Spindle speed selection for tool life testing using Bayesian inference

Jaydeep M. Karandikar, Tony L. Schmitz, Ali E. Abbas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

According to the Taylor tool life equation, tool life is dependent on cutting speed (or spindle speed for a selected tool diameter in milling) and their relationship is quantified empirically using a power law exponent, n, and a constant, C, which are tool-workpiece dependent. However, the Taylor tool life model is deterministic and does not incorporate the inherent uncertainty in tool life. In this work, Bayesian inference is applied to estimate tool life. With this approach, tool life is described using a probability distribution at each spindle speed. Random sample tool life curves are then generated and the probability that a selected curve represents the true tool life curve is updated using experimental results. Tool wear tests are performed using an inserted (uncoated) carbide endmill to machine AISI 1018 steel. The test point selection is based on the maximum value of information approach. The updated beliefs are then used to predict tool life using a probability distribution function.

Original languageEnglish
Title of host publication40th North American Manufacturing Research Conference 2012 - Transactions of the North American Manufacturing Research Institution of SME
Pages297-306
Number of pages10
StatePublished - 2012
Externally publishedYes
Event40th Annual North American Manufacturing Research Conference, NAMRC40 - Notre Dame, IN, United States
Duration: Jun 4 2012Jun 8 2012

Publication series

NameTransactions of the North American Manufacturing Research Institution of SME
Volume40
ISSN (Print)1047-3025

Conference

Conference40th Annual North American Manufacturing Research Conference, NAMRC40
Country/TerritoryUnited States
CityNotre Dame, IN
Period06/4/1206/8/12

Funding

The authors gratefully acknowledge financial support from the National Science Foundation ( CMMI-0927051 and CMMI-0926667 ). They would also like to thank M. Traverso and G. Hazelrigg for numerous helpful discussions.

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