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.