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Machining dynamics research lays a solid foundation for machining operationsby providing stable combinations of spindle speed and depth of cut.Furthermore, machine learning has been applied to predict tool life as afunction of cutting speed. However, the existing research does not consider thediscrete-event dynamics in machine shop, i.e., the machine tool needs toprocess a series of parts in queue under various practical productionrequirements. This paper addresses the integration of discrete-event dynamicsand machining dynamics to achieve cost savings in machining. We first propose alearning-based cost function for the studied integrated optimization problem ofmachine tool. The proposed cost function utilizes the predicted tool life underdifferent stable cutting speeds for further optimizing speed selection ofmachine tool to deal with the discrete-event dynamics in machine shop. Then,according to the practical production requirements, we develop severalmathematical optimization models for the related integrated optimizationproblems with the consideration of cost, makespan and due date. Numericalresults show the effectiveness of our proposed methods and also the potentialto be used in practice.

Integration of discrete-event dynamics and machining dynamics for machine tool: modeling, analysis and algorithms