Journal article
Identification of common sensory features for the control of CNC milling operations under varying cutting conditions
International journal of machine tools & manufacture, v 43(9), pp 897-904
01 Jul 2003
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The main focus of this study is to identify the most influential and common sensory features for the process quality characteristics in CNC milling operations—dimensional accuracy (bore size tolerance) and surface roughness—using three different material types (6061-T6 aluminum, 7075-T6 aluminum, and ANSI-4140 steel). The materials were machined on a vertical CNC mill, retrofitted with multiple sensors and data acquisition systems, to investigate the effects of variations in material types and machining parameters. The sensor data include cutting force measurements, spindle quill vibration, and acoustic emission, each of which further divided into measurable components, such as
x,
y, and
z components in cutting force, x and
y spindle quill vibration, DC, AC, and Count Rate for acoustic emission signals. Those components were filtered and analyzed to determine the sensory features that best correlate with process quality characteristics. Tool wear rate and machining characteristics appeared differently, depending on the material types, yet some components of the sensory data were found to be significant with relation to the variations in bore size and surface roughness for all three types of materials. This suggests that even under the varying cutting conditions involving different materials, the identified sensory features can be used for the reliable and accurate control of milling operations.
Metrics
Details
- Title
- Identification of common sensory features for the control of CNC milling operations under varying cutting conditions
- Creators
- Yalcin M. Ertekin - Western Kentucky UniversityYongjin Kwon - University of IowaTzu-Liang (Bill) Tseng - Western Kentucky University
- Publication Details
- International journal of machine tools & manufacture, v 43(9), pp 897-904
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Technology
- Web of Science ID
- WOS:000183194800005
- Scopus ID
- 2-s2.0-0038218320
- Other Identifier
- 991020531861004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Manufacturing
- Engineering, Mechanical