A Bayesian Model of Machining Economics for Optimization by Adaptive Control

[+] Author and Article Information
Donald S. Ermer

Department of Industrial Engineering, The Pennsylvania State University, University Park, Pa.

J. Eng. Ind 92(3), 628-632 (Aug 01, 1970) (5 pages) doi:10.1115/1.3427825 History: Received December 18, 1969; Online July 15, 2010


A learning model of tool wear based on Bayesian statistical methods provides a means for regulating the optimum cutting conditions as periodic sampling data on flank wear become available during production under adaptive control. The sampling process is used to estimate the current parameters of the wear process, and by incorporating this updated information into the machining economics model, an optimal a posteriori program of cutting conditions can be determined to best match the current conditions of the tool, workpiece, and machine. The application of the Bayesian learning model is illustrated for a basic turning operation with minimum cost as the optimizing criterion.

Copyright © 1970 by ASME
Your Session has timed out. Please sign back in to continue.






Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In