TY - JOUR
T1 - Towards rapid, in situ characterization for materials-on-demand manufacturing
AU - Iquebal, Ashif Sikandar
AU - Botcha, Bhaskar
AU - Bukkapatnam, Satish
N1 - Funding Information:
The authors would like to sincerely acknowledge the support from the National Science Foundation through grants CMMI 1432914 , and CMMI 1301439 , and the X-Grants, part of the Texas A&M President’s Excellence Fund. The authors would also like to thank B N Ravi Srivatsa, Indian Institute of Technology, Tirupati for verifying the classification results presented in this work.
Publisher Copyright:
© 2019 Society of Manufacturing Engineers (SME)
PY - 2020/1
Y1 - 2020/1
N2 - Recent hybrid additive-subtractive manufacturing platforms can usher a materials-on-demand manufacturing paradigm where the materials, along with geometry and surface morphology, are designed to meet the functionality. While in situ measurement systems have advanced to monitor geometric and morphological variations, microstructure and phase characterization methods remain slow, impeding this vision. Towards addressing this issue, we present an approach to characterize the material concurrently during fabrication by associating in situ sensor measurements with the microstructures. Analysis of sensor data from machining of 316L steel suggests that the microstructural variations under different process parameters can be predicted in real time with 93% accuracy.
AB - Recent hybrid additive-subtractive manufacturing platforms can usher a materials-on-demand manufacturing paradigm where the materials, along with geometry and surface morphology, are designed to meet the functionality. While in situ measurement systems have advanced to monitor geometric and morphological variations, microstructure and phase characterization methods remain slow, impeding this vision. Towards addressing this issue, we present an approach to characterize the material concurrently during fabrication by associating in situ sensor measurements with the microstructures. Analysis of sensor data from machining of 316L steel suggests that the microstructural variations under different process parameters can be predicted in real time with 93% accuracy.
KW - High-resolution sensing
KW - Hybrid manufacturing
KW - Machine learning
KW - Machining dynamics
KW - Microstructure characterization
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U2 - 10.1016/j.mfglet.2019.11.002
DO - 10.1016/j.mfglet.2019.11.002
M3 - Article
AN - SCOPUS:85076780224
SN - 2213-8463
VL - 23
SP - 29
EP - 33
JO - Manufacturing Letters
JF - Manufacturing Letters
ER -