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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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An Examination of the Utilization of Machine Learning in Fused Deposition Modeling.

Author(s):Jayaram H.K1, Mohan R2, Mahesh Babu S3

Affiliation: Department of Mechanical Engineering ,1.2.3. Sri Venkateswara college of Engineering, Chittoor, India.

Page No: 19-23

Volume issue & Publishing Year: Volume 1 Issue 2,June-2024

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

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Abstract:
Fused deposition modeling (FDM) is a type of additive manufacturing (AM) that creates components by layering material in a sequential manner. Compared to traditional manufacturing techniques, FDM can produce complex geometries and intricate details more quickly, without the need for a fixed process plan or specialized tooling, and requires minimal human intervention. FDM parts exhibit excellent heat and chemical resistance, along with impressive strength-to-weight ratios. However, challenges remain with the consistency, reliability, and accuracy of FDM-produced parts. To ensure consistent quality and process reliability, real-time monitoring of the FDM process is essential. Recent research indicates that machine learning (ML) models offer a powerful computational approach to help AM processes achieve high-quality standards, maintain product consistency, and optimize process outcomes. Despite its potential, the integration of ML with FDM remains relatively unexplored. While existing research is limited, there is a lack of review-based studies on the application of ML in the FDM process, which could guide future research. This paper aims to fill that gap by providing a comprehensive overview of the use of ML in FDM. Keywords: Fused Deposition Modeling, Machine Learning

Keywords: Fused Deposition Modeling,Machine Learning

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