Development of ANFIS Predictive Model for Additive Manufacturing of TPU Material Mohan Babu University

Format:

Book

Conference/Event

Author/Creator:

Pasupuleti, Thejasree, author.

Contributor:

A, Gnanarathinam

D, Palanisamy

Kiruthika, Jothi

Natarajan, Manikandan

Umapathi, D.

Conference Name:

International Automotive CAE Conference Road to Virtual World (2024-10-23 : Delhi, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Additive Manufacturing (AM) techniques, particularly Fusion Deposition Modeling (FDM), have received considerable interest due to their capacity to create complex structures using a diverse array of materials. The objective of this study is to improve the process control and efficiency of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material by creating a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The study investigates the impact of FDM process parameters, including layer height, nozzle temperature, and printing speed, on key printing attributes such as tensile strength, flexibility, and surface quality. Several experimental trials are performed to gather data on these parameters and their corresponding printing attributes. The ANFIS predictive model is built using the collected dataset to forecast printing characteristics by analyzing input process parameters. The ANFIS model utilizes the learning capabilities of neural networks and fuzzy logic systems to analyze the intricate relationships within the FDM process. This model allows for precise predictions of printing outcomes. The model shows its ability to precisely forecast printing attributes, enabling the determination of ideal process parameter configurations for enhanced FDM performance with TPU material. The proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) predictive model presents a methodical strategy for optimizing Fused Deposition Modeling (FDM) parameters. This model serves as a valuable tool for manufacturers to improve productivity and product quality in additive manufacturing operations using Thermoplastic Polyurethane (TPU) material. This research enhances the comprehension of FDM processes and provides practical recommendations for optimizing AM operations in diverse industrial applications

Notes:

Vendor supplied data

Publisher Number:

2024-28-0025

Access Restriction:

Restricted for use by site license