The fundamental application of decision analysis to manufacturing.
1 online resource (199 pages) : PDF
University of North Carolina at Charlotte
Machining models are available to predict nearly every aspect of machining processes. In milling, for example, models are available to relate stability, part accuracy (from forced vibrations during stable machining), and tool wear to the selected operating parameters, material and tool properties, tool geometry, and part-tool-holder-spindle-machine dynamics. The models capture the underlying physics. However, models are deterministic and do not take into account the uncertainty that exists due to the model assumptions, model inputs, and factors that are unknown. Therefore, to enable reliable parameter selection using process models, uncertainty should be included in the formulation. This research will apply the normative mathematical framework of decision theory to select optical machining parameters while taking into account the inherent uncertainty in milling processes. The objective function will be profit because it (arguably) represents the decision maker's primary motivation in the manufacturing environment. The objective of this research is to select the optimal machining parameters which minimize cost while considering the uncertainty in tool life and stability for a given machine, tool, tool path and workpiece material.
BAYESIAN INFERENCEDECISION ANALYSISMACHINING STABILITYTOOL WEARUNCERTAINTYVALUE OF INFORMATION
Ziegert, JohnOzelkan, ErtungaSuleski, Tom
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2013.
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.
Copyright is held by the author unless otherwise indicated.