Multi-objective titanium alloy belt grinding parameters optimization oriented to resources allocation and environment

Belt grinding is a widely adopted method in processing titanium alloy materials. However, its unique grinding mechanism will greatly affect grinding efficiency. Meanwhile, in order to finish processing task, adopting the method of replacing belts frequently will further make processing time, cost, and energy consumption rise, as well as the pollution during the whole processing. Considering the problems above, first, establish a multi-objective optimization model, which takes spindle rotary speed, feed speed of workpiece, grinding depth, and axial feed as the variables and takes processing time, SEC (specific energy consumption), cost, and environmental impact as objective optimization functions. Secondly, multi-objective particle swarm optimization algorithm is applied to solve resources allocation objectives for Pareto optimal solutions. Then, analytic hierarchy process is applied to analyze the degree of environmental impact of each solution among Pareto optimal solutions and finalize the best optimization solution of belt grinding. Finally, an experiment case is performed to verify the effectiveness of the optimization model. The result shows that, compared with experience-guided parameters, the optimized grinding parameters make processing efficiency improve by 17.3% and SEC declines by 13.2% cost decline by 17.0%. The proposed optimization method of grinding parameters is effective and can be applied to engineering practice. Meanwhile, it can provide a reference to the selection of grinding processing parameters.