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Quality detection of laser additive manufacturing process based on coaxial vision monitoring

Quality detection of laser additive manufacturing process based on coaxial vision monitoring This paper aims to explore the influences of different process parameters, including laser power, scanning speed, defocusing distance and scanning mode, on the shape features of molten pool and, based on the obtained relationship, realize the diagnosis of forming defects during the process.Design/methodology/approachMolten pool was captured on-line based on a coaxial CCD camera mounted on the welding head, then image processing algorithms were developed to obtain melt pool features that could reflect the forming status, and it suggested that the molten pool area was the most sensitive characteristic. The influence of the processing parameters such as laser power, traverse speed, powder feed rate, defocusing distance and the melt pool area was studied, and then the melt pool area was used as the characteristic to detect the forming defects during the cladding and additive manufacturing process.FindingsThe influences of different process parameters on molten pool area were explored. Based on the relationship, different types of defects were accurately detected through analyzing the relationship between the molten pool area and time.Originality/valueThe findings would be helpful for the quality control of laser additive manufacturing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

Quality detection of laser additive manufacturing process based on coaxial vision monitoring

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0260-2288
DOI
10.1108/sr-03-2018-0068
Publisher site
See Article on Publisher Site

Abstract

This paper aims to explore the influences of different process parameters, including laser power, scanning speed, defocusing distance and scanning mode, on the shape features of molten pool and, based on the obtained relationship, realize the diagnosis of forming defects during the process.Design/methodology/approachMolten pool was captured on-line based on a coaxial CCD camera mounted on the welding head, then image processing algorithms were developed to obtain melt pool features that could reflect the forming status, and it suggested that the molten pool area was the most sensitive characteristic. The influence of the processing parameters such as laser power, traverse speed, powder feed rate, defocusing distance and the melt pool area was studied, and then the melt pool area was used as the characteristic to detect the forming defects during the cladding and additive manufacturing process.FindingsThe influences of different process parameters on molten pool area were explored. Based on the relationship, different types of defects were accurately detected through analyzing the relationship between the molten pool area and time.Originality/valueThe findings would be helpful for the quality control of laser additive manufacturing.

Journal

Sensor ReviewEmerald Publishing

Published: Jul 26, 2019

Keywords: Laser additive manufacturing; Forming defects; Molten pool area; Visual sensing

References