Machine Learning Approach to Predict Bead Height and Width in Wire Arc Additive Manufacturing Sample
Machine Learning Approach to Predict Bead Height and Width in Wire Arc Additive Manufacturing Sample
- New search for: Vincent, Akash
- New search for: Natarajan, Harshavardhana
- New search for: Vincent, Akash
- New search for: Natarajan, Harshavardhana
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.
Document information
Machine Learning Approach to Predict Bead Height and Width in Wire Arc Additive Manufacturing Sample
Sae Technical Papers
International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility ; 2023
2023-11-10
Conference paper
English
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