A multi-granularity NC program optimization approach for energy efficient machining. (January 2018) - Li, X.X.; Li, W.D.; He, F.Z.
Highlights: A multi-granularity NC program optimization approach for energy efficient machining which includes the granularity of a group of NC programs for a setup and the granularity of a NC program. On the first level of granularity, the execution sequence of the NC programs for the setup of a part is optimized to reduce the energy consumed by the cutting tool change among the NC programs. On the second level of granularity, the execution sequence of the features in the same NC program is optimized to reduce the energy consumed by the cutting tool's traveling among the machining features. Experiments on the practical cases show that the optimization results are promising and the approach has significant potential of applicability in practice. Abstract: NC programs are widely developed and applied to various machining processes. However, the lack of effective NC program optimization strategy for the machining energy efficiency has been crippling the implementation of sustainability in companies. To address this issue, a multi-granularity NC program optimization approach for energy efficient machining has been developed and presented in this paper. This approach consists of two levels of granularities: the granularity of a group of NC programs for a setup where the features are machined on a single CNC machine with the same fixture and the granularity of a NC program. On the former level of granularity, the execution sequence of the NC programs for the setup of a part is optimized to reduce the energy consumed by the cutting tool change among the NC programs. On the latter level of granularity, the execution sequence of the features in the same NC program is optimized to reduce the energy consumed by the cutting tool's traveling among the machining features. Experiments on the practical cases show that the optimization results from this approach are promising and the approach has significant potential of applicability in practice.