Defect Detection and Quality Prediction in Additive Manufacturing using Supervised Learning

Project: Research

Project Details

Description

The aim of this research project is to develop and evaluate a supervised learning model for detecting defects and predicting part quality in AM. The research objectives are:
- To identify key defects and quality indicators in AM through a literature review and analysis of existing datasets.
- To develop and optimize a supervised learning model for defect detection and quality prediction in AM using selected algorithms such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN).
- To validate the performance of the developed model using various model validation techniques, including k-fold cross-validation and holdout validation.
- To compare the performance of the developed model with existing approaches for defect detection and quality prediction in AM.
The proposed research project will contribute to the development of a more accurate and efficient quality control process for AM. The project will also provide insights into the effectiveness of various supervised learning algorithms for defect detection and quality prediction in AM, which can inform future research in this field.
StatusActive
Effective start/end date1/03/241/03/27

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