Tool planning for a lights-out machining system

Abstract

The goal of most advanced manufacturing systems is to continuously improve uptime while reducing the amount of direct supervision required for operations. However, when there is randomness in system components, this improvement can be difficult to attain. For instance, when automating metal cutting operations, the randomness of tool life requires ensuring that there are sufficient cutting tools available on the machines to meet unsupervised production requirements, and variations in tool life can make planning challenging. This paper focuses on the problem of selecting the cutting speeds for processing a set of part types by an unsupervised metal cutting flexible machine in such a situation. The machine is set up to operate unsupervised for a specific known duration. The tool magazine of this machine is preloaded with tools commensurate with the processing requirements. The lifetime of each tool is random, with the coefficient of variation assumed to be constant, and a system for online monitoring of the tool condition is available. The objective in this situation is to ensure that disruption is minimized—in other words, that the machine operates while ensuring some minimum probability of completion. This is referred to as the required service level. This paper presents models for determining the optimal magazine loading and cutting speeds that will meet a required service level. Solutions obtained using commonly available nonlinear programming solvers are included for illustration, and differences when the tool life distributions are either normal or Erlang are contrasted.