On Development of an Optimal Control System for Real-time Process Optimization on Milling Machine Tools

Developing an intelligent machine tool means to augment its level of automation. This augmentation, in turn, requires a machine controller able to perform actions and to implement attributes that are currently demanded to, and hold by, human operators. The present paper describes how this issue is being faced by a large Italian national research project, funded under the Industria 2015 initiative. Considering the case of milling machines, human operators are currently in charge of supervising the cutting process by acting on spindle speed and feed override controls in order to compensate for undesired process conditions (e.g. excessive vibration or power absorption) caused by a wrong choice of process parameters during the design of the part-program, by tool wear, by unexpected work material properties, or by machine tool dynamics. The first part of the paper proposes the architecture for an augmented-automation machine tool. Rather than revolutionizing the well-established architecture of a conventional machine tool, the concept is based on an additional controller that implements a supervision and optimization loop. This additional controller gets process state information from the CNC and from dedicated measurement systems, and closes a feedback action on the CNC as a human operator would do: by acting on feed and spindle speed overrides. The second part of the paper illustrates how the additional controller works: following the optimal control theory, it is based on a dynamic process model, a set of state variables (i.e. measurements), and a set of controls. Exploiting a simplified process model and efficient optimization algorithms, it performs a real-time optimization of the controls (i.e. the overrides named above) on the basis of a weighted multi-objective target function and a set of measurements taken from the cutting process (power, forces, accelerations). In particular, the target function takes into account the following objectives: cutting time, work-piece surface finish, tool wear rate and vibration mitigation in general. The third part of the paper details the strategies concerned with tool vibrations prediction, monitoring and mitigation, which are integrated into the optimization loop. A vibrations prediction module based on a simplified cutting process model allows the estimation of the vibration level and/or chatter occurrence during a pre-processing phase: thus, through the computation of the Stability Lobes Diagram along the tool path, the more stable spindle speeds can be identified. The pre-processing phase is complemented with an in-process chatter monitoring algorithm based on a recursive dynamic model identification: detecting real- time self-excited vibrations onset, and distinguishing them from forced vibrations, this module allows the controller to properly update the vibration estimation.