Integrated of Machining Operation Sequences and Cutting Tools Selection Based Artificial Neural Network for Rotational Parts

Abstract

Process planning is a central, knowledge extensive and important activity in a manufacturing company. The selection of machining operations sequence and cutting tools are important tasks in process planning. In this paper, the main aim is to develop an integrated process planning and artificial neural networks that can automatically perform the task of process planning for rotational parts". The proposed method consists of two modules; the first module NN1 for selection of machining operations sequence based on feature type and their attribute include dimensional, "tolerance, and surface finish by pre-structure of thumb rule and neural network were employed for automated CAPP. The second module NN2 for cutting tool selection based on the type, condition and dimensional ratio of feature, so the NN1outputs as parameter of input layer for NN2. The methods of training, testing and validation of the network have been used back propagation". Case study has found that the developed system is able to give best prediction solutions for process planning problems so, the proposed approach to explain its selection of machining operation sequences  and cutting tools for using in real manufacturing environment