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Application of genetic algorithm to computer-aided process

Author(s): Amrinder Chahal

Journal: Journal of Current Engineering Research
ISSN 2250-2637

Volume: 2;
Issue: 2;
Date: 2012;
Original page

Keywords: Genetic algorithm | Computer-aided process planning | Operation sequencing | Preliminary planning | Detailed planning

Process planning is a task of transforming design specifications into manufacturing instructions. It is an engineering task that determinesthe detailed manufacturing requirements for transforminga raw material into a completed part, within the available machining resources. The output of process planning generally includes operations, machine tools, cutting tools, fixtures, machining parameters, etc. Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machineand cutting tool for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine and cutting tool for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine and cutting tool for each operation using the proposed GA, simultaneously.
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