Applications of Optical Sensors in Additive Manufacturing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".
Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 1341
Special Issue Editors
Interests: additive manufacturing; sensors metrology; on-machine measurement
Interests: manufacturing process mechanics; additive manufacturing; CAD; design engineering; machining; mechanical processes; CNC machining; product design and development; engineering drawing; product development
Special Issue Information
Dear Colleagues,
Despite the potential benefits inherent in Additive Manufacturing (AM), technologies related to this concept have not yet met the expected level of adoption largely due to persisting concerns related to the quality of the produced components.
Addressing these issues would require a comprehensive assessment of the manufacturing process and meticulous process control. In this context, the utilization of optical sensors distinguishes itself by its ability to swiftly and non-intrusively collect multifaceted data. Furthermore, integrating these sensors into existing AM machines is relatively straightforward, and they can operate effectively across a wide array of environments.
For this Special Issue of Applied Sciences, "Applications of Optical Sensors in Additive Manufacturing", we would like to send an open invitation to the academic and scientific community to submit scholarly contributions pertaining to the integration and application of optical sensors within the additive manufacturing process. These contributions can be related to the following:
- The improvement of product quality;
- The monitoring of the manufacturing process;
- The integration of optical sensors into AM machines for real‑time or in situ measurements;
- The analysis or processing of data collected from these sensors in the context of AM components or machines.
Dr. Pablo Zapico
Dr. David Blanco
Guest Editors
Manuscript Submission Information
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Keywords
- dimensional and/or geometric assessment of the layers and/or the part
- residual stress detection
- surface defect detection
- sub-surface defect detection
- process monitoring (consistency during extrusion, bed of particles unevenness, environment, etc.)
- raw material or non-used material analysis
- feedstock delivery system monitoring
- data handling and processing (layer contour detection, deep learning, etc.)
- in situ or in-process optical sensor integration
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