Planning optimised multi-tasking operations under the capability for parallel machining

Abstract The advent of advanced multi-tasking machines (MTMs) in the metalworking industry has provided the opportunity for more efficient parallel machining as compared to traditional sequential processing. It entailed the need for developing appropriate reasoning schemes for efficient process planning to take advantage of machining capabilities inherent in these machines. This paper addresses an adequate methodical approach for a non-linear process planning with a variety of alternatives, enabled through the STEP_NC standard. A relevant algorithmic approach of high efficacy is developed for feature clustering and operation sequencing based on AND-OR graph modelling. It involves a discrete modelling scheme for setup formulation so that the workload of machine spindles is levelled and its total cycle time is minimized. The so-formulated optimization problem, and related in particular to feature distribution among setups, can be successfully solved by a non-linear generalized reduced gradient (GRC) algorithm. The solution algorithms outlined can be relatively readily implemented in industrial informatics systems of small and medium-sized manufacturing enterprises. The entire methodical approach is validated through illustrative case studies based on exemplary mill-turn parts.