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Machine Learning Classification for Tool Life Modeling in Production Environment Using Shop Floor Data

  • today
  • access_time 2:15 - 2:45 PM ET
  • location_onRoom 302/303
  • blur_circularConference Session
  • monetization_onPaid Upgrade

This presentation describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment. Laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment under limited datasets.

Learning Objectives:

  • Learn basics of machine learning classification methods
  • Identify potential applications in manufacturing for machine learning
  • Recognize tools and challenges in implementing machine learning