INTEGRATED DATA PROCESSING AND MODEL SELECTION IN MACHINE LEARNING FRAMEWORK DEVELOPMENT TO PREDICT DIMENSIONAL ERRORS IN WIRE ARC ADDITIVE MANUFACTURING (WAAM)

Document Type

Conference Proceeding

Publication Date

8-20-2024

Department

Department of Mechanical Engineering-Engineering Mechanics; Department of Manufacturing and Mechanical Engineering Technology

Abstract

In-situ monitoring of Wire Arc Additive Manufacturing (WAAM) is crucial to provide real-time insights into the process for corrective feedback. Machine Learning (ML) approaches have proven to provide reasonable correlations between the input data and the process outputs in WAAM. However, there is a lack of knowledge with the development of an integrated ML development framework to address the simultaneous consideration of data processing and ML model development. This study aims to provide a search method to select ML frameworks with the combination of data processing and ML models with holistic performance metrics. Acoustic, welding current, welding fume, and the plate temperature datasets were collected during each printing operation. Subsequently, the collected datasets were preprocessed and used for feature extraction of the input data based on the intrinsic physical behavior of each time series dataset. The processed and extracted features were used to train six ML regression models to predict the corresponding measured height of the WAAM-printed workpieces. Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and R-Squared metrics were then used to compare the performance of each of the regression models. Therefore, this study efficiently searches among different integrated ML frameworks to result in the combination of the data processing method and ML model with the best performance for the prediction of height in WAAM.

Publication Title

Proceedings of ASME 2024 19th International Manufacturing Science and Engineering Conference, MSEC 2024

ISBN

[9780791888100]

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