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Near surface residual stress (NSRS) induced by machining (e.g., milling) is known to drive distortion in machined aluminum, particularly in thin complex geometries with tight tolerance requirements where large distortion is undesirable. The understanding and characterization of NSRS in milled aluminum parts is important and should be included in the design and manufacturing process. There exist a variety of experimental tests characterizing these stresses. The objective of this paper is to assess the quality of three experimental methods for evaluating NSRS in prismatic aluminum parts subject to various milling parameters. The three methods are: hole-drilling, slotting, and x-ray diffraction, all of which include incremental material removal. The aluminum parts are cut from stress-relieved plate, AA7050-T7451. A combination of milling table and tool speeds are used to machine a flat surface in the parts. Measurements are made at specified locations and depths on each part. NSRS data from the hole drilling and slotting measurements were comparable; NSRS data from x-ray diffraction differed and was less repeatable. NSRS data for different milling parameters shows that the depth of NSRS increases with feed per tooth but is unaffected by different cutting speeds.
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Multimodal data-driven machine learning for the prediction of surface topography in end milling
Prediction of surface topography in milling usually requires complex kinematics and dynamics modeling of the milling process, plus solving physical models of surface generation is a daunting task. This paper presents a multimodal data-driven machine learning (ML) method to predict milled surface topography. The proposed method predicts the height map of the surface topography by fusing process parameters and in-process acoustic information as model inputs. This method has been validated by comparing the predicted surface topography with the measured data.
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- PAR ID:
- 10528345
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Production Engineering
- Volume:
- 18
- Issue:
- 3-4
- ISSN:
- 0944-6524
- Page Range / eLocation ID:
- 507 to 523
- Subject(s) / Keyword(s):
- Machine learning Multimodal data Surface topography Milling
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11740-023-01253-z
