Comparing the Predictability of Soft Computing and Statistical Techniques for the Prediction of Tensile Strength of Additively Manufactured Carbon Fiber Polylactic Acid Parts

The objective of this study is to investigate the influence of input factors, namely layer height (LH), print speed (PS), and infill line direction (ID), on the tensile strength (TS) of polymer components fabricated using fused deposition modelling. The primary objective of this study is to construc...

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Veröffentlicht in:Journal of materials engineering and performance Jg. 33; H. 22; S. 12729 - 12741
Hauptverfasser: Raj, Abhishek, Tyagi, Bobby, Goyal, Ashish, Sahai, Ankit, Sharma, Rahul Swarup
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2024
Springer Nature B.V
Schlagworte:
Accuracy
Adaptive systems
Artificial intelligence
Artificial neural networks
Back propagation networks
Carbon fibers
Characterization and Evaluation of Materials
Chemistry and Materials Science
Corrosion and Coatings
Engineering Design
Fused deposition modeling
Fuzzy logic
Genetic algorithms
Machine learning
Materials Science
Mechanical properties
Neural networks
Original Research Article
Orthogonal arrays
Polylactic acid
Polymers
Prediction models
Quality Control
Reliability
Response surface methodology
Safety and Risk
Soft computing
Statistical methods
Structural integrity
Tensile strength
Tribology
Variables
ANN
additive manufacturing
ANFIS
fused deposition modelling
RSM
carbon fibre PLA
ISSN:1059-9495, 1544-1024
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Zusammenfassung:The objective of this study is to investigate the influence of input factors, namely layer height (LH), print speed (PS), and infill line direction (ID), on the tensile strength (TS) of polymer components fabricated using fused deposition modelling. The primary objective of this study is to construct a robust prediction model for TS utilising soft computing methodologies, namely a two-layered feed-forward backpropagation algorithm and a hybrid neural network-integrated fuzzy interface system (FIS). The specimens utilised for analysis were fabricated using carbon fibre fibre-reinforced polylactic acid (CF-PLA) composites per the ASTM D638 standard. A dataset is generated using a C27 orthogonal array to capture variations in LH, PS, and ID techniques. In this study, two soft computing methodologies, namely an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS), are employed to effectively describe the fused deposition process and accurately predict the TS of the printed objects. The performance of these strategies is evaluated in comparison to the response surface methodology (RSM). The findings imply an inverse correlation between the TS and the LH, indicating that decreasing the LH can improve the structural integrity of the printed components. Furthermore, The ID region’s effect depends on the tensile force’s orientation. Infill lines aligned at 0° had the highest TS, while those at 90° had the lowest. The results of this study show how input variables affect the strength of additively produced (AM) polymer components. Soft computing methods enable AM parameter optimisation and accurate TS forecasts. The ANFIS method predicted tensile strength better than ANN and RSM. The negative relationship between LH and TS emphasises the importance of choosing the right LH for mechanical qualities.
Bibliographie:ObjectType-Article-1
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ISSN:1059-9495
1544-1024
DOI:10.1007/s11665-023-08844-y