Details
| Original language | English |
|---|---|
| Title of host publication | High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XI |
| Publisher | SPIE |
| Publication status | Published - 4 Mar 2022 |
| Event | High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XI 2022 - Virtual, Online Duration: 20 Feb 2022 → 24 Feb 2022 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Publisher | SPIE |
| Volume | 11994 |
| ISSN (Print) | 0277-786X |
Abstract
Laser transmission welding (LTW) is a known technique to join conventionally produced high volume thermoplastic parts, e.g. injected molded parts for the automotive sector. For using LTW for additively manufactured parts (usually prototypes, small series, or one-off products), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. In comparison to the injection molding process, the additive manufacturing process leads to an inhomogeneous structure with trapped air inside the volume. Therefore, a change in the transmissivity results due to the additive manufacturing process. In this paper, a method is presented to enhance the weld seam quality of laser welded additively manufactured parts assisted by a neural network-based expert system. The designed expert system supports the user setting up the additive manufacturing process. With the results of a preliminary work, a neural network is trained to predict the transmissivity values of the transparent samples. To validate the expert system, specimen of transparent polylactide are additively manufactured with various manufacturing parameters in order to change the transmissivity. The transmissivity of the parts are measured with a spectroscope. The parameters of the additive manufacturing process are used to predict the transmissivity with the neural network and are compared to the measurements. The transparent samples are welded to black polylactide samples with different laser power in overlap configuration and shear tensile tests are performed. With these experiments, the prediction of additive manufacturing parameters with the expert system in order to use the parts for a LTW process is demonstrated.
Keywords
- additive manufacturing, fused deposition modeling, Laser transmission welding, neuronalnetwork, transmissivity
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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- BibTeX
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High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XI. SPIE, 2022. 119940G (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11994).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Laser welding of additively manufactured thermoplastic components assisted by a neural network-based expert system
AU - Kuklik, Julian
AU - Mente, Torben
AU - Wippo, Verena
AU - Jaeschke, Peter
AU - Kuester, Benjamin
AU - Stonis, Malte
AU - Kaierle, Stefan
AU - Overmeyer, Ludger
N1 - Funding Information: The IGF-project „Qualitätssicherung beim Laserstrahlschweißen additiv gefertigter thermoplastischer Bauteile - QualLa“ (Nr. 21571N) of the Research Community for Quality (FQS), August-Schanz-Straße 21A, 60433 Frankfurt/Main has been funded by the AiF within the programme for sponsorship by Industrial Joint Research (IGF) of the German Federal Ministry of Economic Affairs and Energy based on an enactment of the German Parliament.
PY - 2022/3/4
Y1 - 2022/3/4
N2 - Laser transmission welding (LTW) is a known technique to join conventionally produced high volume thermoplastic parts, e.g. injected molded parts for the automotive sector. For using LTW for additively manufactured parts (usually prototypes, small series, or one-off products), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. In comparison to the injection molding process, the additive manufacturing process leads to an inhomogeneous structure with trapped air inside the volume. Therefore, a change in the transmissivity results due to the additive manufacturing process. In this paper, a method is presented to enhance the weld seam quality of laser welded additively manufactured parts assisted by a neural network-based expert system. The designed expert system supports the user setting up the additive manufacturing process. With the results of a preliminary work, a neural network is trained to predict the transmissivity values of the transparent samples. To validate the expert system, specimen of transparent polylactide are additively manufactured with various manufacturing parameters in order to change the transmissivity. The transmissivity of the parts are measured with a spectroscope. The parameters of the additive manufacturing process are used to predict the transmissivity with the neural network and are compared to the measurements. The transparent samples are welded to black polylactide samples with different laser power in overlap configuration and shear tensile tests are performed. With these experiments, the prediction of additive manufacturing parameters with the expert system in order to use the parts for a LTW process is demonstrated.
AB - Laser transmission welding (LTW) is a known technique to join conventionally produced high volume thermoplastic parts, e.g. injected molded parts for the automotive sector. For using LTW for additively manufactured parts (usually prototypes, small series, or one-off products), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. In comparison to the injection molding process, the additive manufacturing process leads to an inhomogeneous structure with trapped air inside the volume. Therefore, a change in the transmissivity results due to the additive manufacturing process. In this paper, a method is presented to enhance the weld seam quality of laser welded additively manufactured parts assisted by a neural network-based expert system. The designed expert system supports the user setting up the additive manufacturing process. With the results of a preliminary work, a neural network is trained to predict the transmissivity values of the transparent samples. To validate the expert system, specimen of transparent polylactide are additively manufactured with various manufacturing parameters in order to change the transmissivity. The transmissivity of the parts are measured with a spectroscope. The parameters of the additive manufacturing process are used to predict the transmissivity with the neural network and are compared to the measurements. The transparent samples are welded to black polylactide samples with different laser power in overlap configuration and shear tensile tests are performed. With these experiments, the prediction of additive manufacturing parameters with the expert system in order to use the parts for a LTW process is demonstrated.
KW - additive manufacturing
KW - fused deposition modeling
KW - Laser transmission welding
KW - neuronalnetwork
KW - transmissivity
UR - http://www.scopus.com/inward/record.url?scp=85131216671&partnerID=8YFLogxK
U2 - 10.1117/12.2609365
DO - 10.1117/12.2609365
M3 - Conference contribution
AN - SCOPUS:85131216671
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XI
PB - SPIE
T2 - High-Power Laser Materials Processing
Y2 - 20 February 2022 through 24 February 2022
ER -