Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition
Abstract Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainle...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80350-0 |
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| author | Yu-Xiang Chen Jun-Ru Qiu Wei-Ling Chang Yi-Kai Hwang Sheng-Jye Hwang |
| author_facet | Yu-Xiang Chen Jun-Ru Qiu Wei-Ling Chang Yi-Kai Hwang Sheng-Jye Hwang |
| author_sort | Yu-Xiang Chen |
| collection | DOAJ |
| description | Abstract Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios. |
| format | Article |
| id | doaj-art-74744ee8ab56485bb819e3887b74bc0f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-74744ee8ab56485bb819e3887b74bc0f2024-11-24T12:26:00ZengNature PortfolioScientific Reports2045-23222024-11-0114112110.1038/s41598-024-80350-0Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy depositionYu-Xiang Chen0Jun-Ru Qiu1Wei-Ling Chang2Yi-Kai Hwang3Sheng-Jye Hwang4Department of mechanical Engineering, National Chang Kung UniversityDepartment of mechanical Engineering, National Chang Kung UniversityDepartment of mechanical Engineering, National Chang Kung UniversityDepartment of mechanical Engineering, National Chang Kung UniversityDepartment of mechanical Engineering, National Chang Kung UniversityAbstract Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios.https://doi.org/10.1038/s41598-024-80350-0Directed energy depositionTaguchi experimental design methodMulti-materialsGrey relational analysis and process parameter optimization |
| spellingShingle | Yu-Xiang Chen Jun-Ru Qiu Wei-Ling Chang Yi-Kai Hwang Sheng-Jye Hwang Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition Scientific Reports Directed energy deposition Taguchi experimental design method Multi-materials Grey relational analysis and process parameter optimization |
| title | Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition |
| title_full | Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition |
| title_fullStr | Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition |
| title_full_unstemmed | Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition |
| title_short | Process optimization and mechanical properties analysis of Inconel 718/stainless steel 316 L multi-material via direct energy deposition |
| title_sort | process optimization and mechanical properties analysis of inconel 718 stainless steel 316 l multi material via direct energy deposition |
| topic | Directed energy deposition Taguchi experimental design method Multi-materials Grey relational analysis and process parameter optimization |
| url | https://doi.org/10.1038/s41598-024-80350-0 |
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