PMID- 37050334
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230415
IS  - 2073-4360 (Electronic)
IS  - 2073-4360 (Linking)
VI  - 15
IP  - 7
DP  - 2023 Mar 30
TI  - Prediction of Mechanical Properties for Carbon fiber/PLA Composite Lattice 
      Structures Using Mathematical and ANFIS Models.
LID - 10.3390/polym15071720 [doi]
LID - 1720
AB  - This study investigates the influence of design, relative density (RD), and 
      carbon fiber (CF) incorporation parameters on mechanical characteristics, 
      including compressive modulus (E), strength, and specific energy absorption (SEA) 
      of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices 
      were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) 
      and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS 
      lattice structures were evaluated under uniaxial compression testing based on the 
      design of experiments (DOE) approach, namely, full factorial design. Prediction 
      modeling was conducted and compared using mathematical and intelligent modeling, 
      namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed 
      the 3D printing imperfections (e.g., RD variations) to be taken into account by 
      considering the actual RDs instead of the designed ones, as in the case of 
      mathematical modeling. In this regard, this was the first time the ANFIS modeling 
      utilized the actual RDs. The desirability approach was applied for 
      multi-objective optimization. The mechanical properties were found to be 
      significantly influenced by cell type, cell size, CF incorporation, and RD, as 
      well as their combination. The findings demonstrated a variation in the E (0.144 
      GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 
      J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models 
      outperformed mathematical models in predicting all mechanical characteristics, 
      including E, strength, and SEA. For instance, the maximum absolute percent 
      deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical 
      prediction. The accuracy of mathematical predictions is highly influenced by the 
      degree of RD deviation: a higher deviation in RD indicates a lower accuracy of 
      predictions. The findings of this study provide a prior prediction of the 
      mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better 
      understanding of their potential and limitations.
FAU - Saleh, Mustafa
AU  - Saleh M
AUID- ORCID: 0000-0002-2806-7827
AD  - Industrial Engineering Department, College of Engineering, King Saud University, 
      P.O. Box 800, Riyadh 11421, Saudi Arabia.
FAU - Anwar, Saqib
AU  - Anwar S
AUID- ORCID: 0000-0003-2657-163X
AD  - Industrial Engineering Department, College of Engineering, King Saud University, 
      P.O. Box 800, Riyadh 11421, Saudi Arabia.
FAU - Al-Ahmari, Abdulrahman M
AU  - Al-Ahmari AM
AUID- ORCID: 0000-0002-3079-0141
AD  - Industrial Engineering Department, College of Engineering, King Saud University, 
      P.O. Box 800, Riyadh 11421, Saudi Arabia.
FAU - AlFaify, Abdullah Yahia
AU  - AlFaify AY
AUID- ORCID: 0000-0001-9386-1720
AD  - Industrial Engineering Department, College of Engineering, King Saud University, 
      P.O. Box 800, Riyadh 11421, Saudi Arabia.
LA  - eng
GR  - IFKSUDR_P114/Deputyship for Research & Innovation, "Ministry of Education" in 
      Saudi Arabia/
PT  - Journal Article
DEP - 20230330
PL  - Switzerland
TA  - Polymers (Basel)
JT  - Polymers
JID - 101545357
PMC - PMC10097322
OTO - NOTNLM
OT  - ANFIS
OT  - FDM
OT  - TPMS lattice structures
OT  - additive manufacturing
OT  - artificial intelligence
OT  - biodegradable polymer
OT  - carbon fiber-reinforced PLA
OT  - composites
OT  - compression testing
OT  - specific energy absorption
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/14 06:00
MHDA- 2023/04/14 06:01
PMCR- 2023/03/30
CRDT- 2023/04/13 01:29
PHST- 2023/02/14 00:00 [received]
PHST- 2023/03/17 00:00 [revised]
PHST- 2023/03/22 00:00 [accepted]
PHST- 2023/04/14 06:01 [medline]
PHST- 2023/04/13 01:29 [entrez]
PHST- 2023/04/14 06:00 [pubmed]
PHST- 2023/03/30 00:00 [pmc-release]
AID - polym15071720 [pii]
AID - polymers-15-01720 [pii]
AID - 10.3390/polym15071720 [doi]
PST - epublish
SO  - Polymers (Basel). 2023 Mar 30;15(7):1720. doi: 10.3390/polym15071720.