Machine Learning Approach to Predict Bead Height and Width in Wire Arc Additive Manufacturing Sample

2023-28-0145

11/10/2023

Features
Event
International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
Authors Abstract
Content
Wire Arc Additive Manufacturing (WAAM) is a type of 3D printing technology which build up layer by layer material using welding to create a finished product. To this extent, we have developed the machine learning approach using the KNN regression model to predict the bead’s height and width of the E71T1 mild steel sample by wire arc additive manufacturing (WAAM). We have conducted a systematic experimental study by varying the process parameters such as Voltage (V), Current (A) and wire feed rate (f), and the corresponding output value: height, and width of the bead are recorded. A total of 195 experiments were conducted, and the corresponding output values were noted. From the experimental data, 80% data was used to train the model, and 20% was used for testing the model. Further, the model’s accuracy was predicted using an independent set of test samples. This approach will enable us to efficiently identify the optimal set of process parameters at a short time duration and reduce the traditional experimental methods.
Meta TagsDetails
DOI
https://doi.org/10.4271/2023-28-0145
Pages
10
Citation
Vincent, A., and Natarajan, H., "Machine Learning Approach to Predict Bead Height and Width in Wire Arc Additive Manufacturing Sample," SAE Technical Paper 2023-28-0145, 2023, https://doi.org/10.4271/2023-28-0145.
Additional Details
Publisher
Published
Nov 10, 2023
Product Code
2023-28-0145
Content Type
Technical Paper
Language
English