy required. Hole diameter, length to diameter (LID) ratio, surface finish and tolerances are presented to the network for each feature as input parameters. The network classifies the required machining operations into three steps; hole starting, core making and hole finishing operations. The advantage and effectiveness of the proposed model are verified through a several types of hole features.
M. EL-mabrouk, O., & M. amaitik, S. (2009). NEURAL NETWORK APPROACH TO FEATURE-BASED PROCESS PLANNING. ERJ. Engineering Research Journal, 32(3), 353-358. doi: 10.21608/erjm.2009.69355
MLA
Omar M. EL-mabrouk; Saleh M. amaitik. "NEURAL NETWORK APPROACH TO FEATURE-BASED PROCESS PLANNING". ERJ. Engineering Research Journal, 32, 3, 2009, 353-358. doi: 10.21608/erjm.2009.69355
HARVARD
M. EL-mabrouk, O., M. amaitik, S. (2009). 'NEURAL NETWORK APPROACH TO FEATURE-BASED PROCESS PLANNING', ERJ. Engineering Research Journal, 32(3), pp. 353-358. doi: 10.21608/erjm.2009.69355
VANCOUVER
M. EL-mabrouk, O., M. amaitik, S. NEURAL NETWORK APPROACH TO FEATURE-BASED PROCESS PLANNING. ERJ. Engineering Research Journal, 2009; 32(3): 353-358. doi: 10.21608/erjm.2009.69355
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