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 language | English |
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Title of host publication | 40th North American Manufacturing Research Conference 2012 - Transactions of the North American Manufacturing Research Institution of SME |
Pages | 297-306 |
Number of pages | 10 |
State | Published - 2012 |
Externally published | Yes |
Event | 40th Annual North American Manufacturing Research Conference, NAMRC40 - Notre Dame, IN, United States Duration: Jun 4 2012 → Jun 8 2012 |
Publication series
Name | Transactions of the North American Manufacturing Research Institution of SME |
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Volume | 40 |
ISSN (Print) | 1047-3025 |
Conference
Conference | 40th Annual North American Manufacturing Research Conference, NAMRC40 |
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Country/Territory | United States |
City | Notre Dame, IN |
Period | 06/4/12 → 06/8/12 |
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.