Details
| Original language | English |
|---|---|
| Pages (from-to) | 470-474 |
| Number of pages | 5 |
| Journal | Procedia CIRP |
| Volume | 111 |
| Early online date | 6 Sept 2022 |
| Publication status | Published - 2022 |
| Event | 12th CIRP Conference on Photonic Technologies, LANE 2022 - Erlangen, Germany Duration: 4 Sept 2022 → 8 Sept 2022 |
Abstract
Laser transmission welding (LTW) is a known technique to join conventionally produced thermoplastic parts, e.g. injected molded parts. When using LTW for additively manufactured parts (usually prototypes, small series), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. 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. To validate the expert system, specimens are additively manufactured from polylactide. The parameters of the additive manufacturing process, the transmissivity, and the LTW process parameters are used to predict the shear tensile force with the neural network. The transparent samples are welded to black absorbent samples in overlap configuration and shear tensile tests are performed. In this work, the prediction of the shear tensile force with an accuracy of 88.1% of the neuronal network based expert system is demonstrated.
Keywords
- additive manufacturing, fused deposition modeling, Laser transmission welding, neuronal network, shear tensile force, transmissivity
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 111, 2022, p. 470-474.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Expert system-supported optimization of laser welding of additively manufactured thermoplastic components
AU - Kuklik, Julian
AU - Mente, Torben
AU - Wippo, Verena
AU - Jaeschke, Peter
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 Climate Action based on an enactment of the German Parliament.
PY - 2022
Y1 - 2022
N2 - Laser transmission welding (LTW) is a known technique to join conventionally produced thermoplastic parts, e.g. injected molded parts. When using LTW for additively manufactured parts (usually prototypes, small series), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. 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. To validate the expert system, specimens are additively manufactured from polylactide. The parameters of the additive manufacturing process, the transmissivity, and the LTW process parameters are used to predict the shear tensile force with the neural network. The transparent samples are welded to black absorbent samples in overlap configuration and shear tensile tests are performed. In this work, the prediction of the shear tensile force with an accuracy of 88.1% of the neuronal network based expert system is demonstrated.
AB - Laser transmission welding (LTW) is a known technique to join conventionally produced thermoplastic parts, e.g. injected molded parts. When using LTW for additively manufactured parts (usually prototypes, small series), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. 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. To validate the expert system, specimens are additively manufactured from polylactide. The parameters of the additive manufacturing process, the transmissivity, and the LTW process parameters are used to predict the shear tensile force with the neural network. The transparent samples are welded to black absorbent samples in overlap configuration and shear tensile tests are performed. In this work, the prediction of the shear tensile force with an accuracy of 88.1% of the neuronal network based expert system is demonstrated.
KW - additive manufacturing
KW - fused deposition modeling
KW - Laser transmission welding
KW - neuronal network
KW - shear tensile force
KW - transmissivity
UR - http://www.scopus.com/inward/record.url?scp=85141897064&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2022.08.070
DO - 10.1016/j.procir.2022.08.070
M3 - Conference article
AN - SCOPUS:85141897064
VL - 111
SP - 470
EP - 474
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 12th CIRP Conference on Photonic Technologies, LANE 2022
Y2 - 4 September 2022 through 8 September 2022
ER -