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Optimization and ANFIS Predictive Modeling of Additive Manufacturing (Fused Deposition Modeling) for PLA Material Mohan Babu University
- Format:
- Book
- Conference/Event
- Author/Creator:
- Pasupuleti, Thejasree, author.
- Conference Name:
- 11th SAEINDIA International Mobility Conference (SIIMC 2024) (2024-12-11 : New Delhi, India)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a revolutionary technology for creating intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly properties, affordability, and ease of use. The objective of this study is to optimize the FDM parameters for PLA material and create predictive models using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast printing performance. An investigation was carried out through experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes such as dimensional accuracy, surface finish, and mechanical properties. The utilization of design of experiments (DOE) methodology enabled a methodical exploration of parameters. A predictive model using ANFIS was created to forecast printing performance by utilizing input parameters. The results demonstrated the effectiveness of the ANFIS predictive models in accurately predicting printing performance for PLA material. The models offer valuable insights into the most effective parameter configurations for maximizing printing efficiency and ensuring high-quality parts. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides a useful tool for optimizing the manufacturing process. Manufacturers can improve printing productivity and quality by utilizing ANFIS predictive models. This will help promote the wider use of FDM technology in different industries such as prototyping, manufacturing, and healthcare
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-28-0240
- Access Restriction:
- Restricted for use by site license
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