Surface Roughness Prediction in End Milling Processes Considering the Vibration of the Cutting Tool
Portland State University. Department of Mechanical and Materials Engineering
Term of Graduation
Date of Publication
Doctor of Philosophy (Ph.D.) in Mechanical Engineering
Mechanical and Materials Engineering
1 online resource (xxv, 210 pages)
Purpose: Machining is a manufacturing process for making the desired design with dimensional tolerance and surface roughness. Despite the development of engineering technology, predicting machined surface roughness is still challenging since there are various factors, such as material properties, tool material properties, the rigidity of the machine tool, and the use of coolant. In particular, tool vibration is the most critical factor since it makes it challenging to obtain the desired quality of the machined product by unintentionally making a tool move. Therefore, surface finish was explored in this study, considering cutting tool vibration under various axial depth-of-cuts and feed speeds.
Design/Methodology/Approach: This study investigated the relationship between milling tool vibrations and machining parameters by computing milling simulation through the Newmark integration method. Then, several surface roughness prediction models, considering tool vibrations, were compared.
Findings: In a correlation of milling parameters, cutting forces, and tool vibration, an increase in axial depth-of-cut and/or feed speed led to an increase in the magnitude of tool vibration. Also, an increase in axial depth-of-cut/or feed speed resulted in a high surface finish value.
Practical Implication: This study will help industries predict milling cutting forces, vibrations, cutter trajectory, and surface roughness. Also, when developing a new cutting tool, this study can be used to understand the relationship between cutting tool properties and metal cutting parameters.
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Lee, Yubin, "Surface Roughness Prediction in End Milling Processes Considering the Vibration of the Cutting Tool" (2023). Dissertations and Theses. Paper 6313.
Available for download on Saturday, March 09, 2024