Abstract
This paper presents the development of a supervised machine learning model to predict dimensional accuracy for different parts fabricated using fused deposition modeling (FDM) technique. Supervised learning models, namely, polynomial regression, decision trees, and random forest regression were used to predict the overall dimensional accuracy as well as that of individual part features. All three selected algorithms performed satisfactorily when random datasets from existing data were provided for predicting the expected accuracy of the parts produced using FDM, which validates the proposed model. The results also show that the polynomial regression model provided the best results by predicting the dimensions most accurately. The proposed machine learning approach could be further implemented to develop models for recommending appropriate additive manufacturing process parameters for attaining best accuracy.
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Abbreviations
- AM:
-
Additive Manufacturing
- ANN:
-
Artificial Neural Network
- BP-NN:
-
Back Propagation Neural Network
- CNN:
-
Convolutional Neural Network
- DEM:
-
Discrete Element Method
- FDM:
-
Fused Deposition Modeling
- FF-NN:
-
Feed Forward Neural Network
- GA:
-
Genetic Algorithm
- LPBF:
-
Laser Powder Bed Fusion
- ML:
-
Machine Learning
- PLA:
-
Polylactic Acid
- RP:
-
Rapid Prototyping
- S/N:
-
Signal-to-Noise
- SVM:
-
Support Vector Machine
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Bansal, R., Dhami, S.S., Madan, J. (2022). Design Feature Assessment for Fused Deposition Modeling Using Supervised Machine Learning Algorithms. In: Sachdeva, A., Kumar, P., Yadav, O.P., Tyagi, M. (eds) Recent Advances in Operations Management Applications. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-7059-6_20
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