APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS
Fused Deposition Modeling, among the various 3D printing approaches, is becoming more and more popular because of its capacity to produce complicated parts quickly. The tensile strength of parts printed with polylactic acid (PLA) showed a significant variation of many factors such as printing speed,...
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| Format: | Article |
| Language: | English |
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UIN Sunan Kalijaga, Faculty of Science and Technology, Industrial Engineering Department
2022-07-01
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| Series: | Journal of Industrial Engineering and Halal Industries |
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| Online Access: | https://ejournal.uin-suka.ac.id/saintek/JIEHIS/article/view/3468 |
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| _version_ | 1841558763784372224 |
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| author | Nguyen Huu Tho |
| author_facet | Nguyen Huu Tho |
| author_sort | Nguyen Huu Tho |
| collection | DOAJ |
| description | Fused Deposition Modeling, among the various 3D printing approaches, is becoming more and more popular because of its capacity to produce complicated parts quickly. The tensile strength of parts printed with polylactic acid (PLA) showed a significant variation of many factors such as printing speed, printing temperature, printing angle and infill pattern. This study presented an experimental investigation of collecting data with four input factors namely printing speed, printing temperature, printing angle and infill pattern with the tensile strength response. The research methodology of the RSM Box-Behnken DOE method, ANN (Artificial neural network), and ANFIS (Adaptive neuro-fuzzy inference systems) has been used to determine the optimum process 3D printing parameters. The obtained results based on RSM, ANN and ANFIS methods are used to predict the tensile strength of 3D printed FDM details. The best tensile value is 7,03303 MPa corresponding to print speed of 30,0003 mm/s, printing temperature of 211,594℃, printing angle of 90° with Honeycomb” infill printing pattern. Moreover, the results also highlighted that ANFIS is potential approach for forecasting the tensile strength of 3D printing parts more competitively. |
| format | Article |
| id | doaj-art-463a7552e3ab4d5785ce5f5ff5b09b43 |
| institution | Kabale University |
| issn | 2722-8150 2722-8142 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | UIN Sunan Kalijaga, Faculty of Science and Technology, Industrial Engineering Department |
| record_format | Article |
| series | Journal of Industrial Engineering and Halal Industries |
| spelling | doaj-art-463a7552e3ab4d5785ce5f5ff5b09b432025-01-06T05:54:18ZengUIN Sunan Kalijaga, Faculty of Science and Technology, Industrial Engineering DepartmentJournal of Industrial Engineering and Halal Industries2722-81502722-81422022-07-0131647610.14421/jiehis.34683094APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTSNguyen Huu Tho0Ho Chi Minh City University of Food IndustryFused Deposition Modeling, among the various 3D printing approaches, is becoming more and more popular because of its capacity to produce complicated parts quickly. The tensile strength of parts printed with polylactic acid (PLA) showed a significant variation of many factors such as printing speed, printing temperature, printing angle and infill pattern. This study presented an experimental investigation of collecting data with four input factors namely printing speed, printing temperature, printing angle and infill pattern with the tensile strength response. The research methodology of the RSM Box-Behnken DOE method, ANN (Artificial neural network), and ANFIS (Adaptive neuro-fuzzy inference systems) has been used to determine the optimum process 3D printing parameters. The obtained results based on RSM, ANN and ANFIS methods are used to predict the tensile strength of 3D printed FDM details. The best tensile value is 7,03303 MPa corresponding to print speed of 30,0003 mm/s, printing temperature of 211,594℃, printing angle of 90° with Honeycomb” infill printing pattern. Moreover, the results also highlighted that ANFIS is potential approach for forecasting the tensile strength of 3D printing parts more competitively.https://ejournal.uin-suka.ac.id/saintek/JIEHIS/article/view/3468 fdm 3d printing boc behnken ann fanfis doe |
| spellingShingle | Nguyen Huu Tho APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS Journal of Industrial Engineering and Halal Industries fdm 3d printing boc behnken ann fanfis doe |
| title | APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS |
| title_full | APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS |
| title_fullStr | APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS |
| title_full_unstemmed | APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS |
| title_short | APPLICATION OF BOX-BEHNKEN, ANN, AND ANFIS TECHNIQUES FOR IDENTIFICATION OF THE OPTIMUM PROCESSING PARAMETERS FOR FDM 3D PRINTING PARTS |
| title_sort | application of box behnken ann and anfis techniques for identification of the optimum processing parameters for fdm 3d printing parts |
| topic | fdm 3d printing boc behnken ann fanfis doe |
| url | https://ejournal.uin-suka.ac.id/saintek/JIEHIS/article/view/3468 |
| work_keys_str_mv | AT nguyenhuutho applicationofboxbehnkenannandanfistechniquesforidentificationoftheoptimumprocessingparametersforfdm3dprintingparts |