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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Abstract	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.
AbstractList	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.
Author	Goyal, Ashish Sharma, Rahul Swarup Raj, Abhishek Tyagi, Bobby Sahai, Ankit
Author_xml	– sequence: 1 givenname: Abhishek surname: Raj fullname: Raj, Abhishek organization: Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute – sequence: 2 givenname: Bobby surname: Tyagi fullname: Tyagi, Bobby organization: Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute – sequence: 3 givenname: Ashish surname: Goyal fullname: Goyal, Ashish organization: Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute – sequence: 4 givenname: Ankit surname: Sahai fullname: Sahai, Ankit email: sahaiankit@dei.ac.in organization: Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute – sequence: 5 givenname: Rahul Swarup surname: Sharma fullname: Sharma, Rahul Swarup organization: Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute
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SubjectTerms	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
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Title	Comparing the Predictability of Soft Computing and Statistical Techniques for the Prediction of Tensile Strength of Additively Manufactured Carbon Fiber Polylactic Acid Parts
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