Towards rapid, in situ characterization for materials-on-demand manufacturing

Ashif Sikandar Iquebal; Bhaskar Botcha; Satish Bukkapatnam

doi:10.1016/j.mfglet.2019.11.002

Towards rapid, in situ characterization for materials-on-demand manufacturing

Ashif Sikandar Iquebal, Bhaskar Botcha, Satish Bukkapatnam

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	29-33
Number of pages	5
Journal	Manufacturing Letters
Volume	23
DOIs	https://doi.org/10.1016/j.mfglet.2019.11.002
State	Published - Jan 2020
Externally published	Yes

Keywords

High-resolution sensing
Hybrid manufacturing
Machine learning
Machining dynamics
Microstructure characterization

ASJC Scopus subject areas

Mechanics of Materials
Industrial and Manufacturing Engineering

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10.1016/j.mfglet.2019.11.002

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title = "Towards rapid, in situ characterization for materials-on-demand manufacturing",

abstract = "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.",

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note = "Publisher Copyright: {\textcopyright} 2019 Society of Manufacturing Engineers (SME)",

year = "2020",

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doi = "10.1016/j.mfglet.2019.11.002",

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AU - Iquebal, Ashif Sikandar

AU - Botcha, Bhaskar

AU - Bukkapatnam, Satish

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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JO - Manufacturing Letters

JF - Manufacturing Letters

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Towards rapid, in situ characterization for materials-on-demand manufacturing

Abstract

Keywords

ASJC Scopus subject areas

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