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

Mason Ma, Alisa Ren, Christopher Tyler, Jaydeep Karandikar, Michael Gomez, Tony Shi, Tony Schmitz

Research output: Contribution to journalArticlepeer-review

Abstract

Machining dynamics research lays a solid foundation for machining operations by providing stable combinations of spindle speed and depth of cut. Furthermore, machine learning has been applied to predict tool life as a function of cutting speed. However, the existing research does not consider the discrete-event dynamics in machine shop, i.e., the machine tool needs to process a series of parts in queue under various practical production requirements. This paper addresses the integration of discrete-event dynamics and machining dynamics to achieve cost savings in machining. A learning-based cost function is first proposed for the studied integrated optimization problem of machine tool. The proposed cost function utilizes the predicted tool life under different stable cutting speeds for further optimizing speed selection of machine tool to deal with the discrete-event dynamics in machine shop. Then, according to the practical production requirements, effective mathematical optimization models are developed for the related integrated optimization problems with the consideration of cost, makespan and due date, respectively. Numerical results show the effectiveness of our proposed methods and also the potential to be used in practice.

Original languageEnglish
Pages (from-to)321-332
Number of pages12
JournalManufacturing Letters
Volume35
DOIs
StatePublished - Aug 2023

Funding

This material is based upon work supported in part by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (AMO), which is operated by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. This work is supported in part by the University of Tennessee Knoxville under the Graduate Advancement Training and Education (GATE) program of Science Alliance, and the Southeastern Advanced Machine Tools Networks (SEAMTN). This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Keywords

  • Discrete-event dynamics
  • Machine tool
  • Machining dynamics
  • Mathematical optimization
  • Operational excellence

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