Abstract
Tool wear development is an important parameter to control in order to obtain a specific surface integrity in machining operations. Unfortunately, it is difficult to accurately predict the tool life since wear rates generally exhibit a large scattering, and in consequence a scattering in the surface roughness is also present. This paper presents an approach to evaluate uncertainty contributors for tool wear and surface roughness in end milling of superalloy Inconel 718. Multiple regression analysis was applied to develop empirical models of tool wear and surface roughness based on the experimental data. Principles of uncertainty in measurement were applied and uncertainty contributors were identified. It was shown that cutting speed is the principal contributor to the combined uncertainty of tool wear and surface roughness.
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