Optimization of machining datum selection and machining tolerance allocation with genetic algorithms

In machining process planning, selection of machining datum and allocation of machining tolerances are crucial as they directly affect the part quality and machining efficiency. This study explores the feasibility to build a mathematical model for computer aided process planning to find the optimal machining datum set and machining tolerances simultaneously for rotational parts. Tolerance chart and an efficient dimension chain tracing method are utilized to establish the relationship between machining datums and tolerances. A mixed-discrete nonlinear optimization model is formulated with the manufacturing cost as the objective function and blueprint tolerances and machine tool capabilities as constraints. A directed random search method, genetic algorithm, is used to find optimum solutions. The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances. The proposed model and solution procedure can be used as a building block for computer automated process planning.